System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving user registration information; means for setting a user profile; means for interacting with and training a pet using a virtual reality environment; means for generating a training plan optimized for the user and the pet using generative artificial intelligence; and means for providing feedback to the user in real-time.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] Pet training is a challenge that requires a high level of experience and knowledge for novice pet owners and trainers. In particular, new owners often lack a full understanding of appropriate pet training methods, which can lead to poor training practices and behavioral problems. Furthermore, it is difficult to monitor training progress and the pet's reactions in real time, and the lack of feedback makes it difficult to make appropriate corrections. Therefore, a system is needed that allows novice pet owners and trainers to train their pets with confidence and deepen their relationships with their pets. [Means for solving the problem]

[0005] The present invention provides a system that allows users to register for a service and set up an individual profile, and then provides an optimal training plan tailored to their characteristics. The system is configured to allow users to interact with and train virtual pets using a virtual reality environment. Specifically, the system includes the following means:

[0006] A means of accepting user registration information

[0007] A way to set up a user profile

[0008] Means for interacting with and training pets using a virtual reality environment

[0009] A means for generating training plans optimized for users and pets using generative artificial intelligence

[0010] A way to provide real-time feedback to users

[0011] Furthermore, after the training session, the system provides feedback based on the analytical data from the generative AI and provides a means for users to receive support from experts, helping novice pet owners and trainers acquire practical skills and gain confidence in training their pets.

[0012] ---

[0013] "Users" refer to pet owners and trainers who use this system.

[0014] "Service" refers to all functions and support provided by the System, including the pet training experience.

[0015] "User registration information" refers to personal information and authentication information provided by a user to use the service.

[0016] "User Profile" refers to information containing information, characteristics, and training needs of a user and their pet.

[0017] "Virtual reality environment" refers to a simulation environment created using VR technology in which users can interact with virtual pets.

[0018] "Virtual pet" refers to a simulated model of a pet that is programmed to appear within a virtual reality environment and interact with a user.

[0019] "Generative AI" refers to AI technology that analyzes information about the user and their pet and generates an optimal training plan.

[0020] "Training Plan" refers to a training plan or scenario generated by the generative artificial intelligence that is optimized for the user and their pet.

[0021] "Feedback" refers to evaluation information, including suggestions for improvement, provided during or after a training session.

[0022] "Communication means" refers to communication methods such as chat and messaging functions that allow users to receive support from experts.

[0023] An "expert" is someone who has knowledge and experience in pet training and is responsible for providing advice and support to users. [Brief explanation of the drawings]

[0024] [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

[0025] 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.

[0026] First, the terms used in the following description will be explained.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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."

[0032] [First embodiment]

[0033] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0034] 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.

[0035] 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).

[0036] 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.

[0037] 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.

[0038] 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.

[0039] 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.

[0040] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0041] 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.

[0042] 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.

[0043] 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.

[0044] 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."

[0045] The present invention provides a pet training experience system that uses a virtual reality environment and generative artificial intelligence to enable users to effectively train their pets. Specific embodiments of this system are described below.

[0046] System Overview

[0047] After registering and setting up a profile, the system allows users to train with a virtual pet in a virtual reality (VR) environment, where generative artificial intelligence (AI) provides real-time feedback to improve the user's training skills. After completing the training, users can receive detailed feedback and expert support.

[0048] Program processing

[0049] 1. User Registration and Login

[0050] When a user opens the service's website or app, the device displays a new registration screen. The user enters the required information, such as name, email address, and password, and presses the registration button.

[0051] The server receives the entered information and saves it in the user database. After saving, the server sends the user a registration completion email.

[0052] The user activates their account by clicking the link in the email and enters their credentials on the login screen. The server verifies the credentials and, if the login is successful, instructs the device to display the dashboard.

[0053] 2. Setting up your user profile

[0054] When a user clicks on the "Profile Settings" tab on the dashboard, the device displays a form where they can enter their pet's type, age, name, and characteristics.

[0055] The user enters the pet's information and presses the save button, and the server saves this information to the database, completing the profile setup.

[0056] 3. Start your training session

[0057] The user clicks the "Start a new training session" button on the dashboard. The server retrieves the user's profile data and sends it to the generation AI module.

[0058] The AI ​​generates the optimal training scenario based on the characteristics of the user and their pet. The server sends the generated training scenario to the device and displays it to the user.

[0059] 4. Setting up the VR environment

[0060] The user puts on the VR headset, launches the VR application on the device, loads the training scenario received from the server, and presses the start session button when the user is ready.

[0061] 5. Training execution

[0062] The user interacts with the virtual pet in VR and gives instructions. For example, if the user gives the instruction "sit," the device recognizes the voice command and sends the instruction to the virtual pet.

[0063] The virtual pet reacts according to the algorithms of the generative AI, which determines whether to follow the user's commands, and provides real-time feedback based on the user's training methods and the pet's responses.

[0064] The server monitors the training progress and updates the data as needed.

[0065] 6. Feedback at the end of training

[0066] When the user presses the end training session button, the terminal notifies the server that the session is over.

[0067] The server retrieves the analytical data from the generated AI and provides detailed feedback to the user, including suggestions for improvement and the next training session.

[0068] 7. Expert Support

[0069] When a user clicks on the "Support" tab in the dashboard, the device displays a chat window where the user can type in their question and the server will forward it to an expert.

[0070] The expert enters the answer to the question, and the server sends the answer to the user, who can then check the answer and use it for the next training session if necessary.

[0071] Specific examples

[0072] For example, consider the case of a novice pet owner, Mr. A, training a new puppy.

[0073] 1. Person A registers with the system and logs in. On the profile settings screen, he enters the breed, age, and name of the puppy.

[0074] 2. Person A clicks the "Start a new training session" button on the dashboard. The server sends the puppy's profile information to the generation AI.

[0075] 3. The generation AI generates a training scenario optimized for Mr. A's puppy, and the server sends it to the device.

[0076] 4. Person A puts on the VR headset and starts the session. He commands the virtual pet, such as "sit," and the generating AI provides real-time feedback.

[0077] 5. After the training is completed, feedback based on the analysis data from the generative AI is received and used for the next training.

[0078] 6. If you have further questions, you can get help from experts.

[0079] Through the above process, the system helps novice pet owners and trainers to effectively train and deepen their relationships with their pets.

[0080] The processing flow will be explained below.

[0081] ---

[0082] User Registration and Login

[0083] Step 1:

[0084] The user opens the service's website or app, and the device displays the new registration screen.

[0085] Step 2:

[0086] The user enters the required information such as name, email address, and password, and presses the registration button.

[0087] Step 3:

[0088] The server receives the entered information and stores it in a user database.

[0089] Step 4:

[0090] After the server saves the data, it generates a registration completion email and sends it to the user.

[0091] Step 5:

[0092] The user clicks the link in the email they received to activate their account and enters their email address and password on the login screen.

[0093] Step 6:

[0094] The server checks the entered authentication information and, if authentication is successful, instructs the device to display the dashboard.

[0095] User Profile Settings

[0096] Step 1:

[0097] The user clicks the "Profile Settings" tab on the dashboard. The device displays a form to enter the pet's type, age, name, and characteristics.

[0098] Step 2:

[0099] The user enters the pet's information and presses the save button.

[0100] Step 3:

[0101] The server stores the information you entered in a database and completes your profile setup.

[0102] Starting a training session

[0103] Step 1:

[0104] A user clicks the "Start a New Training Session" button on the dashboard.

[0105] Step 2:

[0106] The server retrieves the user's profile data and sends it to the generation AI module.

[0107] Step 3:

[0108] Generative AI generates optimal training scenarios based on the characteristics of the user and their pet.

[0109] Step 4:

[0110] The server transmits the generated training scenario to the terminal and displays it to the user.

[0111] Setting up the VR environment

[0112] Step 1:

[0113] The user puts on the VR headset.

[0114] Step 2:

[0115] The device launches the VR application and loads the training scenario received from the server.

[0116] Step 3:

[0117] When the user is ready, he presses the Start Session button.

[0118] Training run

[0119] Step 1:

[0120] The user interacts with the virtual pet in VR and gives commands (e.g., "sit").

[0121] Step 2:

[0122] The terminal recognizes the user's voice commands and transmits the instructions to the virtual pet.

[0123] Step 3:

[0124] The virtual pet reacts according to the algorithm of the generating AI and decides whether to follow the user's instructions.

[0125] Step 4:

[0126] Generative AI provides real-time feedback based on your training methods and your pet's reactions.

[0127] Step 5:

[0128] The server monitors the training progress and updates the data as needed.

[0129] Feedback at the end of training

[0130] Step 1:

[0131] The user presses the end training session button.

[0132] Step 2:

[0133] The terminal notifies the server of the end of the session.

[0134] Step 3:

[0135] The server obtains analytical data from the generated AI and provides detailed feedback to the user.

[0136] Step 4:

[0137] Feedback includes suggestions for improvement and next training session.

[0138] Expert support

[0139] Step 1:

[0140] User clicks on the Support tab on the dashboard.

[0141] Step 2:

[0142] The terminal displays a chat window and the user types in a question.

[0143] Step 3:

[0144] The server forwards the question to an expert.

[0145] Step 4:

[0146] The expert enters the answer to the question and the server sends the answer to the user.

[0147] Step 5:

[0148] Users can review their answers and use them in future training sessions if necessary.

[0149] ---

[0150] The above is a description of the system program processing divided into specific steps.

[0151] Example 1

[0152] 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."

[0153] Currently, many pet owners struggle to find effective pet training methods. Learning the correct training methods can be particularly difficult for novice pet owners, potentially hindering efforts to improve their pet's behavior and build a relationship with them. Furthermore, actual training requires time and effort, and it is difficult to obtain immediate feedback, making it difficult to maximize the effectiveness of training. Furthermore, with little access to expert support, owners are unable to obtain appropriate advice for problem-solving. To address these challenges, a system is needed that allows users to effectively train their pets while learning on their own.

[0154] 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.

[0155] In this invention, the server includes means for accepting user registration information, means for setting a user profile, means for interacting with and training an animal using a virtual reality environment, means for generating a training plan optimized for the user and the animal using generative artificial intelligence, means for providing feedback to the user in real time, means for generating a training scenario by inputting prompts into the generative AI model, means for recognizing user instructions using voice recognition technology and transmitting them to the virtual animal, and means for receiving support from experts. This allows users to effectively train their pets while learning on their own. Furthermore, the ability to receive real-time feedback and expert support improves training effectiveness and deepens the relationship with their pet.

[0156] "User registration information" refers to basic information such as name, email address, and password provided by the user in order to use the service.

[0157] A "user profile" is a data set containing detailed information about a user and their pets, such as the pet's type, age, name, characteristics, etc.

[0158] A "virtual reality environment" is a three-dimensional space generated using computer technology, in which users experience and operate in a virtual space that is different from the real world.

[0159] "Animals" are living creatures such as dogs and cats kept as pets.

[0160] "Generative artificial intelligence" refers to algorithms that have the ability to autonomously learn and perform specific tasks based on input data.

[0161] A "training plan" is a plan that shows the specific training procedures and methods to be used on the user's pet, and is optimized by the generation AI.

[0162] "Feedback" refers to the advice and evaluation provided by the generative AI in real time during training.

[0163] A "generative AI model" is an algorithm that learns from large amounts of data and generates appropriate outputs for new inputs.

[0164] A "prompt sentence" is a sentence that is input to give instructions or ask a question to a generative AI model.

[0165] "Speech recognition technology" is a technology that analyzes the words spoken by a user and converts them into text data.

[0166] "Communication means" refers to electronic communication means used by users to receive support from experts, including web chat and email.

[0167] The present invention is a system that enables users to effectively train their pets. This system provides a consistent service, from accepting users' registration information, to training with their pets, and providing feedback and expert support after training is complete. An embodiment of the present invention will be described in detail below.

[0168] Hardware and Software Configuration

[0169] This system operates by combining multiple hardware and software components. The main hardware components include the user access terminals (e.g., PCs and smartphones), the VR headset (e.g., Oculus Quest 2), and the server. The main software components include the web application, the VR application, and the algorithms that run the generative AI model.

[0170] Processing flow

[0171] When a user opens the service's website or app, the device displays a registration screen. The user enters the required information, such as name, email address, and password, and presses the Register button. The server receives the information and stores it in a secure format in the user database. The server then sends a registration completion email, and the user clicks the link in the email to activate their account. If the user enters authentication information on the login screen, the server verifies the authentication information, and if the login is successful, the device displays a dashboard.

[0172] When a user clicks on the "Profile Settings" tab on the dashboard, the device displays a form for entering the pet's type, age, name, and characteristics. Once the user enters the information and presses the save button, the server saves the information to a database and completes the profile settings.

[0173] Next, when the user clicks the "Start a new training session" button on the dashboard, the server sends the user's profile data to the generation AI module. The generation AI generates an optimal training scenario based on the characteristics of the user and their pet, and the scenario is sent from the server to the device. The user then puts on a VR headset and launches a dedicated VR application. The training scenario received from the server is loaded there, and the user presses the start session button when ready.

[0174] During a training session, the user interacts with the virtual pet in VR, issuing voice commands (e.g., "sit"). The device uses voice recognition technology to interpret the commands and transmit them to the virtual pet. Generative AI controls the virtual pet's movements and provides real-time feedback to the user. At the same time, the server monitors the training progress and updates the data as needed.

[0175] After a training session, the user presses the end button, and the device notifies the server that the session has ended. The server then retrieves analytical data from the generated AI and provides detailed feedback to the user, including specific advice on areas for improvement and the next training session. Furthermore, when the user clicks the "Support" tab on the dashboard, the device displays a chat window where the user can enter a question. The question is then forwarded to an expert via the server, who then provides the user with an answer.

[0176] Specific examples

[0177] For example, when a novice pet owner trains a new puppy, the following steps are taken: First, the user registers and logs in. Next, they enter the puppy's information on the profile settings screen. When the user clicks the "Start New Training Session" button, the server sends the puppy's profile information to the generation AI, which then generates an optimal training scenario. This scenario is sent to the device, and the user puts on a VR headset to start the session. During the training session, the user issues voice commands to the virtual pet and receives real-time feedback based on its responses. After the session ends, the user receives detailed feedback based on analytical data, which can be used for the next training session. The user can also ask experts any questions and receive appropriate advice.

[0178] In a specific example, when a user inputs a prompt sentence into a generative AI model to generate a training scenario, an example of the prompt sentence is, "Please create a basic obedience training scenario for a 5-year-old Labrador Retriever."

[0179] This invention allows users to effectively train their pets and deepen their relationships with them. Real-time feedback and expert support improve training effectiveness and are expected to improve pet behavior.

[0180] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0181] Step 1:

[0182] The user opens the service's website or app.

[0183] Input: A user action to open a website or app.

[0184] Output: The terminal displays the new registration screen.

[0185] What happens: The device's browser loads a web page and displays a sign-up form on the screen, where the user enters information such as their name, email address, and password.

[0186] Step 2:

[0187] The user enters the required information and presses the registration button.

[0188] Input: The user enters their name, email address, password, etc. and clicks the Register button.

[0189] Output: Information is sent to the server.

[0190] What it does: The device collects the information entered and sends it to the server using the HTTPS protocol. The server receives it, encrypts it, and stores it in a database.

[0191] Step 3:

[0192] The server sends a registration completion email.

[0193] Input: User's email address and registration information.

[0194] Output: A registration completion email will be sent to the user's email address.

[0195] Specific operation: The server uses the email sending API to send a registration confirmation email to the email address specified by the user, which includes a link to activate the account.

[0196] Step 4:

[0197] The user clicks the link in the email to activate their account.

[0198] Input: A user clicks on a link in an email.

[0199] Output: The account is activated and the user is redirected to the login screen.

[0200] What happens: When the user clicks the link, the server updates the account status to be activated, and then the user is taken to the login screen.

[0201] Step 5:

[0202] The user enters their credentials on the login screen.

[0203] Input: The user enters their email address and password and clicks the login button.

[0204] Output: The server verifies the credentials and if the login is successful, displays the dashboard.

[0205] Specific operation: The device sends the entered authentication information to the server, which checks it against the database. If it matches, authentication is successful and the user's dashboard screen is displayed.

[0206] Step 6:

[0207] The user clicks the Profile Settings tab on the dashboard.

[0208] Input: User clicks on the "Profile Settings" tab.

[0209] Output: The device displays the profile setup form.

[0210] What happens: The device loads the form associated with the "Profile Settings" tab and displays it on the screen.

[0211] Step 7:

[0212] The user enters the pet's type, age, name, and characteristics and presses the save button.

[0213] Input: The user enters pet information and presses the save button.

[0214] Output: The information is sent to the server and stored in a database.

[0215] Specific operation: The terminal sends the entered pet information to the server as an HTTP request, and the server receives the information and stores it in a database.

[0216] Step 8:

[0217] User clicks the "Start New Training Session" button.

[0218] Input: User clicks the "Start New Training Session" button.

[0219] Output: The server sends the user profile data to the generation AI module.

[0220] Specific operation: The server collects the user's profile data and sends it to the generation AI module, which then inputs a prompt to generate the optimal training scenario.

[0221] Step 9:

[0222] Generative AI generates optimal training scenarios.

[0223] Input: Profile data based on user and pet characteristics and a prompt.

[0224] Output: The generated training scenario.

[0225] How it works: The generation AI analyzes the profile data and prompts, and uses the learning model to generate optimal training scenarios. The generated scenario data is then sent to the server.

[0226] Step 10:

[0227] The server transmits the generated training scenario to the terminal.

[0228] Input: Training scenario data sent from the generative AI.

[0229] Output: The terminal displays the training scenario to the user.

[0230] Specific operation: The server sends training scenario data to the terminal, which then renders it into a format that is displayed to the user.

[0231] Step 11:

[0232] The user puts on the VR headset and starts the session.

[0233] Input: The user puts on a VR headset and launches an application.

[0234] Output: A training session begins in the VR environment.

[0235] Specific operation: The user puts on the VR headset and launches the dedicated application. The application loads the training scenario received from the server.

[0236] Step 12:

[0237] The user issues voice commands to the virtual pet.

[0238] Input: The user's voice command (e.g., "sit").

[0239] Output: The virtual pet responds to voice commands.

[0240] Specific operation: The device uses voice recognition technology to analyze the user's voice commands and transmit them to the virtual pet. The generated AI controls the pet's movements based on those instructions.

[0241] Step 13:

[0242] Generative AI provides real-time feedback.

[0243] Input: User behavior and pet reaction data.

[0244] Output: Real-time feedback to the user.

[0245] Specific operation: The generative AI analyzes the collected data and provides appropriate feedback to the user via screen or voice.

[0246] Step 14:

[0247] The server monitors the training progress and updates the data as needed.

[0248] Input: The data being trained.

[0249] Output: Updated progress data.

[0250] What it does: The server monitors training session data in real time and updates the information in the database as needed.

[0251] Step 15:

[0252] The user presses the end training session button.

[0253] Input: The user presses the end session button.

[0254] Output: A session termination notification is sent to the server.

[0255] Specific operation: The terminal sends a session termination request to the server, and the server terminates the session.

[0256] Step 16:

[0257] The server obtains analytical data from the generated AI and provides detailed feedback to the user.

[0258] Input: Analysis data from the generative AI.

[0259] Output: Detailed feedback to the user.

[0260] Specific behavior: The server obtains analytical data from the generated AI and uses it to provide detailed feedback to the user, including specific advice on areas for improvement and the next training session.

[0261] Step 17:

[0262] User clicks on the Support tab on the dashboard.

[0263] Input: User clicks on the Support tab.

[0264] Output: The terminal displays a chat window.

[0265] Specific behavior: The device displays a support chat window, allowing the user to enter questions.

[0266] Step 18:

[0267] The user enters a question and the server forwards it to an expert.

[0268] Input: The user's question.

[0269] Output: The question is forwarded to an expert.

[0270] Specific operation: The device receives the user's question and sends it to the server, which then forwards the question to an expert.

[0271] Step 19:

[0272] The expert enters the answer and the server sends the answer to the user.

[0273] Input: Expert answers.

[0274] Output: The answer to the user.

[0275] Specific operation: The expert enters the answer, and the server sends the answer to the user's device. The user can check the answer and incorporate it into the next training if necessary.

[0276] (Application example 1)

[0277] 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."

[0278] Modern factories require new workers to quickly and effectively master advanced robot operation and maintenance. However, operating actual robots is expensive and poses a high risk of operational errors, making it difficult to provide adequate training using traditional educational methods. Furthermore, there are limited means for workers to learn while receiving real-time feedback. To address this challenge, an efficient training system using virtual reality environments and generative artificial intelligence is needed.

[0279] 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.

[0280] In this invention, the server includes means for accepting user registration information, means for setting a user profile, means for interacting with and educating a training subject using a virtual reality environment, means for generating an instruction plan optimized for the user and the training subject using generative artificial intelligence, means for providing feedback to the user in real time, and means for executing a specific training scenario using a virtual reality device, thereby enabling a user to safely and effectively acquire robot operation and maintenance skills in the virtual reality environment.

[0281] "Means for accepting user registration information" refers to a mechanism for inputting and saving basic user information (such as name, email address, and password).

[0282] The "means for setting a user profile" is a mechanism by which a user sets and saves details of the training subject (robot model, experience level, etc.).

[0283] "Means for interacting with and educating training subjects using a virtual reality environment" refers to a system in which a user interacts with a training subject (robot) in a virtual environment using a VR device, and receives training in operation and maintenance.

[0284] "Means for generating an educational plan optimized for the user and training subject using generative artificial intelligence" refers to AI technology for generating optimal training scenarios based on profile information of the user and training subject.

[0285] "Means of providing feedback to users in real time" refers to a system in which AI provides on-the-spot improvements and advice based on the user's operations and instructions during training.

[0286] "Means for executing a specific training scenario using a virtual reality device" refers to a mechanism for executing a generated training scenario in a virtual environment using a VR headset or related device.

[0287] MODE FOR CARRYING OUT THE INVENTION

[0288] System Overview

[0289] This invention is an educational system that enables factory workers to effectively learn robot operation and maintenance in a virtual reality (VR) environment. It includes user registration, profile setup, training in the VR environment, real-time feedback provided by generative artificial intelligence (AI), feedback after training, and expert support.

[0290] System Configuration

[0291] The system uses the following hardware and software:

[0292] Hardware: VR headset (e.g. Oculus Quest 2), computer, network connection equipment

[0293] Software: VR framework, generative AI module, server application (e.g., Flask, Django)

[0294] 1. User Registration and Login

[0295] The server first provides a means to accept user registration information. The user registers with the system and enters basic information (name, email address, password, etc.). The entered information is saved on the server, and the user is sent a registration completion email. The user then clicks the link in the email to activate their account and enters their login information to access the system.

[0296] 2. Setting up your user profile

[0297] After registration, the user uses the profile setting means to enter details of the robot they wish to train (robot model, experience level, etc.) The server receives this information and stores it in a database.

[0298] 3. Start your training session

[0299] When a user clicks the "Start a new training session" button on the dashboard, the server retrieves the user's profile data and sends it to the Generative AI module, which then generates an optimal training plan for the user and training subject and sends it to the user via the server.

[0300] 4. Setting up the VR environment

[0301] The user puts on a VR headset and executes a specific training scenario, and the server uses the VR framework to load the scenario. Once the user is ready, the training session begins.

[0302] 5. Training execution and real-time feedback

[0303] In the virtual reality environment, the user issues instructions to the robot, and the generative AI provides real-time feedback based on these instructions. For example, if the user issues an instruction such as "set welding points," the AI ​​will provide the execution results and appropriate feedback in real time.

[0304] 6. Post-training feedback

[0305] When the user presses the end button for the training session, the server records the end of the session and provides detailed feedback based on the analysis data from the generative AI, including an evaluation of the user's actions and suggestions for improvement next time.

[0306] 7. Expert Support

[0307] If the user needs additional support, the server provides a means of communication to receive support from experts. The user can enter a question, and the expert will provide an answer, which the user can use to help with their next training.

[0308] Examples of concrete examples and prompts

[0309] As a concrete example, consider a scenario in which new factory worker B is learning to operate a new welding robot. After completing registration and profile setup, B issues the command "set welding point" in the VR environment. The generating AI provides real-time feedback based on this command. After completing the training, B receives detailed feedback from the generating AI, which can be used to improve the next training session.

[0310] Examples of prompts include:

[0311] "Create a new training scenario. The target robot is 'Weld-2000' and the operation skill level is 'Beginner'. Create a scenario that includes the following elements: 1. Safety check 2. Basic operation 3. Weld point setting 4. Real-time feedback"

[0312] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0313] Step 1:

[0314] User Registration and Login

[0315] The server receives the user's name, email address, and password and stores them in a database. It processes the information entered by the user and automatically generates and sends a confirmation email. If the user clicks on the link in the email to activate their account, the server verifies the authentication information and allows them to log in.

[0316] Input: Name, Email Address, Password

[0317] Output: Sending a registration completion email, saving authentication information

[0318] What happens: A user enters their name, email address, and password into a web form and presses the submit button. The server receives this information, stores it in a database, and sends a confirmation email to the user.

[0319] Step 2:

[0320] User Profile Settings

[0321] The server receives information such as the robot model and experience level input by the user through the user profile setting means, and stores this profile information in a database.

[0322] Input: Robot model, experience level

[0323] Output: Save profile information

[0324] Specific operation: The user selects the robot model "Weld-2000" and the experience level "Beginner" on the dashboard and presses the save button. The server receives this and saves it in the database.

[0325] Step 3:

[0326] Starting a training session

[0327] When a user clicks the "Start New Training Session" button, the server sends the user's profile data to the Generative AI module, which then generates an optimal training scenario and sends it to the user.

[0328] Input: User profile data

[0329] Output: Optimal training scenario

[0330] Specific operation: The server obtains the user's profile information and sends it to the generation AI. The generation AI generates a "Weld-2000 training scenario for beginners," and the server sends it to the user's device.

[0331] Step 4:

[0332] Setting up the VR environment

[0333] The user puts on the VR headset, the server loads the training scenario through the VR framework, and when the user is ready, they press the "Start Session" button.

[0334] Input: Training scenario

[0335] Output: Loaded VR training environment

[0336] Specific operation: The user starts the VR headset, the server provides guidance to load the received training scenario into the VR framework, and the user presses the start session button.

[0337] Step 5:

[0338] Training execution and real-time feedback

[0339] When a user issues an instruction to the robot in VR, the AI ​​analyzes it and provides real-time feedback. For example, it determines whether an instruction such as "set welding points" is accurate and provides on-the-spot advice.

[0340] Input: User instructions

[0341] Output: Real-time feedback

[0342] Specific operation: The user gives instructions in VR such as "Set welding points," and the generated AI analyzes them. For example, if the welding points are appropriate, it will return feedback such as "This is the correct position."

[0343] Step 6:

[0344] Post-training feedback

[0345] When the user presses the end button for the training session, the server records the end of the session and provides detailed feedback based on the analysis data from the generated AI, including an overall evaluation and suggestions for improvement next time.

[0346] Input: Training session data

[0347] Output: Detailed feedback

[0348] How it works: When a user presses the end session button, the server collects session data, and the AI ​​analyzes it to create detailed feedback. Improvements and next steps are displayed on the user's device.

[0349] Step 7:

[0350] Expert support

[0351] If the user needs further assistance, the server provides a means of communication to forward the user's question to an expert, and receives the expert's answer and sends it to the user.

[0352] Input: User question

[0353] Output: Expert Answers

[0354] How it works: A user clicks on the "Support" tab on the dashboard and enters a question. The server forwards it to an expert, who then enters an answer and sends it back to the server. Finally, the answer is displayed to the user.

[0355] 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.

[0356] The present invention is a system that combines a virtual reality environment and a pet training experience system that uses generative artificial intelligence to enable users to effectively train their pets with an emotion engine that recognizes the user's emotions. Specific embodiments of this system are described below.

[0357] System Overview

[0358] After users register and set up their profile, the system allows them to train with a virtual pet in a virtual reality (VR) environment. Generative artificial intelligence (AI) provides real-time feedback to improve the user's training skills. Furthermore, it uses an emotion engine that recognizes the user's emotions in real time and dynamically adjusts the feedback and training scenarios. After completing training, users can receive detailed feedback and expert support.

[0359] Program processing

[0360] 1. User Registration and Login

[0361] When a user opens the service's website or app, the device displays a new registration screen. The user enters the required information, such as name, email address, and password, and presses the registration button.

[0362] The server receives the entered information and saves it in the user database. After saving, the server sends the user a registration completion email.

[0363] The user activates their account by clicking the link in the email and enters their credentials on the login screen. The server verifies the credentials and, if the login is successful, instructs the device to display the dashboard.

[0364] 2. Setting up your user profile

[0365] When a user clicks on the "Profile Settings" tab on the dashboard, the device displays a form where they can enter their pet's type, age, name, and characteristics.

[0366] The user enters the pet's information and presses the save button, and the server saves this information to the database, completing the profile setup.

[0367] 3. Start your training session

[0368] The user clicks the "Start a new training session" button on the dashboard. The server retrieves the user's profile data and sends it to the generation AI module.

[0369] The AI ​​generates the optimal training scenario based on the characteristics of the user and their pet. The server sends the generated scenario to the device and displays it to the user.

[0370] 4. Setting up the VR environment

[0371] The user puts on the VR headset, launches the VR application on the device, loads the training scenario received from the server, and presses the start session button when the user is ready.

[0372] 5. Training execution

[0373] The user interacts with the virtual pet in VR and gives instructions. For example, if the user commands "sit," the device recognizes the voice command and sends the instruction to the virtual pet.

[0374] The virtual pet reacts according to the algorithms of the generative AI, which determines whether to follow the user's commands, and provides real-time feedback based on the user's training methods and the pet's responses.

[0375] The emotion engine recognizes the user's emotions and feeds that data back to the generative AI, which then adjusts the feedback and advice it provides to take the user's emotions into account.

[0376] The emotion engine analyzes the emotional data and dynamically changes the training scenario according to the user's stress and satisfaction.

[0377] 6. Feedback at the end of training

[0378] When the user presses the end training session button, the terminal notifies the server that the session is over.

[0379] The server retrieves analytical data from the generative AI and emotion engine and provides detailed feedback to the user, including suggestions for improvement and the next training session.

[0380] 7. Expert Support

[0381] When a user clicks on the "Support" tab in the dashboard, the device displays a chat window where the user can type in their question and the server will forward it to an expert.

[0382] The expert enters the answer to the question, and the server sends the answer to the user, who can then check the answer and use it for the next training session if necessary.

[0383] Specific examples

[0384] For example, consider the case of a novice pet owner, Mr. A, training a new puppy.

[0385] 1. Person A registers with the system and logs in. On the profile settings screen, he enters the breed, age, and name of the puppy.

[0386] 2. Person A clicks the "Start a new training session" button on the dashboard. The server sends the puppy's profile information to the generation AI.

[0387] 3. The generation AI generates a training scenario optimized for Mr. A's puppy, and the server sends it to the device.

[0388] 4. Person A puts on the VR headset and begins the session. He commands the virtual pet, such as "sit," and the generative AI provides real-time feedback. The emotion engine recognizes Person A's emotions and adjusts the feedback accordingly.

[0389] 5. After the training is complete, feedback based on the analysis data from the generative AI and emotion engine is received and used for the next training.

[0390] 6. If you have further questions, get help from a professional.

[0391] Through this process, the system can help beginner pet owners and trainers effectively train their pets and deepen their relationships with them. It also recognizes the user's emotions and provides appropriate feedback, improving user satisfaction and training effectiveness.

[0392] The processing flow will be explained below.

[0393] ---

[0394] User Registration and Login

[0395] Step 1:

[0396] The user opens the service's website or app, and the device displays the new registration screen.

[0397] Step 2:

[0398] The user enters the required information such as name, email address, and password, and presses the registration button.

[0399] Step 3:

[0400] The server receives the entered information and stores it in a user database.

[0401] Step 4:

[0402] After the server saves the data, it generates a registration completion email and sends it to the user.

[0403] Step 5:

[0404] The user clicks the link in the email they received to activate their account and enters their email address and password on the login screen.

[0405] Step 6:

[0406] The server checks the entered authentication information and, if authentication is successful, instructs the device to display the dashboard.

[0407] User Profile Settings

[0408] Step 1:

[0409] The user clicks the "Profile Settings" tab on the dashboard. The device displays a form to enter the pet's type, age, name, and characteristics.

[0410] Step 2:

[0411] The user enters the pet's information and presses the save button.

[0412] Step 3:

[0413] The server stores the information you entered in a database and completes your profile setup.

[0414] Starting a training session

[0415] Step 1:

[0416] A user clicks the "Start a New Training Session" button on the dashboard.

[0417] Step 2:

[0418] The server retrieves the user's profile data and sends it to the generation AI module.

[0419] Step 3:

[0420] Generative AI generates optimal training scenarios based on the characteristics of the user and their pet.

[0421] Step 4:

[0422] The server transmits the generated training scenario to the terminal and displays it to the user.

[0423] Setting up the VR environment

[0424] Step 1:

[0425] The user puts on the VR headset.

[0426] Step 2:

[0427] The device launches the VR application and loads the training scenario received from the server.

[0428] Step 3:

[0429] When the user is ready, he presses the Start Session button.

[0430] Training run

[0431] Step 1:

[0432] The user interacts with the virtual pet in VR and gives commands (e.g., "sit").

[0433] Step 2:

[0434] The terminal recognizes the user's voice commands and transmits the instructions to the virtual pet.

[0435] Step 3:

[0436] The virtual pet reacts according to the algorithm of the generating AI and decides whether to follow the user's instructions.

[0437] Step 4:

[0438] Generative AI provides real-time feedback based on your training methods and your pet's reactions.

[0439] Step 5:

[0440] The emotion engine recognizes the user's emotions and feeds that data back to the generative AI, which then adjusts the feedback and advice it provides to take the user's emotions into account.

[0441] Step 6:

[0442] The emotion engine analyzes the emotional data and dynamically changes the training scenario according to the user's stress and satisfaction.

[0443] Feedback at the end of training

[0444] Step 1:

[0445] The user presses the end training session button.

[0446] Step 2:

[0447] The terminal notifies the server of the end of the session.

[0448] Step 3:

[0449] The server obtains analytical data from the generative AI and emotion engine and provides detailed feedback to the user.

[0450] Step 4:

[0451] Feedback includes suggestions for improvement and next training session.

[0452] Expert support

[0453] Step 1:

[0454] User clicks on the Support tab on the dashboard.

[0455] Step 2:

[0456] The terminal displays a chat window and the user types in a question.

[0457] Step 3:

[0458] The server forwards the question to an expert.

[0459] Step 4:

[0460] The expert enters the answer to the question and the server sends the answer to the user.

[0461] Step 5:

[0462] Users can review their answers and use them in future training sessions if necessary.

[0463] ---

[0464] The above is a description of the system program processing divided into specific steps.

[0465] Example 2

[0466] 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."

[0467] Conventional pet training systems require users to have specialized knowledge and experience to effectively train their pets, making them difficult for beginners to use. Furthermore, the effectiveness of training is limited because the system does not provide feedback that takes into account the user's emotions. Furthermore, only a limited number of systems offer support from experts.

[0468] 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.

[0469] In this invention, the server includes means for accepting user registration information, means for setting a user profile, means for interacting with and training a pet using a virtual reality environment, means for generating a training plan optimized for the user and the pet using generative artificial intelligence, means for recognizing voice commands and conveying instructions to the virtual pet, means for recognizing the user's emotions in real time using an emotion engine and dynamically adjusting feedback and training scenarios, and means for providing feedback to the user in real time. This allows even beginners to train effectively and provides feedback that takes the user's emotions into consideration. Furthermore, the quality of training is improved by easily receiving support from experts.

[0470] "Means for accepting user registration information" refers to the function by which a user inputs their own information into the system and the system records that information.

[0471] "Means for setting up a user profile" is a function that allows users to input detailed information about themselves and their pets, and the system then stores and manages that information.

[0472] "Means for interacting with and training a pet using a virtual reality environment" refers to a function that uses virtual reality technology to allow a user to interact with and train a virtual pet.

[0473] "Means for generating training plans optimized for users and their pets using generative artificial intelligence" refers to a function that utilizes artificial intelligence technology to automatically create training plans based on the characteristics of users and their pets.

[0474] The "means for recognizing voice commands and transmitting instructions to a virtual pet" is a function for recognizing the user's voice and transmitting those instructions to a virtual pet.

[0475] "Means of recognizing the user's emotions in real time using an emotion engine and dynamically adjusting feedback and training scenarios" is a function that analyzes the user's facial expressions and voice to determine their emotions and adapts feedback and training content based on that.

[0476] "Means for providing feedback to the user in real time" refers to a function that provides immediate evaluation and advice on the operations and reactions performed by the user during training.

[0477] The present invention is a system that combines a virtual reality environment and a pet training experience system that uses generative artificial intelligence to enable users to effectively train their pets with an emotion engine that recognizes the user's emotions. Specific embodiments of this system are described below.

[0478] User Registration and Login

[0479] When a user opens the service's website or app, the device displays a new registration screen. The user enters the required information, such as name, email address, and password, and presses the registration button. The server receives the entered information and saves it in the user database. After saving, the server sends the user a registration completion email. The user clicks the link in the received email to activate their account and enters their authentication information on the login screen. The server verifies the authentication information, and if login is successful, it instructs the device to display the dashboard.

[0480] User Profile Settings

[0481] When a user clicks on the "Profile Settings" tab on the dashboard, the terminal displays a form to input the pet's type, age, name, and characteristics. The user enters the pet's information and presses the save button. The server saves this information in the database and completes the profile settings.

[0482] Starting a training session

[0483] The user clicks the "Start a new training session" button on the dashboard. The server retrieves the user's profile data and sends it to the generation AI module. The generation AI generates an optimal training scenario based on the characteristics of the user and their pet. The server sends the generated scenario to the device and displays it to the user.

[0484] Setting up the VR environment

[0485] The user puts on the VR headset, the device launches the VR application, loads the training scenario received from the server, and when the user is ready, presses the start session button.

[0486] Training run

[0487] The user interacts with the virtual pet in VR and gives instructions. For example, if the user commands "sit," the device recognizes the voice command and sends the instruction to the virtual pet. The virtual pet responds according to the algorithm of the generation AI and determines whether to follow the user's instructions. The emotion engine recognizes the user's emotions and feeds that data back to the generation AI. The generation AI dynamically adjusts its feedback and advice taking the user's emotions into account.

[0488] Feedback at the end of training

[0489] When the user presses the end button on the device, the device notifies the server that the session is over. The server then receives analytical data from the generative AI and emotion engine and provides detailed feedback to the user, including suggestions for improvement and advice for the next training session.

[0490] Expert support

[0491] When a user clicks the "Support" tab on the dashboard, the device displays a chat window. The user enters a question, which the server forwards to an expert. The expert enters an answer to the question, and the server sends the answer to the user. The user reviews the answer and, if necessary, uses it for future training.

[0492] Hardware and software used

[0493] Hardware: PC, smartphone, VR headset, audio input device

[0494] Software: Web browsers, server software (e.g., Apache, NGINX), databases (e.g., MySQL, PostgreSQL), generative AI modules, emotion engines, VR applications, chat applications

[0495] Specific examples

[0496] For example, consider a novice pet owner training a new puppy. The user registers and logs in to the system. They enter the puppy's breed, age, and name on the profile settings screen. When the user clicks the "Start New Training Session" button on the dashboard, the server sends the puppy's profile information to the generation AI. The generation AI generates a training scenario optimized for the user's puppy, and the server sends it to the device. The user puts on the VR headset and starts the session. They give commands to the virtual pet, such as "sit," and the generation AI provides feedback in real time. The emotion engine recognizes the user's emotions and adjusts the feedback content based on them. After training is complete, the user receives feedback based on the analysis data from the generation AI and emotion engine, which is used for the next training session. If they have further questions, they can receive support from experts.

[0497] Example prompts to input to the generative AI model

[0498] "Generate a new puppy training scenario. Please suggest the best scenario based on the profile below.

[0499] Puppy breed: Labrador Retriever

[0500] Age: 6 months

[0501] Name: Lucky

[0502] Characteristics: Active and curious

[0503] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0504] Step 1: Enter user registration information and register

[0505] The user opens the service's website or app and enters the required information, such as name, email address, and password, on the new registration screen displayed on the device. The device acquires this information and sends it to the server. The server saves the entered information in the user database and sends the user a registration completion email.

[0506] Input: Name, Email Address, Password

[0507] Data processing: Validating input and saving to user database

[0508] Output: Sending a registration completion email

[0509] Specific operation: Enter and register the name "Yamada Taro", email address "taro@example.com", and password "password123"

[0510] Step 2: Activate your account and log in

[0511] The user activates their account by clicking the link in the email they received. When the user enters their credentials on the login screen, the device sends the information to the server, which verifies the credentials. If the login is successful, the server instructs the device to display the dashboard.

[0512] Input: Authentication information (email address, password)

[0513] Data processing: Authentication information verification

[0514] Output: Display dashboard

[0515] Specific behavior: Activate the account via the link in the email and log in with "taro@example.com" and "password123"

[0516] Step 3: Configure User Profile

[0517] When a user clicks on the "Profile Settings" tab on the dashboard, a form appears on the device asking for the pet's type, age, name, and characteristics. The user enters this information and presses the save button. The device sends the data to the server, which stores it in a database.

[0518] Input: Pet type, age, name, characteristics

[0519] Data processing: Saving input data

[0520] Output: Notification of save completion

[0521] Specific behavior: Enter and save pet information (Labrador retriever, 6 months old, lucky, active and curious)

[0522] Step 4: Generate training scenarios

[0523] When a user clicks the "Start a new training session" button on the dashboard, the server retrieves the user's profile data and sends it to the generation AI module. The generation AI module generates an optimal training scenario based on the profile data and returns it to the server. The server then sends the generated scenario to the device and displays it to the user.

[0524] Input: User profile data

[0525] Data processing: Generative AI generates training scenarios

[0526] Output: Display of training scenario

[0527] Specific behavior: Generate and display the optimal training scenario for "lucky"

[0528] Step 5: Setting up the VR environment

[0529] The user puts on the VR headset, the device launches the VR application, loads the training scenario received from the server, and the user presses the "Start Session" button.

[0530] Input: Training scenario

[0531] Data processing: Loading the training scenario

[0532] Output: VR environment setup complete

[0533] Specific actions: Putting on a VR headset and launching the app

[0534] Step 6: Training Run

[0535] The user issues commands such as "sit" to a virtual pet in VR. The device recognizes the voice command and transmits the command to the virtual pet. The virtual pet responds to the command according to the algorithm of the generating AI. The emotion engine recognizes the user's emotions in real time and feeds that data back to the generating AI. The generating AI dynamically adjusts its feedback and advice taking the user's emotions into account.

[0536] Input: User voice commands, emotion data

[0537] Data processing: speech recognition, emotion recognition, dynamic feedback adjustment

[0538] Output: Virtual pet behavior, real-time feedback

[0539] Specific actions: "Sit" command and virtual pet's response, "Good job" feedback

[0540] Step 7: End-of-training feedback

[0541] When the user presses the end button on the device, the device notifies the server that the session is over. The server then combines analytical data from the generative AI and emotion engine to provide detailed feedback to the user, including suggestions for improvement and the next training session.

[0542] Input: Analysis data for generative AI and emotion engine

[0543] Data processing: Integration of analytical data

[0544] Output: Detailed feedback

[0545] Specific behaviors: Providing advice such as, "Next time, let's continue practicing sitting."

[0546] Step 8: Expert help

[0547] When a user clicks the "Support" tab on the dashboard, a chat window appears on the device. The user enters a question, the device sends it to the server, and the server forwards it to an expert. The expert enters an answer to the question, and the server sends the answer to the user. The user can then review the answer and use it for their next training session.

[0548] Input: User question

[0549] Data processing: forwarding questions and sending answers

[0550] Output: Expert Support

[0551] Specific behavior: Asking questions such as "What should I do when my puppy barks?" and receiving answers

[0552] (Application example 2)

[0553] 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."

[0554] Conventional pet training systems lacked sufficient technical means for users to effectively train their pets. Furthermore, conventional factory automation systems lacked the ability to recognize workers' stress and emotions in real time and generate feedback. As a result, work efficiency declined and worker stress management was inadequate. The present invention aims to solve these problems.

[0555] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for accepting user registration information, means for setting a user profile, means for interacting with and training a pet using a virtual reality environment, means for generating a training plan optimized for the user and the pet using generative artificial intelligence, means for providing feedback to the user in real time, means for recognizing the user's emotions in real time using an emotion engine and dynamically adjusting the feedback and training scenario, and means for monitoring automated processes in a factory, evaluating worker efficiency and stress based on emotion recognition, and generating feedback. This allows users to effectively train their pets, and makes it possible to improve work efficiency and manage worker stress in automated processes in a factory.

[0556] "Means for accepting user registration information" refers to the process by which a user enters and registers the necessary information into the system.

[0557] "Means for setting up a user profile" is the process by which a user enters details about themselves and their pets into the system to create a personalized profile.

[0558] "Means for interacting with and training a pet using a virtual reality environment" refers to a process in which a user interacts with and trains a virtual pet using virtual reality technology.

[0559] "Means for generating training plans optimized for users and pets using generative artificial intelligence" refers to a process that uses a generative artificial intelligence algorithm to create an optimal training plan based on the characteristics of the user and pet.

[0560] "Means for providing feedback to the user in real time" refers to a process that provides instantaneous and appropriate advice and evaluation based on the user's actions and the pet's reactions.

[0561] "Means for recognizing user emotions in real time using an emotion engine and dynamically adjusting feedback and training scenarios" is a process for detecting a user's emotions and instantly changing training content and feedback based on that information.

[0562] "Means for monitoring automated processes in factories and evaluating worker efficiency and stress based on emotion recognition and generating feedback" refers to a process that works in conjunction with the work automation systems in factories, monitors the emotional state of workers, evaluates their work efficiency and stress levels, and provides feedback.

[0563] The present invention is a pet training experience system that uses a virtual reality environment and generative artificial intelligence to help users effectively train their pets, combined with an emotion engine that recognizes user emotions. This system can also monitor the emotions of workers in a factory environment to improve the efficiency of automated processes.

[0564] Program Overview

[0565] The program of this system includes the following processing steps.

[0566] 1. User Registration and Login

[0567] When a user inputs registration information from a terminal, the server stores the information in a user database.

[0568] 2. Setting up your user profile

[0569] The user provides details about the pet's breed, age, name and characteristics, and the server stores this information in a database.

[0570] 3. Start your training session

[0571] The server uses a generative AI module based on the user's profile data to create an optimal training plan.

[0572] 4. Setting up the VR environment

[0573] The user puts on a VR headset, and the server sends the generated training scenario to the device.

[0574] 5. Training execution

[0575] Users interact with their pets in a virtual environment, an emotion engine recognizes the user's emotions in real time, and generative AI provides corresponding feedback.

[0576] 6. Feedback and Support

[0577] After the training is completed, the server provides detailed feedback to the user based on the analysis data, and the user can receive support from experts.

[0578] This system is also effective in a factory environment, specifically for the following processes:

[0579] 1. Monitoring operations within the factory

[0580] The camera captures the worker's movements in real time and transmits them to a server.

[0581] 2. Emotion recognition

[0582] The emotion engine recognizes the worker's emotions in real time from the received video stream.

[0583] 3. Feedback Generation

[0584] The server uses a generative AI model to generate feedback for work efficiency and stress management based on emotional data.

[0585] The hardware used is a video streaming camera, a VR headset, and an automated robot in a factory. The software used is an emotion recognition model based on TensorFlow, a face recognition algorithm using OpenCV, and an HTTP-based communication module.

[0586] Specific examples

[0587] For example, a novice pet owner might follow these steps to train a new puppy:

[0588] 1. The pet owner registers and logs in to the system. On the profile setting screen, they enter the breed, age, and name of the puppy.

[0589] 2. The server requests a training scenario from the generation AI based on the user's profile information.

[0590] 3. The pet owner puts on a VR headset and commands their puppy in the virtual environment, such as "sit." The generative AI provides real-time feedback, and the emotion engine recognizes and adjusts the user's emotions.

[0591] 4. After the training is completed, the server provides detailed feedback based on the analysis data to help improve the next training session.

[0592] An example of a prompt is, "The worker is currently feeling ____ (e.g., stress, anxiety, satisfaction). Please provide suggestions for improving the work based on this emotion."

[0593] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0594] Step 1:

[0595] The user enters registration information from the device. Specifically, the device accepts necessary information such as name, email address, and password through a user interface. The entered information is sent to the server, which then stores it in a user database.

[0596] Step 2:

[0597] The user sets up a profile. The device displays a screen where the user can enter details about the pet's breed, age, name, and characteristics. Once the user enters this information and presses the save button, the information is sent to the server and stored in a database.

[0598] Step 3:

[0599] The user starts a training session. When the user clicks the "Start New Training Session" button on the device, the server obtains the user's profile data and sends it to the generation AI module. The generation AI module generates an optimal training plan based on the characteristics of the user and their pet, and returns the scenario to the server. The server then sends this scenario to the device.

[0600] Step 4:

[0601] The user wears a VR headset and performs a training scenario in the virtual reality environment. The server sends the training scenario to the device, which then launches the VR application and displays the training scenario to the user.

[0602] Step 5:

[0603] The user interacts with the virtual pet in VR (e.g., commanding it to "sit"). The device recognizes the voice command and sends the information to the server. The server then sends the information to the generation AI, which generates real-time feedback based on the command. The device then displays the feedback to the user.

[0604] Step 6:

[0605] The emotion engine recognizes the user's emotions in real time. The device extracts emotional information from the user's facial expressions and voice and sends it to the server. The server uses the emotion engine to analyze the user's emotions and feeds that data back to the generation AI. The generation AI dynamically adjusts feedback and training scenarios based on this information.

[0606] Step 7:

[0607] After the training session is over, the server generates detailed feedback based on the analysis data from the generation AI and emotion engine. The device displays this feedback to the user, including suggestions for improving the training and the next training session.

[0608] Step 8:

[0609] The user receives support from an expert. When the user clicks the "Support" tab on the device, a chat window appears. The device sends the user's question to the server, which then forwards it to the expert. The expert enters an answer, which is then sent to the user via the server. The user then checks the answer and uses it for their next training session.

[0610] Step 9:

[0611] This system monitors automated processes in factories. Cameras capture the activity in the factory in real time and send the video stream to a server. The server uses an emotion engine to recognize the emotions of workers, and a generative AI generates feedback based on that information to improve work efficiency and manage stress. The feedback is displayed on a management terminal in the factory.

[0612] 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.

[0613] 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.

[0614] 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.

[0615] [Second embodiment]

[0616] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0617] 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.

[0618] 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).

[0619] 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.

[0620] 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.

[0621] 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).

[0622] 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.

[0623] 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.

[0624] 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.

[0625] 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.

[0626] 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.

[0627] 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."

[0628] The present invention provides a pet training experience system that uses a virtual reality environment and generative artificial intelligence to enable users to effectively train their pets. Specific embodiments of this system are described below.

[0629] System Overview

[0630] After registering and setting up a profile, the system allows users to train with a virtual pet in a virtual reality (VR) environment, where generative artificial intelligence (AI) provides real-time feedback to improve the user's training skills. After completing the training, users can receive detailed feedback and expert support.

[0631] Program processing

[0632] 1. User Registration and Login

[0633] When a user opens the service's website or app, the device displays a new registration screen. The user enters the required information, such as name, email address, and password, and presses the registration button.

[0634] The server receives the entered information and saves it in the user database. After saving, the server sends the user a registration completion email.

[0635] The user activates their account by clicking the link in the email and enters their credentials on the login screen. The server verifies the credentials and, if the login is successful, instructs the device to display the dashboard.

[0636] 2. Setting up your user profile

[0637] When a user clicks on the "Profile Settings" tab on the dashboard, the device displays a form where they can enter their pet's type, age, name, and characteristics.

[0638] The user enters the pet's information and presses the save button, and the server saves this information to the database, completing the profile setup.

[0639] 3. Start your training session

[0640] The user clicks the "Start a new training session" button on the dashboard. The server retrieves the user's profile data and sends it to the generation AI module.

[0641] The AI ​​generates the optimal training scenario based on the characteristics of the user and their pet. The server sends the generated training scenario to the device and displays it to the user.

[0642] 4. Setting up the VR environment

[0643] The user puts on the VR headset, launches the VR application on the device, loads the training scenario received from the server, and presses the start session button when the user is ready.

[0644] 5. Training execution

[0645] The user interacts with the virtual pet in VR and gives instructions. For example, if the user gives the instruction "sit," the device recognizes the voice command and sends the instruction to the virtual pet.

[0646] The virtual pet reacts according to the algorithms of the generative AI, which determines whether to follow the user's commands, and provides real-time feedback based on the user's training methods and the pet's responses.

[0647] The server monitors the training progress and updates the data as needed.

[0648] 6. Feedback at the end of training

[0649] When the user presses the end training session button, the terminal notifies the server that the session is over.

[0650] The server retrieves the analytical data from the generated AI and provides detailed feedback to the user, including suggestions for improvement and the next training session.

[0651] 7. Expert Support

[0652] When a user clicks on the "Support" tab in the dashboard, the device displays a chat window where the user can type in their question and the server will forward it to an expert.

[0653] The expert enters the answer to the question, and the server sends the answer to the user, who can then check the answer and use it for the next training session if necessary.

[0654] Specific examples

[0655] For example, consider the case of a novice pet owner, Mr. A, training a new puppy.

[0656] 1. Person A registers with the system and logs in. On the profile settings screen, he enters the breed, age, and name of the puppy.

[0657] 2. Person A clicks the "Start a new training session" button on the dashboard. The server sends the puppy's profile information to the generation AI.

[0658] 3. The generation AI generates a training scenario optimized for Mr. A's puppy, and the server sends it to the device.

[0659] 4. Person A puts on the VR headset and starts the session. He commands the virtual pet, such as "sit," and the generating AI provides real-time feedback.

[0660] 5. After the training is completed, feedback based on the analysis data from the generative AI is received and used for the next training.

[0661] 6. If you have further questions, you can get help from experts.

[0662] Through the above process, the system helps novice pet owners and trainers to effectively train and deepen their relationships with their pets.

[0663] The processing flow will be explained below.

[0664] ---

[0665] User Registration and Login

[0666] Step 1:

[0667] The user opens the service's website or app, and the device displays the new registration screen.

[0668] Step 2:

[0669] The user enters the required information such as name, email address, and password, and presses the registration button.

[0670] Step 3:

[0671] The server receives the entered information and stores it in a user database.

[0672] Step 4:

[0673] After the server saves the data, it generates a registration completion email and sends it to the user.

[0674] Step 5:

[0675] The user clicks the link in the email they received to activate their account and enters their email address and password on the login screen.

[0676] Step 6:

[0677] The server checks the entered authentication information and, if authentication is successful, instructs the device to display the dashboard.

[0678] User Profile Settings

[0679] Step 1:

[0680] The user clicks the "Profile Settings" tab on the dashboard. The device displays a form to enter the pet's type, age, name, and characteristics.

[0681] Step 2:

[0682] The user enters the pet's information and presses the save button.

[0683] Step 3:

[0684] The server stores the information you entered in a database and completes your profile setup.

[0685] Starting a training session

[0686] Step 1:

[0687] A user clicks the "Start a New Training Session" button on the dashboard.

[0688] Step 2:

[0689] The server retrieves the user's profile data and sends it to the generation AI module.

[0690] Step 3:

[0691] Generative AI generates optimal training scenarios based on the characteristics of the user and their pet.

[0692] Step 4:

[0693] The server transmits the generated training scenario to the terminal and displays it to the user.

[0694] Setting up the VR environment

[0695] Step 1:

[0696] The user puts on the VR headset.

[0697] Step 2:

[0698] The device launches the VR application and loads the training scenario received from the server.

[0699] Step 3:

[0700] When the user is ready, he presses the Start Session button.

[0701] Training run

[0702] Step 1:

[0703] The user interacts with the virtual pet in VR and gives commands (e.g., "sit").

[0704] Step 2:

[0705] The terminal recognizes the user's voice commands and transmits the instructions to the virtual pet.

[0706] Step 3:

[0707] The virtual pet reacts according to the algorithm of the generating AI and decides whether to follow the user's instructions.

[0708] Step 4:

[0709] Generative AI provides real-time feedback based on your training methods and your pet's reactions.

[0710] Step 5:

[0711] The server monitors the training progress and updates the data as needed.

[0712] Feedback at the end of training

[0713] Step 1:

[0714] The user presses the end training session button.

[0715] Step 2:

[0716] The terminal notifies the server of the end of the session.

[0717] Step 3:

[0718] The server obtains analytical data from the generated AI and provides detailed feedback to the user.

[0719] Step 4:

[0720] Feedback includes suggestions for improvement and next training session.

[0721] Expert support

[0722] Step 1:

[0723] User clicks on the Support tab on the dashboard.

[0724] Step 2:

[0725] The terminal displays a chat window and the user types in a question.

[0726] Step 3:

[0727] The server forwards the question to an expert.

[0728] Step 4:

[0729] The expert enters the answer to the question and the server sends the answer to the user.

[0730] Step 5:

[0731] Users can review their answers and use them in future training sessions if necessary.

[0732] ---

[0733] The above is a description of the system program processing divided into specific steps.

[0734] Example 1

[0735] 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."

[0736] Currently, many pet owners struggle to find effective pet training methods. Learning the correct training methods can be particularly difficult for novice pet owners, potentially hindering efforts to improve their pet's behavior and build a relationship with them. Furthermore, actual training requires time and effort, and it is difficult to obtain immediate feedback, making it difficult to maximize the effectiveness of training. Furthermore, with little access to expert support, owners are unable to obtain appropriate advice for problem-solving. To address these challenges, a system is needed that allows users to effectively train their pets while learning on their own.

[0737] 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.

[0738] In this invention, the server includes means for accepting user registration information, means for setting a user profile, means for interacting with and training an animal using a virtual reality environment, means for generating a training plan optimized for the user and the animal using generative artificial intelligence, means for providing feedback to the user in real time, means for generating a training scenario by inputting prompts into the generative AI model, means for recognizing user instructions using voice recognition technology and transmitting them to the virtual animal, and means for receiving support from experts. This allows users to effectively train their pets while learning on their own. Furthermore, the ability to receive real-time feedback and expert support improves training effectiveness and deepens the relationship with their pet.

[0739] "User registration information" refers to basic information such as name, email address, and password provided by the user in order to use the service.

[0740] A "user profile" is a data set containing detailed information about a user and their pets, such as the pet's type, age, name, characteristics, etc.

[0741] A "virtual reality environment" is a three-dimensional space generated using computer technology, in which users experience and operate in a virtual space that is different from the real world.

[0742] "Animals" are living creatures such as dogs and cats kept as pets.

[0743] "Generative artificial intelligence" refers to algorithms that have the ability to autonomously learn and perform specific tasks based on input data.

[0744] A "training plan" is a plan that shows the specific training procedures and methods to be used on the user's pet, and is optimized by the generation AI.

[0745] "Feedback" refers to the advice and evaluation provided by the generative AI in real time during training.

[0746] A "generative AI model" is an algorithm that learns from large amounts of data and generates appropriate outputs for new inputs.

[0747] A "prompt sentence" is a sentence that is input to give instructions or ask a question to a generative AI model.

[0748] "Speech recognition technology" is a technology that analyzes the words spoken by a user and converts them into text data.

[0749] "Communication means" refers to electronic communication means used by users to receive support from experts, including web chat and email.

[0750] The present invention is a system that enables users to effectively train their pets. This system provides a consistent service, from accepting users' registration information, to training with their pets, and providing feedback and expert support after training is complete. An embodiment of the present invention will be described in detail below.

[0751] Hardware and Software Configuration

[0752] This system operates by combining multiple hardware and software components. The main hardware components include the user access terminals (e.g., PCs and smartphones), the VR headset (e.g., Oculus Quest 2), and the server. The main software components include the web application, the VR application, and the algorithms that run the generative AI model.

[0753] Processing flow

[0754] When a user opens the service's website or app, the device displays a registration screen. The user enters the required information, such as name, email address, and password, and presses the Register button. The server receives the information and stores it in a secure format in the user database. The server then sends a registration completion email, and the user clicks the link in the email to activate their account. If the user enters authentication information on the login screen, the server verifies the authentication information, and if the login is successful, the device displays a dashboard.

[0755] When a user clicks on the "Profile Settings" tab on the dashboard, the device displays a form for entering the pet's type, age, name, and characteristics. Once the user enters the information and presses the save button, the server saves the information to a database and completes the profile settings.

[0756] Next, when the user clicks the "Start a new training session" button on the dashboard, the server sends the user's profile data to the generation AI module. The generation AI generates an optimal training scenario based on the characteristics of the user and their pet, and the scenario is sent from the server to the device. The user then puts on a VR headset and launches a dedicated VR application. The training scenario received from the server is loaded there, and the user presses the start session button when ready.

[0757] During a training session, the user interacts with the virtual pet in VR, issuing voice commands (e.g., "sit"). The device uses voice recognition technology to interpret the commands and transmit them to the virtual pet. Generative AI controls the virtual pet's movements and provides real-time feedback to the user. At the same time, the server monitors the training progress and updates the data as needed.

[0758] After a training session, the user presses the end button, and the device notifies the server that the session has ended. The server then retrieves analytical data from the generated AI and provides detailed feedback to the user, including specific advice on areas for improvement and the next training session. Furthermore, when the user clicks the "Support" tab on the dashboard, the device displays a chat window where the user can enter a question. The question is then forwarded to an expert via the server, who then provides the user with an answer.

[0759] Specific examples

[0760] For example, when a novice pet owner trains a new puppy, the following steps are taken: First, the user registers and logs in. Next, they enter the puppy's information on the profile settings screen. When the user clicks the "Start New Training Session" button, the server sends the puppy's profile information to the generation AI, which then generates an optimal training scenario. This scenario is sent to the device, and the user puts on a VR headset to start the session. During the training session, the user issues voice commands to the virtual pet and receives real-time feedback based on its responses. After the session ends, the user receives detailed feedback based on analytical data, which can be used for the next training session. The user can also ask experts any questions and receive appropriate advice.

[0761] In a specific example, when a user inputs a prompt sentence into a generative AI model to generate a training scenario, an example of the prompt sentence is, "Please create a basic obedience training scenario for a 5-year-old Labrador Retriever."

[0762] This invention allows users to effectively train their pets and deepen their relationships with them. Real-time feedback and expert support improve training effectiveness and are expected to improve pet behavior.

[0763] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0764] Step 1:

[0765] The user opens the service's website or app.

[0766] Input: A user action to open a website or app.

[0767] Output: The terminal displays the new registration screen.

[0768] What happens: The device's browser loads a web page and displays a sign-up form on the screen, where the user enters information such as their name, email address, and password.

[0769] Step 2:

[0770] The user enters the required information and presses the registration button.

[0771] Input: The user enters their name, email address, password, etc. and clicks the Register button.

[0772] Output: Information is sent to the server.

[0773] What it does: The device collects the information entered and sends it to the server using the HTTPS protocol. The server receives it, encrypts it, and stores it in a database.

[0774] Step 3:

[0775] The server sends a registration completion email.

[0776] Input: User's email address and registration information.

[0777] Output: A registration completion email will be sent to the user's email address.

[0778] Specific operation: The server uses the email sending API to send a registration confirmation email to the email address specified by the user, which includes a link to activate the account.

[0779] Step 4:

[0780] The user clicks the link in the email to activate their account.

[0781] Input: A user clicks on a link in an email.

[0782] Output: The account is activated and the user is redirected to the login screen.

[0783] What happens: When the user clicks the link, the server updates the account status to be activated, and then the user is taken to the login screen.

[0784] Step 5:

[0785] The user enters their credentials on the login screen.

[0786] Input: The user enters their email address and password and clicks the login button.

[0787] Output: The server verifies the credentials and if the login is successful, displays the dashboard.

[0788] Specific operation: The device sends the entered authentication information to the server, which checks it against the database. If it matches, authentication is successful and the user's dashboard screen is displayed.

[0789] Step 6:

[0790] The user clicks the Profile Settings tab on the dashboard.

[0791] Input: User clicks on the "Profile Settings" tab.

[0792] Output: The device displays the profile setup form.

[0793] What happens: The device loads the form associated with the "Profile Settings" tab and displays it on the screen.

[0794] Step 7:

[0795] The user enters the pet's type, age, name, and characteristics and presses the save button.

[0796] Input: The user enters pet information and presses the save button.

[0797] Output: The information is sent to the server and stored in a database.

[0798] Specific operation: The terminal sends the entered pet information to the server as an HTTP request, and the server receives the information and stores it in a database.

[0799] Step 8:

[0800] User clicks the "Start New Training Session" button.

[0801] Input: User clicks the "Start New Training Session" button.

[0802] Output: The server sends the user profile data to the generation AI module.

[0803] Specific operation: The server collects the user's profile data and sends it to the generation AI module, which then inputs a prompt to generate the optimal training scenario.

[0804] Step 9:

[0805] Generative AI generates optimal training scenarios.

[0806] Input: Profile data based on user and pet characteristics and a prompt.

[0807] Output: The generated training scenario.

[0808] How it works: The generation AI analyzes the profile data and prompts, and uses the learning model to generate optimal training scenarios. The generated scenario data is then sent to the server.

[0809] Step 10:

[0810] The server transmits the generated training scenario to the terminal.

[0811] Input: Training scenario data sent from the generative AI.

[0812] Output: The terminal displays the training scenario to the user.

[0813] Specific operation: The server sends training scenario data to the terminal, which then renders it into a format that is displayed to the user.

[0814] Step 11:

[0815] The user puts on the VR headset and starts the session.

[0816] Input: The user puts on a VR headset and launches an application.

[0817] Output: A training session begins in the VR environment.

[0818] Specific operation: The user puts on the VR headset and launches the dedicated application. The application loads the training scenario received from the server.

[0819] Step 12:

[0820] The user issues voice commands to the virtual pet.

[0821] Input: The user's voice command (e.g., "sit").

[0822] Output: The virtual pet responds to voice commands.

[0823] Specific operation: The device uses voice recognition technology to analyze the user's voice commands and transmit them to the virtual pet. The generated AI controls the pet's movements based on those instructions.

[0824] Step 13:

[0825] Generative AI provides real-time feedback.

[0826] Input: User behavior and pet reaction data.

[0827] Output: Real-time feedback to the user.

[0828] Specific operation: The generative AI analyzes the collected data and provides appropriate feedback to the user via screen or voice.

[0829] Step 14:

[0830] The server monitors the training progress and updates the data as needed.

[0831] Input: The data being trained.

[0832] Output: Updated progress data.

[0833] What it does: The server monitors training session data in real time and updates the information in the database as needed.

[0834] Step 15:

[0835] The user presses the end training session button.

[0836] Input: The user presses the end session button.

[0837] Output: A session termination notification is sent to the server.

[0838] Specific operation: The terminal sends a session termination request to the server, and the server terminates the session.

[0839] Step 16:

[0840] The server obtains analytical data from the generated AI and provides detailed feedback to the user.

[0841] Input: Analysis data from the generative AI.

[0842] Output: Detailed feedback to the user.

[0843] Specific behavior: The server obtains analytical data from the generated AI and uses it to provide detailed feedback to the user, including specific advice on areas for improvement and the next training session.

[0844] Step 17:

[0845] User clicks on the Support tab on the dashboard.

[0846] Input: User clicks on the Support tab.

[0847] Output: The terminal displays a chat window.

[0848] Specific behavior: The device displays a support chat window, allowing the user to enter questions.

[0849] Step 18:

[0850] The user enters a question and the server forwards it to an expert.

[0851] Input: The user's question.

[0852] Output: The question is forwarded to an expert.

[0853] Specific operation: The device receives the user's question and sends it to the server, which then forwards the question to an expert.

[0854] Step 19:

[0855] The expert enters the answer and the server sends the answer to the user.

[0856] Input: Expert answers.

[0857] Output: The answer to the user.

[0858] Specific operation: The expert enters the answer, and the server sends the answer to the user's device. The user can check the answer and incorporate it into the next training if necessary.

[0859] (Application example 1)

[0860] 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."

[0861] Modern factories require new workers to quickly and effectively master advanced robot operation and maintenance. However, operating actual robots is expensive and poses a high risk of operational errors, making it difficult to provide adequate training using traditional educational methods. Furthermore, there are limited means for workers to learn while receiving real-time feedback. To address this challenge, an efficient training system using virtual reality environments and generative artificial intelligence is needed.

[0862] 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.

[0863] In this invention, the server includes means for accepting user registration information, means for setting a user profile, means for interacting with and educating a training subject using a virtual reality environment, means for generating an instruction plan optimized for the user and the training subject using generative artificial intelligence, means for providing feedback to the user in real time, and means for executing a specific training scenario using a virtual reality device, thereby enabling a user to safely and effectively acquire robot operation and maintenance skills in the virtual reality environment.

[0864] "Means for accepting user registration information" refers to a mechanism for inputting and saving basic user information (such as name, email address, and password).

[0865] The "means for setting a user profile" is a mechanism by which a user sets and saves details of the training subject (robot model, experience level, etc.).

[0866] "Means for interacting with and educating training subjects using a virtual reality environment" refers to a system in which a user interacts with a training subject (robot) in a virtual environment using a VR device, and receives training in operation and maintenance.

[0867] "Means for generating an educational plan optimized for the user and training subject using generative artificial intelligence" refers to AI technology for generating optimal training scenarios based on profile information of the user and training subject.

[0868] "Means of providing feedback to users in real time" refers to a system in which AI provides on-the-spot improvements and advice based on the user's operations and instructions during training.

[0869] "Means for executing a specific training scenario using a virtual reality device" refers to a mechanism for executing a generated training scenario in a virtual environment using a VR headset or related device.

[0870] MODE FOR CARRYING OUT THE INVENTION

[0871] System Overview

[0872] This invention is an educational system that enables factory workers to effectively learn robot operation and maintenance in a virtual reality (VR) environment. It includes user registration, profile setup, training in the VR environment, real-time feedback provided by generative artificial intelligence (AI), feedback after training, and expert support.

[0873] System Configuration

[0874] The system uses the following hardware and software:

[0875] Hardware: VR headset (e.g. Oculus Quest 2), computer, network connection equipment

[0876] Software: VR framework, generative AI module, server application (e.g., Flask, Django)

[0877] 1. User Registration and Login

[0878] The server first provides a means to accept user registration information. The user registers with the system and enters basic information (name, email address, password, etc.). The entered information is saved on the server, and the user is sent a registration completion email. The user then clicks the link in the email to activate their account and enters their login information to access the system.

[0879] 2. Setting up your user profile

[0880] After registration, the user uses the profile setting means to enter details of the robot they wish to train (robot model, experience level, etc.) The server receives this information and stores it in a database.

[0881] 3. Start your training session

[0882] When a user clicks the "Start a new training session" button on the dashboard, the server retrieves the user's profile data and sends it to the Generative AI module, which then generates an optimal training plan for the user and training subject and sends it to the user via the server.

[0883] 4. Setting up the VR environment

[0884] The user puts on a VR headset and executes a specific training scenario, and the server uses the VR framework to load the scenario. Once the user is ready, the training session begins.

[0885] 5. Training execution and real-time feedback

[0886] In the virtual reality environment, the user issues instructions to the robot, and the generative AI provides real-time feedback based on these instructions. For example, if the user issues an instruction such as "set welding points," the AI ​​will provide the execution results and appropriate feedback in real time.

[0887] 6. Post-training feedback

[0888] When the user presses the end button for the training session, the server records the end of the session and provides detailed feedback based on the analysis data from the generative AI, including an evaluation of the user's actions and suggestions for improvement next time.

[0889] 7. Expert Support

[0890] If the user needs additional support, the server provides a means of communication to receive support from experts. The user can enter a question, and the expert will provide an answer, which the user can use to help with their next training.

[0891] Examples of concrete examples and prompts

[0892] As a concrete example, consider a scenario in which new factory worker B is learning to operate a new welding robot. After completing registration and profile setup, B issues the command "set welding point" in the VR environment. The generating AI provides real-time feedback based on this command. After completing the training, B receives detailed feedback from the generating AI, which can be used to improve the next training session.

[0893] Examples of prompts include:

[0894] "Create a new training scenario. The target robot is 'Weld-2000' and the operation skill level is 'Beginner'. Create a scenario that includes the following elements: 1. Safety check 2. Basic operation 3. Weld point setting 4. Real-time feedback"

[0895] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0896] Step 1:

[0897] User Registration and Login

[0898] The server receives the user's name, email address, and password and stores them in a database. It processes the information entered by the user and automatically generates and sends a confirmation email. If the user clicks on the link in the email to activate their account, the server verifies the authentication information and allows them to log in.

[0899] Input: Name, Email Address, Password

[0900] Output: Sending a registration completion email, saving authentication information

[0901] What happens: A user enters their name, email address, and password into a web form and presses the submit button. The server receives this information, stores it in a database, and sends a confirmation email to the user.

[0902] Step 2:

[0903] User Profile Settings

[0904] The server receives information such as the robot model and experience level input by the user through the user profile setting means, and stores this profile information in a database.

[0905] Input: Robot model, experience level

[0906] Output: Save profile information

[0907] Specific operation: The user selects the robot model "Weld-2000" and the experience level "Beginner" on the dashboard and presses the save button. The server receives this and saves it in the database.

[0908] Step 3:

[0909] Starting a training session

[0910] When a user clicks the "Start New Training Session" button, the server sends the user's profile data to the Generative AI module, which then generates an optimal training scenario and sends it to the user.

[0911] Input: User profile data

[0912] Output: Optimal training scenario

[0913] Specific operation: The server obtains the user's profile information and sends it to the generation AI. The generation AI generates a "Weld-2000 training scenario for beginners," and the server sends it to the user's device.

[0914] Step 4:

[0915] Setting up the VR environment

[0916] The user puts on the VR headset, the server loads the training scenario through the VR framework, and when the user is ready, they press the "Start Session" button.

[0917] Input: Training scenario

[0918] Output: Loaded VR training environment

[0919] Specific operation: The user starts the VR headset, the server provides guidance to load the received training scenario into the VR framework, and the user presses the start session button.

[0920] Step 5:

[0921] Training execution and real-time feedback

[0922] When a user issues an instruction to the robot in VR, the AI ​​analyzes it and provides real-time feedback. For example, it determines whether an instruction such as "set welding points" is accurate and provides on-the-spot advice.

[0923] Input: User instructions

[0924] Output: Real-time feedback

[0925] Specific operation: The user gives instructions in VR such as "Set welding points," and the generated AI analyzes them. For example, if the welding points are appropriate, it will return feedback such as "This is the correct position."

[0926] Step 6:

[0927] Post-training feedback

[0928] When the user presses the end button for the training session, the server records the end of the session and provides detailed feedback based on the analysis data from the generated AI, including an overall evaluation and suggestions for improvement next time.

[0929] Input: Training session data

[0930] Output: Detailed feedback

[0931] How it works: When a user presses the end session button, the server collects session data, and the AI ​​analyzes it to create detailed feedback. Improvements and next steps are displayed on the user's device.

[0932] Step 7:

[0933] Expert support

[0934] If the user needs further assistance, the server provides a means of communication to forward the user's question to an expert, and receives the expert's answer and sends it to the user.

[0935] Input: User question

[0936] Output: Expert Answers

[0937] How it works: A user clicks on the "Support" tab on the dashboard and enters a question. The server forwards it to an expert, who then enters an answer and sends it back to the server. Finally, the answer is displayed to the user.

[0938] 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.

[0939] The present invention is a system that combines a virtual reality environment and a pet training experience system that uses generative artificial intelligence to enable users to effectively train their pets with an emotion engine that recognizes the user's emotions. Specific embodiments of this system are described below.

[0940] System Overview

[0941] After users register and set up their profile, the system allows them to train with a virtual pet in a virtual reality (VR) environment. Generative artificial intelligence (AI) provides real-time feedback to improve the user's training skills. Furthermore, it uses an emotion engine that recognizes the user's emotions in real time and dynamically adjusts the feedback and training scenarios. After completing training, users can receive detailed feedback and expert support.

[0942] Program processing

[0943] 1. User Registration and Login

[0944] When a user opens the service's website or app, the device displays a new registration screen. The user enters the required information, such as name, email address, and password, and presses the registration button.

[0945] The server receives the entered information and saves it in the user database. After saving, the server sends the user a registration completion email.

[0946] The user activates their account by clicking the link in the email and enters their credentials on the login screen. The server verifies the credentials and, if the login is successful, instructs the device to display the dashboard.

[0947] 2. Setting up your user profile

[0948] When a user clicks on the "Profile Settings" tab on the dashboard, the device displays a form where they can enter their pet's type, age, name, and characteristics.

[0949] The user enters the pet's information and presses the save button, and the server saves this information to the database, completing the profile setup.

[0950] 3. Start your training session

[0951] The user clicks the "Start a new training session" button on the dashboard. The server retrieves the user's profile data and sends it to the generation AI module.

[0952] The AI ​​generates the optimal training scenario based on the characteristics of the user and their pet. The server sends the generated scenario to the device and displays it to the user.

[0953] 4. Setting up the VR environment

[0954] The user puts on the VR headset, launches the VR application on the device, loads the training scenario received from the server, and presses the start session button when the user is ready.

[0955] 5. Training execution

[0956] The user interacts with the virtual pet in VR and gives instructions. For example, if the user commands "sit," the device recognizes the voice command and sends the instruction to the virtual pet.

[0957] The virtual pet reacts according to the algorithms of the generative AI, which determines whether to follow the user's commands, and provides real-time feedback based on the user's training methods and the pet's responses.

[0958] The emotion engine recognizes the user's emotions and feeds that data back to the generative AI, which then adjusts the feedback and advice it provides to take the user's emotions into account.

[0959] The emotion engine analyzes the emotional data and dynamically changes the training scenario according to the user's stress and satisfaction.

[0960] 6. Feedback at the end of training

[0961] When the user presses the end training session button, the terminal notifies the server that the session is over.

[0962] The server retrieves analytical data from the generative AI and emotion engine and provides detailed feedback to the user, including suggestions for improvement and the next training session.

[0963] 7. Expert Support

[0964] When a user clicks on the "Support" tab in the dashboard, the device displays a chat window where the user can type in their question and the server will forward it to an expert.

[0965] The expert enters the answer to the question, and the server sends the answer to the user, who can then check the answer and use it for the next training session if necessary.

[0966] Specific examples

[0967] For example, consider the case of a novice pet owner, Mr. A, training a new puppy.

[0968] 1. Person A registers with the system and logs in. On the profile settings screen, he enters the breed, age, and name of the puppy.

[0969] 2. Person A clicks the "Start a new training session" button on the dashboard. The server sends the puppy's profile information to the generation AI.

[0970] 3. The generation AI generates a training scenario optimized for Mr. A's puppy, and the server sends it to the device.

[0971] 4. Person A puts on the VR headset and begins the session. He commands the virtual pet, such as "sit," and the generative AI provides real-time feedback. The emotion engine recognizes Person A's emotions and adjusts the feedback accordingly.

[0972] 5. After the training is complete, feedback based on the analysis data from the generative AI and emotion engine is received and used for the next training.

[0973] 6. If you have further questions, get help from a professional.

[0974] Through this process, the system can help beginner pet owners and trainers effectively train their pets and deepen their relationships with them. It also recognizes the user's emotions and provides appropriate feedback, improving user satisfaction and training effectiveness.

[0975] The processing flow will be explained below.

[0976] ---

[0977] User Registration and Login

[0978] Step 1:

[0979] The user opens the service's website or app, and the device displays the new registration screen.

[0980] Step 2:

[0981] The user enters the required information such as name, email address, and password, and presses the registration button.

[0982] Step 3:

[0983] The server receives the entered information and stores it in a user database.

[0984] Step 4:

[0985] After the server saves the data, it generates a registration completion email and sends it to the user.

[0986] Step 5:

[0987] The user clicks the link in the email they received to activate their account and enters their email address and password on the login screen.

[0988] Step 6:

[0989] The server checks the entered authentication information and, if authentication is successful, instructs the device to display the dashboard.

[0990] User Profile Settings

[0991] Step 1:

[0992] The user clicks the "Profile Settings" tab on the dashboard. The device displays a form to enter the pet's type, age, name, and characteristics.

[0993] Step 2:

[0994] The user enters the pet's information and presses the save button.

[0995] Step 3:

[0996] The server stores the information you entered in a database and completes your profile setup.

[0997] Starting a training session

[0998] Step 1:

[0999] A user clicks the "Start a New Training Session" button on the dashboard.

[1000] Step 2:

[1001] The server retrieves the user's profile data and sends it to the generation AI module.

[1002] Step 3:

[1003] Generative AI generates optimal training scenarios based on the characteristics of the user and their pet.

[1004] Step 4:

[1005] The server transmits the generated training scenario to the terminal and displays it to the user.

[1006] Setting up the VR environment

[1007] Step 1:

[1008] The user puts on the VR headset.

[1009] Step 2:

[1010] The device launches the VR application and loads the training scenario received from the server.

[1011] Step 3:

[1012] When the user is ready, he presses the Start Session button.

[1013] Training run

[1014] Step 1:

[1015] The user interacts with the virtual pet in VR and gives commands (e.g., "sit").

[1016] Step 2:

[1017] The terminal recognizes the user's voice commands and transmits the instructions to the virtual pet.

[1018] Step 3:

[1019] The virtual pet reacts according to the algorithm of the generating AI and decides whether to follow the user's instructions.

[1020] Step 4:

[1021] Generative AI provides real-time feedback based on your training methods and your pet's reactions.

[1022] Step 5:

[1023] The emotion engine recognizes the user's emotions and feeds that data back to the generative AI, which then adjusts the feedback and advice it provides to take the user's emotions into account.

[1024] Step 6:

[1025] The emotion engine analyzes the emotional data and dynamically changes the training scenario according to the user's stress and satisfaction.

[1026] Feedback at the end of training

[1027] Step 1:

[1028] The user presses the end training session button.

[1029] Step 2:

[1030] The terminal notifies the server of the end of the session.

[1031] Step 3:

[1032] The server obtains analytical data from the generative AI and emotion engine and provides detailed feedback to the user.

[1033] Step 4:

[1034] Feedback includes suggestions for improvement and next training session.

[1035] Expert support

[1036] Step 1:

[1037] User clicks on the Support tab on the dashboard.

[1038] Step 2:

[1039] The terminal displays a chat window and the user types in a question.

[1040] Step 3:

[1041] The server forwards the question to an expert.

[1042] Step 4:

[1043] The expert enters the answer to the question and the server sends the answer to the user.

[1044] Step 5:

[1045] Users can review their answers and use them in future training sessions if necessary.

[1046] ---

[1047] The above is a description of the system program processing divided into specific steps.

[1048] Example 2

[1049] 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."

[1050] Conventional pet training systems require users to have specialized knowledge and experience to effectively train their pets, making them difficult for beginners to use. Furthermore, the effectiveness of training is limited because the system does not provide feedback that takes into account the user's emotions. Furthermore, only a limited number of systems offer support from experts.

[1051] 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.

[1052] In this invention, the server includes means for accepting user registration information, means for setting a user profile, means for interacting with and training a pet using a virtual reality environment, means for generating a training plan optimized for the user and the pet using generative artificial intelligence, means for recognizing voice commands and conveying instructions to the virtual pet, means for recognizing the user's emotions in real time using an emotion engine and dynamically adjusting feedback and training scenarios, and means for providing feedback to the user in real time. This allows even beginners to train effectively and provides feedback that takes the user's emotions into consideration. Furthermore, the quality of training is improved by easily receiving support from experts.

[1053] "Means for accepting user registration information" refers to the function by which a user inputs their own information into the system and the system records that information.

[1054] "Means for setting up a user profile" is a function that allows users to input detailed information about themselves and their pets, and the system then stores and manages that information.

[1055] "Means for interacting with and training a pet using a virtual reality environment" refers to a function that uses virtual reality technology to allow a user to interact with and train a virtual pet.

[1056] "Means for generating training plans optimized for users and their pets using generative artificial intelligence" refers to a function that utilizes artificial intelligence technology to automatically create training plans based on the characteristics of users and their pets.

[1057] The "means for recognizing voice commands and transmitting instructions to a virtual pet" is a function for recognizing the user's voice and transmitting those instructions to a virtual pet.

[1058] "Means of recognizing the user's emotions in real time using an emotion engine and dynamically adjusting feedback and training scenarios" is a function that analyzes the user's facial expressions and voice to determine their emotions and adapts feedback and training content based on that.

[1059] "Means for providing feedback to the user in real time" refers to a function that provides immediate evaluation and advice on the operations and reactions performed by the user during training.

[1060] The present invention is a system that combines a virtual reality environment and a pet training experience system that uses generative artificial intelligence to enable users to effectively train their pets with an emotion engine that recognizes the user's emotions. Specific embodiments of this system are described below.

[1061] User Registration and Login

[1062] When a user opens the service's website or app, the device displays a new registration screen. The user enters the required information, such as name, email address, and password, and presses the registration button. The server receives the entered information and saves it in the user database. After saving, the server sends the user a registration completion email. The user clicks the link in the received email to activate their account and enters their authentication information on the login screen. The server verifies the authentication information, and if login is successful, it instructs the device to display the dashboard.

[1063] User Profile Settings

[1064] When a user clicks on the "Profile Settings" tab on the dashboard, the terminal displays a form to input the pet's type, age, name, and characteristics. The user enters the pet's information and presses the save button. The server saves this information in the database and completes the profile settings.

[1065] Starting a training session

[1066] The user clicks the "Start a new training session" button on the dashboard. The server retrieves the user's profile data and sends it to the generation AI module. The generation AI generates an optimal training scenario based on the characteristics of the user and their pet. The server sends the generated scenario to the device and displays it to the user.

[1067] Setting up the VR environment

[1068] The user puts on the VR headset, the device launches the VR application, loads the training scenario received from the server, and when the user is ready, presses the start session button.

[1069] Training run

[1070] The user interacts with the virtual pet in VR and gives instructions. For example, if the user commands "sit," the device recognizes the voice command and sends the instruction to the virtual pet. The virtual pet responds according to the algorithm of the generation AI and determines whether to follow the user's instructions. The emotion engine recognizes the user's emotions and feeds that data back to the generation AI. The generation AI dynamically adjusts its feedback and advice taking the user's emotions into account.

[1071] Feedback at the end of training

[1072] When the user presses the end button on the device, the device notifies the server that the session is over. The server then receives analytical data from the generative AI and emotion engine and provides detailed feedback to the user, including suggestions for improvement and advice for the next training session.

[1073] Expert support

[1074] When a user clicks the "Support" tab on the dashboard, the device displays a chat window. The user enters a question, which the server forwards to an expert. The expert enters an answer to the question, and the server sends the answer to the user. The user reviews the answer and, if necessary, uses it for future training.

[1075] Hardware and software used

[1076] Hardware: PC, smartphone, VR headset, audio input device

[1077] Software: Web browsers, server software (e.g., Apache, NGINX), databases (e.g., MySQL, PostgreSQL), generative AI modules, emotion engines, VR applications, chat applications

[1078] Specific examples

[1079] For example, consider a novice pet owner training a new puppy. The user registers and logs in to the system. They enter the puppy's breed, age, and name on the profile settings screen. When the user clicks the "Start New Training Session" button on the dashboard, the server sends the puppy's profile information to the generation AI. The generation AI generates a training scenario optimized for the user's puppy, and the server sends it to the device. The user puts on the VR headset and starts the session. They give commands to the virtual pet, such as "sit," and the generation AI provides feedback in real time. The emotion engine recognizes the user's emotions and adjusts the feedback content based on them. After training is complete, the user receives feedback based on the analysis data from the generation AI and emotion engine, which is used for the next training session. If they have further questions, they can receive support from experts.

[1080] Example prompts to input to the generative AI model

[1081] "Generate a new puppy training scenario. Please suggest the best scenario based on the profile below.

[1082] Puppy breed: Labrador Retriever

[1083] Age: 6 months

[1084] Name: Lucky

[1085] Characteristics: Active and curious

[1086] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1087] Step 1: Enter user registration information and register

[1088] The user opens the service's website or app and enters the required information, such as name, email address, and password, on the new registration screen displayed on the device. The device acquires this information and sends it to the server. The server saves the entered information in the user database and sends the user a registration completion email.

[1089] Input: Name, Email Address, Password

[1090] Data processing: Validating input and saving to user database

[1091] Output: Sending a registration completion email

[1092] Specific operation: Enter and register the name "Yamada Taro", email address "taro@example.com", and password "password123"

[1093] Step 2: Activate your account and log in

[1094] The user activates their account by clicking the link in the email they received. When the user enters their credentials on the login screen, the device sends the information to the server, which verifies the credentials. If the login is successful, the server instructs the device to display the dashboard.

[1095] Input: Authentication information (email address, password)

[1096] Data processing: Authentication information verification

[1097] Output: Display dashboard

[1098] Specific behavior: Activate the account via the link in the email and log in with "taro@example.com" and "password123"

[1099] Step 3: Configure User Profile

[1100] When a user clicks on the "Profile Settings" tab on the dashboard, a form appears on the device asking for the pet's type, age, name, and characteristics. The user enters this information and presses the save button. The device sends the data to the server, which stores it in a database.

[1101] Input: Pet type, age, name, characteristics

[1102] Data processing: Saving input data

[1103] Output: Notification of save completion

[1104] Specific behavior: Enter and save pet information (Labrador retriever, 6 months old, lucky, active and curious)

[1105] Step 4: Generate training scenarios

[1106] When a user clicks the "Start a new training session" button on the dashboard, the server retrieves the user's profile data and sends it to the generation AI module. The generation AI module generates an optimal training scenario based on the profile data and returns it to the server. The server then sends the generated scenario to the device and displays it to the user.

[1107] Input: User profile data

[1108] Data processing: Generative AI generates training scenarios

[1109] Output: Display of training scenario

[1110] Specific behavior: Generate and display the optimal training scenario for "lucky"

[1111] Step 5: Setting up the VR environment

[1112] The user puts on the VR headset, the device launches the VR application, loads the training scenario received from the server, and the user presses the "Start Session" button.

[1113] Input: Training scenario

[1114] Data processing: Loading the training scenario

[1115] Output: VR environment setup complete

[1116] Specific actions: Putting on a VR headset and launching the app

[1117] Step 6: Training Run

[1118] The user issues commands such as "sit" to a virtual pet in VR. The device recognizes the voice command and transmits the command to the virtual pet. The virtual pet responds to the command according to the algorithm of the generating AI. The emotion engine recognizes the user's emotions in real time and feeds that data back to the generating AI. The generating AI dynamically adjusts its feedback and advice taking the user's emotions into account.

[1119] Input: User voice commands, emotion data

[1120] Data processing: speech recognition, emotion recognition, dynamic feedback adjustment

[1121] Output: Virtual pet behavior, real-time feedback

[1122] Specific actions: "Sit" command and virtual pet's response, "Good job" feedback

[1123] Step 7: End-of-training feedback

[1124] When the user presses the end button on the device, the device notifies the server that the session is over. The server then combines analytical data from the generative AI and emotion engine to provide detailed feedback to the user, including suggestions for improvement and the next training session.

[1125] Input: Analysis data for generative AI and emotion engine

[1126] Data processing: Integration of analytical data

[1127] Output: Detailed feedback

[1128] Specific behaviors: Providing advice such as, "Next time, let's continue practicing sitting."

[1129] Step 8: Expert help

[1130] When a user clicks the "Support" tab on the dashboard, a chat window appears on the device. The user enters a question, the device sends it to the server, and the server forwards it to an expert. The expert enters an answer to the question, and the server sends the answer to the user. The user can then review the answer and use it for their next training session.

[1131] Input: User question

[1132] Data processing: forwarding questions and sending answers

[1133] Output: Expert Support

[1134] Specific behavior: Asking questions such as "What should I do when my puppy barks?" and receiving answers

[1135] (Application example 2)

[1136] 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."

[1137] Conventional pet training systems lacked sufficient technical means for users to effectively train their pets. Furthermore, conventional factory automation systems lacked the ability to recognize workers' stress and emotions in real time and generate feedback. As a result, work efficiency declined and worker stress management was inadequate. The present invention aims to solve these problems.

[1138] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for accepting user registration information, means for setting a user profile, means for interacting with and training a pet using a virtual reality environment, means for generating a training plan optimized for the user and the pet using generative artificial intelligence, means for providing feedback to the user in real time, means for recognizing the user's emotions in real time using an emotion engine and dynamically adjusting the feedback and training scenario, and means for monitoring automated processes in a factory, evaluating worker efficiency and stress based on emotion recognition, and generating feedback. This allows users to effectively train their pets, and makes it possible to improve work efficiency and manage worker stress in automated processes in a factory.

[1139] "Means for accepting user registration information" refers to the process by which a user enters and registers the necessary information into the system.

[1140] "Means for setting up a user profile" is the process by which a user enters details about themselves and their pets into the system to create a personalized profile.

[1141] "Means for interacting with and training a pet using a virtual reality environment" refers to a process in which a user interacts with and trains a virtual pet using virtual reality technology.

[1142] "Means for generating training plans optimized for users and pets using generative artificial intelligence" refers to a process that uses a generative artificial intelligence algorithm to create an optimal training plan based on the characteristics of the user and pet.

[1143] "Means for providing feedback to the user in real time" refers to a process that provides instantaneous and appropriate advice and evaluation based on the user's actions and the pet's reactions.

[1144] "Means for recognizing user emotions in real time using an emotion engine and dynamically adjusting feedback and training scenarios" is a process for detecting a user's emotions and instantly changing training content and feedback based on that information.

[1145] "Means for monitoring automated processes in factories and evaluating worker efficiency and stress based on emotion recognition and generating feedback" refers to a process that works in conjunction with the work automation systems in factories, monitors the emotional state of workers, evaluates their work efficiency and stress levels, and provides feedback.

[1146] The present invention is a pet training experience system that uses a virtual reality environment and generative artificial intelligence to help users effectively train their pets, combined with an emotion engine that recognizes user emotions. This system can also monitor the emotions of workers in a factory environment to improve the efficiency of automated processes.

[1147] Program Overview

[1148] The program of this system includes the following processing steps.

[1149] 1. User Registration and Login

[1150] When a user inputs registration information from a terminal, the server stores the information in a user database.

[1151] 2. Setting up your user profile

[1152] The user provides details about the pet's breed, age, name and characteristics, and the server stores this information in a database.

[1153] 3. Start your training session

[1154] The server uses a generative AI module based on the user's profile data to create an optimal training plan.

[1155] 4. Setting up the VR environment

[1156] The user puts on a VR headset, and the server sends the generated training scenario to the device.

[1157] 5. Training execution

[1158] Users interact with their pets in a virtual environment, an emotion engine recognizes the user's emotions in real time, and generative AI provides corresponding feedback.

[1159] 6. Feedback and Support

[1160] After the training is completed, the server provides detailed feedback to the user based on the analysis data, and the user can receive support from experts.

[1161] This system is also effective in a factory environment, specifically for the following processes:

[1162] 1. Monitoring operations within the factory

[1163] The camera captures the worker's movements in real time and transmits them to a server.

[1164] 2. Emotion recognition

[1165] The emotion engine recognizes the worker's emotions in real time from the received video stream.

[1166] 3. Feedback Generation

[1167] The server uses a generative AI model to generate feedback for work efficiency and stress management based on emotional data.

[1168] The hardware used is a video streaming camera, a VR headset, and an automated robot in a factory. The software used is an emotion recognition model based on TensorFlow, a face recognition algorithm using OpenCV, and an HTTP-based communication module.

[1169] Specific examples

[1170] For example, a novice pet owner might follow these steps to train a new puppy:

[1171] 1. The pet owner registers and logs in to the system. On the profile setting screen, they enter the breed, age, and name of the puppy.

[1172] 2. The server requests a training scenario from the generation AI based on the user's profile information.

[1173] 3. The pet owner puts on a VR headset and commands their puppy in the virtual environment, such as "sit." The generative AI provides real-time feedback, and the emotion engine recognizes and adjusts the user's emotions.

[1174] 4. After the training is completed, the server provides detailed feedback based on the analysis data to help improve the next training session.

[1175] An example of a prompt is, "The worker is currently feeling ____ (e.g., stress, anxiety, satisfaction). Please provide suggestions for improving the work based on this emotion."

[1176] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1177] Step 1:

[1178] The user enters registration information from the device. Specifically, the device accepts necessary information such as name, email address, and password through a user interface. The entered information is sent to the server, which then stores it in a user database.

[1179] Step 2:

[1180] The user sets up a profile. The device displays a screen where the user can enter details about the pet's breed, age, name, and characteristics. Once the user enters this information and presses the save button, the information is sent to the server and stored in a database.

[1181] Step 3:

[1182] The user starts a training session. When the user clicks the "Start New Training Session" button on the device, the server obtains the user's profile data and sends it to the generation AI module. The generation AI module generates an optimal training plan based on the characteristics of the user and their pet, and returns the scenario to the server. The server then sends this scenario to the device.

[1183] Step 4:

[1184] The user wears a VR headset and performs a training scenario in the virtual reality environment. The server sends the training scenario to the device, which then launches the VR application and displays the training scenario to the user.

[1185] Step 5:

[1186] The user interacts with the virtual pet in VR (e.g., commanding it to "sit"). The device recognizes the voice command and sends the information to the server. The server then sends the information to the generation AI, which generates real-time feedback based on the command. The device then displays the feedback to the user.

[1187] Step 6:

[1188] The emotion engine recognizes the user's emotions in real time. The device extracts emotional information from the user's facial expressions and voice and sends it to the server. The server uses the emotion engine to analyze the user's emotions and feeds that data back to the generation AI. The generation AI dynamically adjusts feedback and training scenarios based on this information.

[1189] Step 7:

[1190] After the training session is over, the server generates detailed feedback based on the analysis data from the generation AI and emotion engine. The device displays this feedback to the user, including suggestions for improving the training and the next training session.

[1191] Step 8:

[1192] The user receives support from an expert. When the user clicks the "Support" tab on the device, a chat window appears. The device sends the user's question to the server, which then forwards it to the expert. The expert enters an answer, which is then sent to the user via the server. The user then checks the answer and uses it for their next training session.

[1193] Step 9:

[1194] This system monitors automated processes in factories. Cameras capture the activity in the factory in real time and send the video stream to a server. The server uses an emotion engine to recognize the emotions of workers, and a generative AI generates feedback based on that information to improve work efficiency and manage stress. The feedback is displayed on a management terminal in the factory.

[1195] 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.

[1196] 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.

[1197] 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.

[1198] [Third embodiment]

[1199] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1200] 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.

[1201] 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).

[1202] 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.

[1203] 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.

[1204] 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).

[1205] 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.

[1206] 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.

[1207] 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.

[1208] 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.

[1209] 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.

[1210] 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."

[1211] The present invention provides a pet training experience system that uses a virtual reality environment and generative artificial intelligence to enable users to effectively train their pets. Specific embodiments of this system are described below.

[1212] System Overview

[1213] After registering and setting up a profile, the system allows users to train with a virtual pet in a virtual reality (VR) environment, where generative artificial intelligence (AI) provides real-time feedback to improve the user's training skills. After completing the training, users can receive detailed feedback and expert support.

[1214] Program processing

[1215] 1. User Registration and Login

[1216] When a user opens the service's website or app, the device displays a new registration screen. The user enters the required information, such as name, email address, and password, and presses the registration button.

[1217] The server receives the entered information and saves it in the user database. After saving, the server sends the user a registration completion email.

[1218] The user activates their account by clicking the link in the email and enters their credentials on the login screen. The server verifies the credentials and, if the login is successful, instructs the device to display the dashboard.

[1219] 2. Setting up your user profile

[1220] When a user clicks on the "Profile Settings" tab on the dashboard, the device displays a form where they can enter their pet's type, age, name, and characteristics.

[1221] The user enters the pet's information and presses the save button, and the server saves this information to the database, completing the profile setup.

[1222] 3. Start your training session

[1223] The user clicks the "Start a new training session" button on the dashboard. The server retrieves the user's profile data and sends it to the generation AI module.

[1224] The AI ​​generates the optimal training scenario based on the characteristics of the user and their pet. The server sends the generated training scenario to the device and displays it to the user.

[1225] 4. Setting up the VR environment

[1226] The user puts on the VR headset, launches the VR application on the device, loads the training scenario received from the server, and presses the start session button when the user is ready.

[1227] 5. Training execution

[1228] The user interacts with the virtual pet in VR and gives instructions. For example, if the user gives the instruction "sit," the device recognizes the voice command and sends the instruction to the virtual pet.

[1229] The virtual pet reacts according to the algorithms of the generative AI, which determines whether to follow the user's commands, and provides real-time feedback based on the user's training methods and the pet's responses.

[1230] The server monitors the training progress and updates the data as needed.

[1231] 6. Feedback at the end of training

[1232] When the user presses the end training session button, the terminal notifies the server that the session is over.

[1233] The server retrieves the analytical data from the generated AI and provides detailed feedback to the user, including suggestions for improvement and the next training session.

[1234] 7. Expert Support

[1235] When a user clicks on the "Support" tab in the dashboard, the device displays a chat window where the user can type in their question and the server will forward it to an expert.

[1236] The expert enters the answer to the question, and the server sends the answer to the user, who can then check the answer and use it for the next training session if necessary.

[1237] Specific examples

[1238] For example, consider the case of a novice pet owner, Mr. A, training a new puppy.

[1239] 1. Person A registers with the system and logs in. On the profile settings screen, he enters the breed, age, and name of the puppy.

[1240] 2. Person A clicks the "Start a new training session" button on the dashboard. The server sends the puppy's profile information to the generation AI.

[1241] 3. The generation AI generates a training scenario optimized for Mr. A's puppy, and the server sends it to the device.

[1242] 4. Person A puts on the VR headset and starts the session. He commands the virtual pet, such as "sit," and the generating AI provides real-time feedback.

[1243] 5. After the training is completed, feedback based on the analysis data from the generative AI is received and used for the next training.

[1244] 6. If you have further questions, you can get help from experts.

[1245] Through the above process, the system helps novice pet owners and trainers to effectively train and deepen their relationships with their pets.

[1246] The processing flow will be explained below.

[1247] ---

[1248] User Registration and Login

[1249] Step 1:

[1250] The user opens the service's website or app, and the device displays the new registration screen.

[1251] Step 2:

[1252] The user enters the required information such as name, email address, and password, and presses the registration button.

[1253] Step 3:

[1254] The server receives the entered information and stores it in a user database.

[1255] Step 4:

[1256] After the server saves the data, it generates a registration completion email and sends it to the user.

[1257] Step 5:

[1258] The user clicks the link in the email they received to activate their account and enters their email address and password on the login screen.

[1259] Step 6:

[1260] The server checks the entered authentication information and, if authentication is successful, instructs the device to display the dashboard.

[1261] User Profile Settings

[1262] Step 1:

[1263] The user clicks the "Profile Settings" tab on the dashboard. The device displays a form to enter the pet's type, age, name, and characteristics.

[1264] Step 2:

[1265] The user enters the pet's information and presses the save button.

[1266] Step 3:

[1267] The server stores the information you entered in a database and completes your profile setup.

[1268] Starting a training session

[1269] Step 1:

[1270] A user clicks the "Start a New Training Session" button on the dashboard.

[1271] Step 2:

[1272] The server retrieves the user's profile data and sends it to the generation AI module.

[1273] Step 3:

[1274] Generative AI generates optimal training scenarios based on the characteristics of the user and their pet.

[1275] Step 4:

[1276] The server transmits the generated training scenario to the terminal and displays it to the user.

[1277] Setting up the VR environment

[1278] Step 1:

[1279] The user puts on the VR headset.

[1280] Step 2:

[1281] The device launches the VR application and loads the training scenario received from the server.

[1282] Step 3:

[1283] When the user is ready, he presses the Start Session button.

[1284] Training run

[1285] Step 1:

[1286] The user interacts with the virtual pet in VR and gives commands (e.g., "sit").

[1287] Step 2:

[1288] The terminal recognizes the user's voice commands and transmits the instructions to the virtual pet.

[1289] Step 3:

[1290] The virtual pet reacts according to the algorithm of the generating AI and decides whether to follow the user's instructions.

[1291] Step 4:

[1292] Generative AI provides real-time feedback based on your training methods and your pet's reactions.

[1293] Step 5:

[1294] The server monitors the training progress and updates the data as needed.

[1295] Feedback at the end of training

[1296] Step 1:

[1297] The user presses the end training session button.

[1298] Step 2:

[1299] The terminal notifies the server of the end of the session.

[1300] Step 3:

[1301] The server obtains analytical data from the generated AI and provides detailed feedback to the user.

[1302] Step 4:

[1303] Feedback includes suggestions for improvement and next training session.

[1304] Expert support

[1305] Step 1:

[1306] User clicks on the Support tab on the dashboard.

[1307] Step 2:

[1308] The terminal displays a chat window and the user types in a question.

[1309] Step 3:

[1310] The server forwards the question to an expert.

[1311] Step 4:

[1312] The expert enters the answer to the question and the server sends the answer to the user.

[1313] Step 5:

[1314] Users can review their answers and use them in future training sessions if necessary.

[1315] ---

[1316] The above is a description of the system program processing divided into specific steps.

[1317] Example 1

[1318] 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."

[1319] Currently, many pet owners struggle to find effective pet training methods. Learning the correct training methods can be particularly difficult for novice pet owners, potentially hindering efforts to improve their pet's behavior and build a relationship with them. Furthermore, actual training requires time and effort, and it is difficult to obtain immediate feedback, making it difficult to maximize the effectiveness of training. Furthermore, with little access to expert support, owners are unable to obtain appropriate advice for problem-solving. To address these challenges, a system is needed that allows users to effectively train their pets while learning on their own.

[1320] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1321] In this invention, the server includes means for accepting user registration information, means for setting a user profile, means for interacting with and training an animal using a virtual reality environment, means for generating a training plan optimized for the user and the animal using generative artificial intelligence, means for providing feedback to the user in real time, means for generating a training scenario by inputting prompts into the generative AI model, means for recognizing user instructions using voice recognition technology and transmitting them to the virtual animal, and means for receiving support from experts. This allows users to effectively train their pets while learning on their own. Furthermore, the ability to receive real-time feedback and expert support improves training effectiveness and deepens the relationship with their pet.

[1322] "User registration information" refers to basic information such as name, email address, and password provided by the user in order to use the service.

[1323] A "user profile" is a data set containing detailed information about a user and their pets, such as the pet's type, age, name, characteristics, etc.

[1324] A "virtual reality environment" is a three-dimensional space generated using computer technology, in which users experience and operate in a virtual space that is different from the real world.

[1325] "Animals" are living creatures such as dogs and cats kept as pets.

[1326] "Generative artificial intelligence" refers to algorithms that have the ability to autonomously learn and perform specific tasks based on input data.

[1327] A "training plan" is a plan that shows the specific training procedures and methods to be used on the user's pet, and is optimized by the generation AI.

[1328] "Feedback" refers to the advice and evaluation provided by the generative AI in real time during training.

[1329] A "generative AI model" is an algorithm that learns from large amounts of data and generates appropriate outputs for new inputs.

[1330] A "prompt sentence" is a sentence that is input to give instructions or ask a question to a generative AI model.

[1331] "Speech recognition technology" is a technology that analyzes the words spoken by a user and converts them into text data.

[1332] "Communication means" refers to electronic communication means used by users to receive support from experts, including web chat and email.

[1333] The present invention is a system that enables users to effectively train their pets. This system provides a consistent service, from accepting users' registration information, to training with their pets, and providing feedback and expert support after training is complete. An embodiment of the present invention will be described in detail below.

[1334] Hardware and Software Configuration

[1335] This system operates by combining multiple hardware and software components. The main hardware components include the user access terminals (e.g., PCs and smartphones), the VR headset (e.g., Oculus Quest 2), and the server. The main software components include the web application, the VR application, and the algorithms that run the generative AI model.

[1336] Processing flow

[1337] When a user opens the service's website or app, the device displays a registration screen. The user enters the required information, such as name, email address, and password, and presses the Register button. The server receives the information and stores it in a secure format in the user database. The server then sends a registration completion email, and the user clicks the link in the email to activate their account. If the user enters authentication information on the login screen, the server verifies the authentication information, and if the login is successful, the device displays a dashboard.

[1338] When a user clicks on the "Profile Settings" tab on the dashboard, the device displays a form for entering the pet's type, age, name, and characteristics. Once the user enters the information and presses the save button, the server saves the information to a database and completes the profile settings.

[1339] Next, when the user clicks the "Start a new training session" button on the dashboard, the server sends the user's profile data to the generation AI module. The generation AI generates an optimal training scenario based on the characteristics of the user and their pet, and the scenario is sent from the server to the device. The user then puts on a VR headset and launches a dedicated VR application. The training scenario received from the server is loaded there, and the user presses the start session button when ready.

[1340] During a training session, the user interacts with the virtual pet in VR, issuing voice commands (e.g., "sit"). The device uses voice recognition technology to interpret the commands and transmit them to the virtual pet. Generative AI controls the virtual pet's movements and provides real-time feedback to the user. At the same time, the server monitors the training progress and updates the data as needed.

[1341] After a training session, the user presses the end button, and the device notifies the server that the session has ended. The server then retrieves analytical data from the generated AI and provides detailed feedback to the user, including specific advice on areas for improvement and the next training session. Furthermore, when the user clicks the "Support" tab on the dashboard, the device displays a chat window where the user can enter a question. The question is then forwarded to an expert via the server, who then provides the user with an answer.

[1342] Specific examples

[1343] For example, when a novice pet owner trains a new puppy, the following steps are taken: First, the user registers and logs in. Next, they enter the puppy's information on the profile settings screen. When the user clicks the "Start New Training Session" button, the server sends the puppy's profile information to the generation AI, which then generates an optimal training scenario. This scenario is sent to the device, and the user puts on a VR headset to start the session. During the training session, the user issues voice commands to the virtual pet and receives real-time feedback based on its responses. After the session ends, the user receives detailed feedback based on analytical data, which can be used for the next training session. The user can also ask experts any questions and receive appropriate advice.

[1344] In a specific example, when a user inputs a prompt sentence into a generative AI model to generate a training scenario, an example of the prompt sentence is, "Please create a basic obedience training scenario for a 5-year-old Labrador Retriever."

[1345] This invention allows users to effectively train their pets and deepen their relationships with them. Real-time feedback and expert support improve training effectiveness and are expected to improve pet behavior.

[1346] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1347] Step 1:

[1348] The user opens the service's website or app.

[1349] Input: A user action to open a website or app.

[1350] Output: The terminal displays the new registration screen.

[1351] What happens: The device's browser loads a web page and displays a sign-up form on the screen, where the user enters information such as their name, email address, and password.

[1352] Step 2:

[1353] The user enters the required information and presses the registration button.

[1354] Input: The user enters their name, email address, password, etc. and clicks the Register button.

[1355] Output: Information is sent to the server.

[1356] What it does: The device collects the information entered and sends it to the server using the HTTPS protocol. The server receives it, encrypts it, and stores it in a database.

[1357] Step 3:

[1358] The server sends a registration completion email.

[1359] Input: User's email address and registration information.

[1360] Output: A registration completion email will be sent to the user's email address.

[1361] Specific operation: The server uses the email sending API to send a registration confirmation email to the email address specified by the user, which includes a link to activate the account.

[1362] Step 4:

[1363] The user clicks the link in the email to activate their account.

[1364] Input: A user clicks on a link in an email.

[1365] Output: The account is activated and the user is redirected to the login screen.

[1366] What happens: When the user clicks the link, the server updates the account status to be activated, and then the user is taken to the login screen.

[1367] Step 5:

[1368] The user enters their credentials on the login screen.

[1369] Input: The user enters their email address and password and clicks the login button.

[1370] Output: The server verifies the credentials and if the login is successful, displays the dashboard.

[1371] Specific operation: The device sends the entered authentication information to the server, which checks it against the database. If it matches, authentication is successful and the user's dashboard screen is displayed.

[1372] Step 6:

[1373] The user clicks the Profile Settings tab on the dashboard.

[1374] Input: User clicks on the "Profile Settings" tab.

[1375] Output: The device displays the profile setup form.

[1376] What happens: The device loads the form associated with the "Profile Settings" tab and displays it on the screen.

[1377] Step 7:

[1378] The user enters the pet's type, age, name, and characteristics and presses the save button.

[1379] Input: The user enters pet information and presses the save button.

[1380] Output: The information is sent to the server and stored in a database.

[1381] Specific operation: The terminal sends the entered pet information to the server as an HTTP request, and the server receives the information and stores it in a database.

[1382] Step 8:

[1383] User clicks the "Start New Training Session" button.

[1384] Input: User clicks the "Start New Training Session" button.

[1385] Output: The server sends the user profile data to the generation AI module.

[1386] Specific operation: The server collects the user's profile data and sends it to the generation AI module, which then inputs a prompt to generate the optimal training scenario.

[1387] Step 9:

[1388] Generative AI generates optimal training scenarios.

[1389] Input: Profile data based on user and pet characteristics and a prompt.

[1390] Output: The generated training scenario.

[1391] How it works: The generation AI analyzes the profile data and prompts, and uses the learning model to generate optimal training scenarios. The generated scenario data is then sent to the server.

[1392] Step 10:

[1393] The server transmits the generated training scenario to the terminal.

[1394] Input: Training scenario data sent from the generative AI.

[1395] Output: The terminal displays the training scenario to the user.

[1396] Specific operation: The server sends training scenario data to the terminal, which then renders it into a format that is displayed to the user.

[1397] Step 11:

[1398] The user puts on the VR headset and starts the session.

[1399] Input: The user puts on a VR headset and launches an application.

[1400] Output: A training session begins in the VR environment.

[1401] Specific operation: The user puts on the VR headset and launches the dedicated application. The application loads the training scenario received from the server.

[1402] Step 12:

[1403] The user issues voice commands to the virtual pet.

[1404] Input: The user's voice command (e.g., "sit").

[1405] Output: The virtual pet responds to voice commands.

[1406] Specific operation: The device uses voice recognition technology to analyze the user's voice commands and transmit them to the virtual pet. The generated AI controls the pet's movements based on those instructions.

[1407] Step 13:

[1408] Generative AI provides real-time feedback.

[1409] Input: User behavior and pet reaction data.

[1410] Output: Real-time feedback to the user.

[1411] Specific operation: The generative AI analyzes the collected data and provides appropriate feedback to the user via screen or voice.

[1412] Step 14:

[1413] The server monitors the training progress and updates the data as needed.

[1414] Input: The data being trained.

[1415] Output: Updated progress data.

[1416] What it does: The server monitors training session data in real time and updates the information in the database as needed.

[1417] Step 15:

[1418] The user presses the end training session button.

[1419] Input: The user presses the end session button.

[1420] Output: A session termination notification is sent to the server.

[1421] Specific operation: The terminal sends a session termination request to the server, and the server terminates the session.

[1422] Step 16:

[1423] The server obtains analytical data from the generated AI and provides detailed feedback to the user.

[1424] Input: Analysis data from the generative AI.

[1425] Output: Detailed feedback to the user.

[1426] Specific behavior: The server obtains analytical data from the generated AI and uses it to provide detailed feedback to the user, including specific advice on areas for improvement and the next training session.

[1427] Step 17:

[1428] User clicks on the Support tab on the dashboard.

[1429] Input: User clicks on the Support tab.

[1430] Output: The terminal displays a chat window.

[1431] Specific behavior: The device displays a support chat window, allowing the user to enter questions.

[1432] Step 18:

[1433] The user enters a question and the server forwards it to an expert.

[1434] Input: The user's question.

[1435] Output: The question is forwarded to an expert.

[1436] Specific operation: The device receives the user's question and sends it to the server, which then forwards the question to an expert.

[1437] Step 19:

[1438] The expert enters the answer and the server sends the answer to the user.

[1439] Input: Expert answers.

[1440] Output: The answer to the user.

[1441] Specific operation: The expert enters the answer, and the server sends the answer to the user's device. The user can check the answer and incorporate it into the next training if necessary.

[1442] (Application example 1)

[1443] 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."

[1444] Modern factories require new workers to quickly and effectively master advanced robot operation and maintenance. However, operating actual robots is expensive and poses a high risk of operational errors, making it difficult to provide adequate training using traditional educational methods. Furthermore, there are limited means for workers to learn while receiving real-time feedback. To address this challenge, an efficient training system using virtual reality environments and generative artificial intelligence is needed.

[1445] 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.

[1446] In this invention, the server includes means for accepting user registration information, means for setting a user profile, means for interacting with and educating a training subject using a virtual reality environment, means for generating an instruction plan optimized for the user and the training subject using generative artificial intelligence, means for providing feedback to the user in real time, and means for executing a specific training scenario using a virtual reality device, thereby enabling a user to safely and effectively acquire robot operation and maintenance skills in the virtual reality environment.

[1447] "Means for accepting user registration information" refers to a mechanism for inputting and saving basic user information (such as name, email address, and password).

[1448] The "means for setting a user profile" is a mechanism by which a user sets and saves details of the training subject (robot model, experience level, etc.).

[1449] "Means for interacting with and educating training subjects using a virtual reality environment" refers to a system in which a user interacts with a training subject (robot) in a virtual environment using a VR device, and receives training in operation and maintenance.

[1450] "Means for generating an educational plan optimized for the user and training subject using generative artificial intelligence" refers to AI technology for generating optimal training scenarios based on profile information of the user and training subject.

[1451] "Means of providing feedback to users in real time" refers to a system in which AI provides on-the-spot improvements and advice based on the user's operations and instructions during training.

[1452] "Means for executing a specific training scenario using a virtual reality device" refers to a mechanism for executing a generated training scenario in a virtual environment using a VR headset or related device.

[1453] MODE FOR CARRYING OUT THE INVENTION

[1454] System Overview

[1455] This invention is an educational system that enables factory workers to effectively learn robot operation and maintenance in a virtual reality (VR) environment. It includes user registration, profile setup, training in the VR environment, real-time feedback provided by generative artificial intelligence (AI), feedback after training, and expert support.

[1456] System Configuration

[1457] The system uses the following hardware and software:

[1458] Hardware: VR headset (e.g. Oculus Quest 2), computer, network connection equipment

[1459] Software: VR framework, generative AI module, server application (e.g., Flask, Django)

[1460] 1. User Registration and Login

[1461] The server first provides a means to accept user registration information. The user registers with the system and enters basic information (name, email address, password, etc.). The entered information is saved on the server, and the user is sent a registration completion email. The user then clicks the link in the email to activate their account and enters their login information to access the system.

[1462] 2. Setting up your user profile

[1463] After registration, the user uses the profile setting means to enter details of the robot they wish to train (robot model, experience level, etc.) The server receives this information and stores it in a database.

[1464] 3. Start your training session

[1465] When a user clicks the "Start a new training session" button on the dashboard, the server retrieves the user's profile data and sends it to the Generative AI module, which then generates an optimal training plan for the user and training subject and sends it to the user via the server.

[1466] 4. Setting up the VR environment

[1467] The user puts on a VR headset and executes a specific training scenario, and the server uses the VR framework to load the scenario. Once the user is ready, the training session begins.

[1468] 5. Training execution and real-time feedback

[1469] In the virtual reality environment, the user issues instructions to the robot, and the generative AI provides real-time feedback based on these instructions. For example, if the user issues an instruction such as "set welding points," the AI ​​will provide the execution results and appropriate feedback in real time.

[1470] 6. Post-training feedback

[1471] When the user presses the end button for the training session, the server records the end of the session and provides detailed feedback based on the analysis data from the generative AI, including an evaluation of the user's actions and suggestions for improvement next time.

[1472] 7. Expert Support

[1473] If the user needs additional support, the server provides a means of communication to receive support from experts. The user can enter a question, and the expert will provide an answer, which the user can use to help with their next training.

[1474] Examples of concrete examples and prompts

[1475] As a concrete example, consider a scenario in which new factory worker B is learning to operate a new welding robot. After completing registration and profile setup, B issues the command "set welding point" in the VR environment. The generating AI provides real-time feedback based on this command. After completing the training, B receives detailed feedback from the generating AI, which can be used to improve the next training session.

[1476] Examples of prompts include:

[1477] "Create a new training scenario. The target robot is 'Weld-2000' and the operation skill level is 'Beginner'. Create a scenario that includes the following elements: 1. Safety check 2. Basic operation 3. Weld point setting 4. Real-time feedback"

[1478] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1479] Step 1:

[1480] User Registration and Login

[1481] The server receives the user's name, email address, and password and stores them in a database. It processes the information entered by the user and automatically generates and sends a confirmation email. If the user clicks on the link in the email to activate their account, the server verifies the authentication information and allows them to log in.

[1482] Input: Name, Email Address, Password

[1483] Output: Sending a registration completion email, saving authentication information

[1484] What happens: A user enters their name, email address, and password into a web form and presses the submit button. The server receives this information, stores it in a database, and sends a confirmation email to the user.

[1485] Step 2:

[1486] User Profile Settings

[1487] The server receives information such as the robot model and experience level input by the user through the user profile setting means, and stores this profile information in a database.

[1488] Input: Robot model, experience level

[1489] Output: Save profile information

[1490] Specific operation: The user selects the robot model "Weld-2000" and the experience level "Beginner" on the dashboard and presses the save button. The server receives this and saves it in the database.

[1491] Step 3:

[1492] Starting a training session

[1493] When a user clicks the "Start New Training Session" button, the server sends the user's profile data to the Generative AI module, which then generates an optimal training scenario and sends it to the user.

[1494] Input: User profile data

[1495] Output: Optimal training scenario

[1496] Specific operation: The server obtains the user's profile information and sends it to the generation AI. The generation AI generates a "Weld-2000 training scenario for beginners," and the server sends it to the user's device.

[1497] Step 4:

[1498] Setting up the VR environment

[1499] The user puts on the VR headset, the server loads the training scenario through the VR framework, and when the user is ready, they press the "Start Session" button.

[1500] Input: Training scenario

[1501] Output: Loaded VR training environment

[1502] Specific operation: The user starts the VR headset, the server provides guidance to load the received training scenario into the VR framework, and the user presses the start session button.

[1503] Step 5:

[1504] Training execution and real-time feedback

[1505] When a user issues an instruction to the robot in VR, the AI ​​analyzes it and provides real-time feedback. For example, it determines whether an instruction such as "set welding points" is accurate and provides on-the-spot advice.

[1506] Input: User instructions

[1507] Output: Real-time feedback

[1508] Specific operation: The user gives instructions in VR such as "Set welding points," and the generated AI analyzes them. For example, if the welding points are appropriate, it will return feedback such as "This is the correct position."

[1509] Step 6:

[1510] Post-training feedback

[1511] When the user presses the end button for the training session, the server records the end of the session and provides detailed feedback based on the analysis data from the generated AI, including an overall evaluation and suggestions for improvement next time.

[1512] Input: Training session data

[1513] Output: Detailed feedback

[1514] How it works: When a user presses the end session button, the server collects session data, and the AI ​​analyzes it to create detailed feedback. Improvements and next steps are displayed on the user's device.

[1515] Step 7:

[1516] Expert support

[1517] If the user needs further assistance, the server provides a means of communication to forward the user's question to an expert, and receives the expert's answer and sends it to the user.

[1518] Input: User question

[1519] Output: Expert Answers

[1520] How it works: A user clicks on the "Support" tab on the dashboard and enters a question. The server forwards it to an expert, who then enters an answer and sends it back to the server. Finally, the answer is displayed to the user.

[1521] 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.

[1522] The present invention is a system that combines a virtual reality environment and a pet training experience system that uses generative artificial intelligence to enable users to effectively train their pets with an emotion engine that recognizes the user's emotions. Specific embodiments of this system are described below.

[1523] System Overview

[1524] After users register and set up their profile, the system allows them to train with a virtual pet in a virtual reality (VR) environment. Generative artificial intelligence (AI) provides real-time feedback to improve the user's training skills. Furthermore, it uses an emotion engine that recognizes the user's emotions in real time and dynamically adjusts the feedback and training scenarios. After completing training, users can receive detailed feedback and expert support.

[1525] Program processing

[1526] 1. User Registration and Login

[1527] When a user opens the service's website or app, the device displays a new registration screen. The user enters the required information, such as name, email address, and password, and presses the registration button.

[1528] The server receives the entered information and saves it in the user database. After saving, the server sends the user a registration completion email.

[1529] The user activates their account by clicking the link in the email and enters their credentials on the login screen. The server verifies the credentials and, if the login is successful, instructs the device to display the dashboard.

[1530] 2. Setting up your user profile

[1531] When a user clicks on the "Profile Settings" tab on the dashboard, the device displays a form where they can enter their pet's type, age, name, and characteristics.

[1532] The user enters the pet's information and presses the save button, and the server saves this information to the database, completing the profile setup.

[1533] 3. Start your training session

[1534] The user clicks the "Start a new training session" button on the dashboard. The server retrieves the user's profile data and sends it to the generation AI module.

[1535] The AI ​​generates the optimal training scenario based on the characteristics of the user and their pet. The server sends the generated scenario to the device and displays it to the user.

[1536] 4. Setting up the VR environment

[1537] The user puts on the VR headset, launches the VR application on the device, loads the training scenario received from the server, and presses the start session button when the user is ready.

[1538] 5. Training execution

[1539] The user interacts with the virtual pet in VR and gives instructions. For example, if the user commands "sit," the device recognizes the voice command and sends the instruction to the virtual pet.

[1540] The virtual pet reacts according to the algorithms of the generative AI, which determines whether to follow the user's commands, and provides real-time feedback based on the user's training methods and the pet's responses.

[1541] The emotion engine recognizes the user's emotions and feeds that data back to the generative AI, which then adjusts the feedback and advice it provides to take the user's emotions into account.

[1542] The emotion engine analyzes the emotional data and dynamically changes the training scenario according to the user's stress and satisfaction.

[1543] 6. Feedback at the end of training

[1544] When the user presses the end training session button, the terminal notifies the server that the session is over.

[1545] The server retrieves analytical data from the generative AI and emotion engine and provides detailed feedback to the user, including suggestions for improvement and the next training session.

[1546] 7. Expert Support

[1547] When a user clicks on the "Support" tab in the dashboard, the device displays a chat window where the user can type in their question and the server will forward it to an expert.

[1548] The expert enters the answer to the question, and the server sends the answer to the user, who can then check the answer and use it for the next training session if necessary.

[1549] Specific examples

[1550] For example, consider the case of a novice pet owner, Mr. A, training a new puppy.

[1551] 1. Person A registers with the system and logs in. On the profile settings screen, he enters the breed, age, and name of the puppy.

[1552] 2. Person A clicks the "Start a new training session" button on the dashboard. The server sends the puppy's profile information to the generation AI.

[1553] 3. The generation AI generates a training scenario optimized for Mr. A's puppy, and the server sends it to the device.

[1554] 4. Person A puts on the VR headset and begins the session. He commands the virtual pet, such as "sit," and the generative AI provides real-time feedback. The emotion engine recognizes Person A's emotions and adjusts the feedback accordingly.

[1555] 5. After the training is complete, feedback based on the analysis data from the generative AI and emotion engine is received and used for the next training.

[1556] 6. If you have further questions, get help from a professional.

[1557] Through this process, the system can help beginner pet owners and trainers effectively train their pets and deepen their relationships with them. It also recognizes the user's emotions and provides appropriate feedback, improving user satisfaction and training effectiveness.

[1558] The processing flow will be explained below.

[1559] ---

[1560] User Registration and Login

[1561] Step 1:

[1562] The user opens the service's website or app, and the device displays the new registration screen.

[1563] Step 2:

[1564] The user enters the required information such as name, email address, and password, and presses the registration button.

[1565] Step 3:

[1566] The server receives the entered information and stores it in a user database.

[1567] Step 4:

[1568] After the server saves the data, it generates a registration completion email and sends it to the user.

[1569] Step 5:

[1570] The user clicks the link in the email they received to activate their account and enters their email address and password on the login screen.

[1571] Step 6:

[1572] The server checks the entered authentication information and, if authentication is successful, instructs the device to display the dashboard.

[1573] User Profile Settings

[1574] Step 1:

[1575] The user clicks the "Profile Settings" tab on the dashboard. The device displays a form to enter the pet's type, age, name, and characteristics.

[1576] Step 2:

[1577] The user enters the pet's information and presses the save button.

[1578] Step 3:

[1579] The server stores the information you entered in a database and completes your profile setup.

[1580] Starting a training session

[1581] Step 1:

[1582] A user clicks the "Start a New Training Session" button on the dashboard.

[1583] Step 2:

[1584] The server retrieves the user's profile data and sends it to the generation AI module.

[1585] Step 3:

[1586] Generative AI generates optimal training scenarios based on the characteristics of the user and their pet.

[1587] Step 4:

[1588] The server transmits the generated training scenario to the terminal and displays it to the user.

[1589] Setting up the VR environment

[1590] Step 1:

[1591] The user puts on the VR headset.

[1592] Step 2:

[1593] The device launches the VR application and loads the training scenario received from the server.

[1594] Step 3:

[1595] When the user is ready, he presses the Start Session button.

[1596] Training run

[1597] Step 1:

[1598] The user interacts with the virtual pet in VR and gives commands (e.g., "sit").

[1599] Step 2:

[1600] The terminal recognizes the user's voice commands and transmits the instructions to the virtual pet.

[1601] Step 3:

[1602] The virtual pet reacts according to the algorithm of the generating AI and decides whether to follow the user's instructions.

[1603] Step 4:

[1604] Generative AI provides real-time feedback based on your training methods and your pet's reactions.

[1605] Step 5:

[1606] The emotion engine recognizes the user's emotions and feeds that data back to the generative AI, which then adjusts the feedback and advice it provides to take the user's emotions into account.

[1607] Step 6:

[1608] The emotion engine analyzes the emotional data and dynamically changes the training scenario according to the user's stress and satisfaction.

[1609] Feedback at the end of training

[1610] Step 1:

[1611] The user presses the end training session button.

[1612] Step 2:

[1613] The terminal notifies the server of the end of the session.

[1614] Step 3:

[1615] The server obtains analytical data from the generative AI and emotion engine and provides detailed feedback to the user.

[1616] Step 4:

[1617] Feedback includes suggestions for improvement and next training session.

[1618] Expert support

[1619] Step 1:

[1620] User clicks on the Support tab on the dashboard.

[1621] Step 2:

[1622] The terminal displays a chat window and the user types in a question.

[1623] Step 3:

[1624] The server forwards the question to an expert.

[1625] Step 4:

[1626] The expert enters the answer to the question and the server sends the answer to the user.

[1627] Step 5:

[1628] Users can review their answers and use them in future training sessions if necessary.

[1629] ---

[1630] The above is a description of the system program processing divided into specific steps.

[1631] Example 2

[1632] 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."

[1633] Conventional pet training systems require users to have specialized knowledge and experience to effectively train their pets, making them difficult for beginners to use. Furthermore, the effectiveness of training is limited because the system does not provide feedback that takes into account the user's emotions. Furthermore, only a limited number of systems offer support from experts.

[1634] 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.

[1635] In this invention, the server includes means for accepting user registration information, means for setting a user profile, means for interacting with and training a pet using a virtual reality environment, means for generating a training plan optimized for the user and the pet using generative artificial intelligence, means for recognizing voice commands and conveying instructions to the virtual pet, means for recognizing the user's emotions in real time using an emotion engine and dynamically adjusting feedback and training scenarios, and means for providing feedback to the user in real time. This allows even beginners to train effectively and provides feedback that takes the user's emotions into consideration. Furthermore, the quality of training is improved by easily receiving support from experts.

[1636] "Means for accepting user registration information" refers to the function by which a user inputs their own information into the system and the system records that information.

[1637] "Means for setting up a user profile" is a function that allows users to input detailed information about themselves and their pets, and the system then stores and manages that information.

[1638] "Means for interacting with and training a pet using a virtual reality environment" refers to a function that uses virtual reality technology to allow a user to interact with and train a virtual pet.

[1639] "Means for generating training plans optimized for users and their pets using generative artificial intelligence" refers to a function that utilizes artificial intelligence technology to automatically create training plans based on the characteristics of users and their pets.

[1640] The "means for recognizing voice commands and transmitting instructions to a virtual pet" is a function for recognizing the user's voice and transmitting those instructions to a virtual pet.

[1641] "Means of recognizing the user's emotions in real time using an emotion engine and dynamically adjusting feedback and training scenarios" is a function that analyzes the user's facial expressions and voice to determine their emotions and adapts feedback and training content based on that.

[1642] "Means for providing feedback to the user in real time" refers to a function that provides immediate evaluation and advice on the operations and reactions performed by the user during training.

[1643] The present invention is a system that combines a virtual reality environment and a pet training experience system that uses generative artificial intelligence to enable users to effectively train their pets with an emotion engine that recognizes the user's emotions. Specific embodiments of this system are described below.

[1644] User Registration and Login

[1645] When a user opens the service's website or app, the device displays a new registration screen. The user enters the required information, such as name, email address, and password, and presses the registration button. The server receives the entered information and saves it in the user database. After saving, the server sends the user a registration completion email. The user clicks the link in the received email to activate their account and enters their authentication information on the login screen. The server verifies the authentication information, and if login is successful, it instructs the device to display the dashboard.

[1646] User Profile Settings

[1647] When a user clicks on the "Profile Settings" tab on the dashboard, the terminal displays a form to input the pet's type, age, name, and characteristics. The user enters the pet's information and presses the save button. The server saves this information in the database and completes the profile settings.

[1648] Starting a training session

[1649] The user clicks the "Start a new training session" button on the dashboard. The server retrieves the user's profile data and sends it to the generation AI module. The generation AI generates an optimal training scenario based on the characteristics of the user and their pet. The server sends the generated scenario to the device and displays it to the user.

[1650] Setting up the VR environment

[1651] The user puts on the VR headset, the device launches the VR application, loads the training scenario received from the server, and when the user is ready, presses the start session button.

[1652] Training run

[1653] The user interacts with the virtual pet in VR and gives instructions. For example, if the user commands "sit," the device recognizes the voice command and sends the instruction to the virtual pet. The virtual pet responds according to the algorithm of the generation AI and determines whether to follow the user's instructions. The emotion engine recognizes the user's emotions and feeds that data back to the generation AI. The generation AI dynamically adjusts its feedback and advice taking the user's emotions into account.

[1654] Feedback at the end of training

[1655] When the user presses the end button on the device, the device notifies the server that the session is over. The server then receives analytical data from the generative AI and emotion engine and provides detailed feedback to the user, including suggestions for improvement and advice for the next training session.

[1656] Expert support

[1657] When a user clicks the "Support" tab on the dashboard, the device displays a chat window. The user enters a question, which the server forwards to an expert. The expert enters an answer to the question, and the server sends the answer to the user. The user reviews the answer and, if necessary, uses it for future training.

[1658] Hardware and software used

[1659] Hardware: PC, smartphone, VR headset, audio input device

[1660] Software: Web browsers, server software (e.g., Apache, NGINX), databases (e.g., MySQL, PostgreSQL), generative AI modules, emotion engines, VR applications, chat applications

[1661] Specific examples

[1662] For example, consider a novice pet owner training a new puppy. The user registers and logs in to the system. They enter the puppy's breed, age, and name on the profile settings screen. When the user clicks the "Start New Training Session" button on the dashboard, the server sends the puppy's profile information to the generation AI. The generation AI generates a training scenario optimized for the user's puppy, and the server sends it to the device. The user puts on the VR headset and starts the session. They give commands to the virtual pet, such as "sit," and the generation AI provides feedback in real time. The emotion engine recognizes the user's emotions and adjusts the feedback content based on them. After training is complete, the user receives feedback based on the analysis data from the generation AI and emotion engine, which is used for the next training session. If they have further questions, they can receive support from experts.

[1663] Example prompts to input to the generative AI model

[1664] "Generate a new puppy training scenario. Please suggest the best scenario based on the profile below.

[1665] Puppy breed: Labrador Retriever

[1666] Age: 6 months

[1667] Name: Lucky

[1668] Characteristics: Active and curious

[1669] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1670] Step 1: Enter user registration information and register

[1671] The user opens the service's website or app and enters the required information, such as name, email address, and password, on the new registration screen displayed on the device. The device acquires this information and sends it to the server. The server saves the entered information in the user database and sends the user a registration completion email.

[1672] Input: Name, Email Address, Password

[1673] Data processing: Validating input and saving to user database

[1674] Output: Sending a registration completion email

[1675] Specific operation: Enter and register the name "Yamada Taro", email address "taro@example.com", and password "password123"

[1676] Step 2: Activate your account and log in

[1677] The user activates their account by clicking the link in the email they received. When the user enters their credentials on the login screen, the device sends the information to the server, which verifies the credentials. If the login is successful, the server instructs the device to display the dashboard.

[1678] Input: Authentication information (email address, password)

[1679] Data processing: Authentication information verification

[1680] Output: Display dashboard

[1681] Specific behavior: Activate the account via the link in the email and log in with "taro@example.com" and "password123"

[1682] Step 3: Configure User Profile

[1683] When a user clicks on the "Profile Settings" tab on the dashboard, a form appears on the device asking for the pet's type, age, name, and characteristics. The user enters this information and presses the save button. The device sends the data to the server, which stores it in a database.

[1684] Input: Pet type, age, name, characteristics

[1685] Data processing: Saving input data

[1686] Output: Notification of save completion

[1687] Specific behavior: Enter and save pet information (Labrador retriever, 6 months old, lucky, active and curious)

[1688] Step 4: Generate training scenarios

[1689] When a user clicks the "Start a new training session" button on the dashboard, the server retrieves the user's profile data and sends it to the generation AI module. The generation AI module generates an optimal training scenario based on the profile data and returns it to the server. The server then sends the generated scenario to the device and displays it to the user.

[1690] Input: User profile data

[1691] Data processing: Generative AI generates training scenarios

[1692] Output: Display of training scenario

[1693] Specific behavior: Generate and display the optimal training scenario for "lucky"

[1694] Step 5: Setting up the VR environment

[1695] The user puts on the VR headset, the device launches the VR application, loads the training scenario received from the server, and the user presses the "Start Session" button.

[1696] Input: Training scenario

[1697] Data processing: Loading the training scenario

[1698] Output: VR environment setup complete

[1699] Specific actions: Putting on a VR headset and launching the app

[1700] Step 6: Training Run

[1701] The user issues commands such as "sit" to a virtual pet in VR. The device recognizes the voice command and transmits the command to the virtual pet. The virtual pet responds to the command according to the algorithm of the generating AI. The emotion engine recognizes the user's emotions in real time and feeds that data back to the generating AI. The generating AI dynamically adjusts its feedback and advice taking the user's emotions into account.

[1702] Input: User voice commands, emotion data

[1703] Data processing: speech recognition, emotion recognition, dynamic feedback adjustment

[1704] Output: Virtual pet behavior, real-time feedback

[1705] Specific actions: "Sit" command and virtual pet's response, "Good job" feedback

[1706] Step 7: End-of-training feedback

[1707] When the user presses the end button on the device, the device notifies the server that the session is over. The server then combines analytical data from the generative AI and emotion engine to provide detailed feedback to the user, including suggestions for improvement and the next training session.

[1708] Input: Analysis data for generative AI and emotion engine

[1709] Data processing: Integration of analytical data

[1710] Output: Detailed feedback

[1711] Specific behaviors: Providing advice such as, "Next time, let's continue practicing sitting."

[1712] Step 8: Expert help

[1713] When a user clicks the "Support" tab on the dashboard, a chat window appears on the device. The user enters a question, the device sends it to the server, and the server forwards it to an expert. The expert enters an answer to the question, and the server sends the answer to the user. The user can then review the answer and use it for their next training session.

[1714] Input: User question

[1715] Data processing: forwarding questions and sending answers

[1716] Output: Expert Support

[1717] Specific behavior: Asking questions such as "What should I do when my puppy barks?" and receiving answers

[1718] (Application example 2)

[1719] 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."

[1720] Conventional pet training systems lacked sufficient technical means for users to effectively train their pets. Furthermore, conventional factory automation systems lacked the ability to recognize workers' stress and emotions in real time and generate feedback. As a result, work efficiency declined and worker stress management was inadequate. The present invention aims to solve these problems.

[1721] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for accepting user registration information, means for setting a user profile, means for interacting with and training a pet using a virtual reality environment, means for generating a training plan optimized for the user and the pet using generative artificial intelligence, means for providing feedback to the user in real time, means for recognizing the user's emotions in real time using an emotion engine and dynamically adjusting the feedback and training scenario, and means for monitoring automated processes in a factory, evaluating worker efficiency and stress based on emotion recognition, and generating feedback. This allows users to effectively train their pets, and makes it possible to improve work efficiency and manage worker stress in automated processes in a factory.

[1722] "Means for accepting user registration information" refers to the process by which a user enters and registers the necessary information into the system.

[1723] "Means for setting up a user profile" is the process by which a user enters details about themselves and their pets into the system to create a personalized profile.

[1724] "Means for interacting with and training a pet using a virtual reality environment" refers to a process in which a user interacts with and trains a virtual pet using virtual reality technology.

[1725] "Means for generating training plans optimized for users and pets using generative artificial intelligence" refers to a process that uses a generative artificial intelligence algorithm to create an optimal training plan based on the characteristics of the user and pet.

[1726] "Means for providing feedback to the user in real time" refers to a process that provides instantaneous and appropriate advice and evaluation based on the user's actions and the pet's reactions.

[1727] "Means for recognizing user emotions in real time using an emotion engine and dynamically adjusting feedback and training scenarios" is a process for detecting a user's emotions and instantly changing training content and feedback based on that information.

[1728] "Means for monitoring automated processes in factories and evaluating worker efficiency and stress based on emotion recognition and generating feedback" refers to a process that works in conjunction with the work automation systems in factories, monitors the emotional state of workers, evaluates their work efficiency and stress levels, and provides feedback.

[1729] The present invention is a pet training experience system that uses a virtual reality environment and generative artificial intelligence to help users effectively train their pets, combined with an emotion engine that recognizes user emotions. This system can also monitor the emotions of workers in a factory environment to improve the efficiency of automated processes.

[1730] Program Overview

[1731] The program of this system includes the following processing steps.

[1732] 1. User Registration and Login

[1733] When a user inputs registration information from a terminal, the server stores the information in a user database.

[1734] 2. Setting up your user profile

[1735] The user provides details about the pet's breed, age, name and characteristics, and the server stores this information in a database.

[1736] 3. Start your training session

[1737] The server uses a generative AI module based on the user's profile data to create an optimal training plan.

[1738] 4. Setting up the VR environment

[1739] The user puts on a VR headset, and the server sends the generated training scenario to the device.

[1740] 5. Training execution

[1741] Users interact with their pets in a virtual environment, an emotion engine recognizes the user's emotions in real time, and generative AI provides corresponding feedback.

[1742] 6. Feedback and Support

[1743] After the training is completed, the server provides detailed feedback to the user based on the analysis data, and the user can receive support from experts.

[1744] This system is also effective in a factory environment, specifically for the following processes:

[1745] 1. Monitoring operations within the factory

[1746] The camera captures the worker's movements in real time and transmits them to a server.

[1747] 2. Emotion recognition

[1748] The emotion engine recognizes the worker's emotions in real time from the received video stream.

[1749] 3. Feedback Generation

[1750] The server uses a generative AI model to generate feedback for work efficiency and stress management based on emotional data.

[1751] The hardware used is a video streaming camera, a VR headset, and an automated robot in a factory. The software used is an emotion recognition model based on TensorFlow, a face recognition algorithm using OpenCV, and an HTTP-based communication module.

[1752] Specific examples

[1753] For example, a novice pet owner might follow these steps to train a new puppy:

[1754] 1. The pet owner registers and logs in to the system. On the profile setting screen, they enter the breed, age, and name of the puppy.

[1755] 2. The server requests a training scenario from the generation AI based on the user's profile information.

[1756] 3. The pet owner puts on a VR headset and commands their puppy in the virtual environment, such as "sit." The generative AI provides real-time feedback, and the emotion engine recognizes and adjusts the user's emotions.

[1757] 4. After the training is completed, the server provides detailed feedback based on the analysis data to help improve the next training session.

[1758] An example of a prompt is, "The worker is currently feeling ____ (e.g., stress, anxiety, satisfaction). Please provide suggestions for improving the work based on this emotion."

[1759] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1760] Step 1:

[1761] The user enters registration information from the device. Specifically, the device accepts necessary information such as name, email address, and password through a user interface. The entered information is sent to the server, which then stores it in a user database.

[1762] Step 2:

[1763] The user sets up a profile. The device displays a screen where the user can enter details about the pet's breed, age, name, and characteristics. Once the user enters this information and presses the save button, the information is sent to the server and stored in a database.

[1764] Step 3:

[1765] The user starts a training session. When the user clicks the "Start New Training Session" button on the device, the server obtains the user's profile data and sends it to the generation AI module. The generation AI module generates an optimal training plan based on the characteristics of the user and their pet, and returns the scenario to the server. The server then sends this scenario to the device.

[1766] Step 4:

[1767] The user wears a VR headset and performs a training scenario in the virtual reality environment. The server sends the training scenario to the device, which then launches the VR application and displays the training scenario to the user.

[1768] Step 5:

[1769] The user interacts with the virtual pet in VR (e.g., commanding it to "sit"). The device recognizes the voice command and sends the information to the server. The server then sends the information to the generation AI, which generates real-time feedback based on the command. The device then displays the feedback to the user.

[1770] Step 6:

[1771] The emotion engine recognizes the user's emotions in real time. The device extracts emotional information from the user's facial expressions and voice and sends it to the server. The server uses the emotion engine to analyze the user's emotions and feeds that data back to the generation AI. The generation AI dynamically adjusts feedback and training scenarios based on this information.

[1772] Step 7:

[1773] After the training session is over, the server generates detailed feedback based on the analysis data from the generation AI and emotion engine. The device displays this feedback to the user, including suggestions for improving the training and the next training session.

[1774] Step 8:

[1775] The user receives support from an expert. When the user clicks the "Support" tab on the device, a chat window appears. The device sends the user's question to the server, which then forwards it to the expert. The expert enters an answer, which is then sent to the user via the server. The user then checks the answer and uses it for their next training session.

[1776] Step 9:

[1777] This system monitors automated processes in factories. Cameras capture the activity in the factory in real time and send the video stream to a server. The server uses an emotion engine to recognize the emotions of workers, and a generative AI generates feedback based on that information to improve work efficiency and manage stress. The feedback is displayed on a management terminal in the factory.

[1778] 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.

[1779] 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.

[1780] 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.

[1781] [Fourth embodiment]

[1782] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1783] 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.

[1784] 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).

[1785] 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.

[1786] 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.

[1787] 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).

[1788] 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.

[1789] 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.

[1790] 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.

[1791] 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.

[1792] 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.

[1793] 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.

[1794] 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."

[1795] The present invention provides a pet training experience system that uses a virtual reality environment and generative artificial intelligence to enable users to effectively train their pets. Specific embodiments of this system are described below.

[1796] System Overview

[1797] After registering and setting up a profile, the system allows users to train with a virtual pet in a virtual reality (VR) environment, where generative artificial intelligence (AI) provides real-time feedback to improve the user's training skills. After completing the training, users can receive detailed feedback and expert support.

[1798] Program processing

[1799] 1. User Registration and Login

[1800] When a user opens the service's website or app, the device displays a new registration screen. The user enters the required information, such as name, email address, and password, and presses the registration button.

[1801] The server receives the entered information and saves it in the user database. After saving, the server sends the user a registration completion email.

[1802] The user activates their account by clicking the link in the email and enters their credentials on the login screen. The server verifies the credentials and, if the login is successful, instructs the device to display the dashboard.

[1803] 2. Setting up your user profile

[1804] When a user clicks on the "Profile Settings" tab on the dashboard, the device displays a form where they can enter their pet's type, age, name, and characteristics.

[1805] The user enters the pet's information and presses the save button, and the server saves this information to the database, completing the profile setup.

[1806] 3. Start your training session

[1807] The user clicks the "Start a new training session" button on the dashboard. The server retrieves the user's profile data and sends it to the generation AI module.

[1808] The AI ​​generates the optimal training scenario based on the characteristics of the user and their pet. The server sends the generated training scenario to the device and displays it to the user.

[1809] 4. Setting up the VR environment

[1810] The user puts on the VR headset, launches the VR application on the device, loads the training scenario received from the server, and presses the start session button when the user is ready.

[1811] 5. Training execution

[1812] The user interacts with the virtual pet in VR and gives instructions. For example, if the user gives the instruction "sit," the device recognizes the voice command and sends the instruction to the virtual pet.

[1813] The virtual pet reacts according to the algorithms of the generative AI, which determines whether to follow the user's commands, and provides real-time feedback based on the user's training methods and the pet's responses.

[1814] The server monitors the training progress and updates the data as needed.

[1815] 6. Feedback at the end of training

[1816] When the user presses the end training session button, the terminal notifies the server that the session is over.

[1817] The server retrieves the analytical data from the generated AI and provides detailed feedback to the user, including suggestions for improvement and the next training session.

[1818] 7. Expert Support

[1819] When a user clicks on the "Support" tab in the dashboard, the device displays a chat window where the user can type in their question and the server will forward it to an expert.

[1820] The expert enters the answer to the question, and the server sends the answer to the user, who can then check the answer and use it for the next training session if necessary.

[1821] Specific examples

[1822] For example, consider the case of a novice pet owner, Mr. A, training a new puppy.

[1823] 1. Person A registers with the system and logs in. On the profile settings screen, he enters the breed, age, and name of the puppy.

[1824] 2. Person A clicks the "Start a new training session" button on the dashboard. The server sends the puppy's profile information to the generation AI.

[1825] 3. The generation AI generates a training scenario optimized for Mr. A's puppy, and the server sends it to the device.

[1826] 4. Person A puts on the VR headset and starts the session. He commands the virtual pet, such as "sit," and the generating AI provides real-time feedback.

[1827] 5. After the training is completed, feedback based on the analysis data from the generative AI is received and used for the next training.

[1828] 6. If you have further questions, you can get help from experts.

[1829] Through the above process, the system helps novice pet owners and trainers to effectively train and deepen their relationships with their pets.

[1830] The processing flow will be explained below.

[1831] ---

[1832] User Registration and Login

[1833] Step 1:

[1834] The user opens the service's website or app, and the device displays the new registration screen.

[1835] Step 2:

[1836] The user enters the required information such as name, email address, and password, and presses the registration button.

[1837] Step 3:

[1838] The server receives the entered information and stores it in a user database.

[1839] Step 4:

[1840] After the server saves the data, it generates a registration completion email and sends it to the user.

[1841] Step 5:

[1842] The user clicks the link in the email they received to activate their account and enters their email address and password on the login screen.

[1843] Step 6:

[1844] The server checks the entered authentication information and, if authentication is successful, instructs the device to display the dashboard.

[1845] User Profile Settings

[1846] Step 1:

[1847] The user clicks the "Profile Settings" tab on the dashboard. The device displays a form to enter the pet's type, age, name, and characteristics.

[1848] Step 2:

[1849] The user enters the pet's information and presses the save button.

[1850] Step 3:

[1851] The server stores the information you entered in a database and completes your profile setup.

[1852] Starting a training session

[1853] Step 1:

[1854] A user clicks the "Start a New Training Session" button on the dashboard.

[1855] Step 2:

[1856] The server retrieves the user's profile data and sends it to the generation AI module.

[1857] Step 3:

[1858] Generative AI generates optimal training scenarios based on the characteristics of the user and their pet.

[1859] Step 4:

[1860] The server transmits the generated training scenario to the terminal and displays it to the user.

[1861] Setting up the VR environment

[1862] Step 1:

[1863] The user puts on the VR headset.

[1864] Step 2:

[1865] The device launches the VR application and loads the training scenario received from the server.

[1866] Step 3:

[1867] When the user is ready, he presses the Start Session button.

[1868] Training run

[1869] Step 1:

[1870] The user interacts with the virtual pet in VR and gives commands (e.g., "sit").

[1871] Step 2:

[1872] The terminal recognizes the user's voice commands and transmits the instructions to the virtual pet.

[1873] Step 3:

[1874] The virtual pet reacts according to the algorithm of the generating AI and decides whether to follow the user's instructions.

[1875] Step 4:

[1876] Generative AI provides real-time feedback based on your training methods and your pet's reactions.

[1877] Step 5:

[1878] The server monitors the training progress and updates the data as needed.

[1879] Feedback at the end of training

[1880] Step 1:

[1881] The user presses the end training session button.

[1882] Step 2:

[1883] The terminal notifies the server of the end of the session.

[1884] Step 3:

[1885] The server obtains analytical data from the generated AI and provides detailed feedback to the user.

[1886] Step 4:

[1887] Feedback includes suggestions for improvement and next training session.

[1888] Expert support

[1889] Step 1:

[1890] User clicks on the Support tab on the dashboard.

[1891] Step 2:

[1892] The terminal displays a chat window and the user types in a question.

[1893] Step 3:

[1894] The server forwards the question to an expert.

[1895] Step 4:

[1896] The expert enters the answer to the question and the server sends the answer to the user.

[1897] Step 5:

[1898] Users can review their answers and use them in future training sessions if necessary.

[1899] ---

[1900] The above is a description of the system program processing divided into specific steps.

[1901] Example 1

[1902] 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."

[1903] Currently, many pet owners struggle to find effective pet training methods. Learning the correct training methods can be particularly difficult for novice pet owners, potentially hindering efforts to improve their pet's behavior and build a relationship with them. Furthermore, actual training requires time and effort, and it is difficult to obtain immediate feedback, making it difficult to maximize the effectiveness of training. Furthermore, with little access to expert support, owners are unable to obtain appropriate advice for problem-solving. To address these challenges, a system is needed that allows users to effectively train their pets while learning on their own.

[1904] 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.

[1905] In this invention, the server includes means for accepting user registration information, means for setting a user profile, means for interacting with and training an animal using a virtual reality environment, means for generating a training plan optimized for the user and the animal using generative artificial intelligence, means for providing feedback to the user in real time, means for generating a training scenario by inputting prompts into the generative AI model, means for recognizing user instructions using voice recognition technology and transmitting them to the virtual animal, and means for receiving support from experts. This allows users to effectively train their pets while learning on their own. Furthermore, the ability to receive real-time feedback and expert support improves training effectiveness and deepens the relationship with their pet.

[1906] "User registration information" refers to basic information such as name, email address, and password provided by the user in order to use the service.

[1907] A "user profile" is a data set containing detailed information about a user and their pets, such as the pet's type, age, name, characteristics, etc.

[1908] A "virtual reality environment" is a three-dimensional space generated using computer technology, in which users experience and operate in a virtual space that is different from the real world.

[1909] "Animals" are living creatures such as dogs and cats kept as pets.

[1910] "Generative artificial intelligence" refers to algorithms that have the ability to autonomously learn and perform specific tasks based on input data.

[1911] A "training plan" is a plan that shows the specific training procedures and methods to be used on the user's pet, and is optimized by the generation AI.

[1912] "Feedback" refers to the advice and evaluation provided by the generative AI in real time during training.

[1913] A "generative AI model" is an algorithm that learns from large amounts of data and generates appropriate outputs for new inputs.

[1914] A "prompt sentence" is a sentence that is input to give instructions or ask a question to a generative AI model.

[1915] "Speech recognition technology" is a technology that analyzes the words spoken by a user and converts them into text data.

[1916] "Communication means" refers to electronic communication means used by users to receive support from experts, including web chat and email.

[1917] The present invention is a system that enables users to effectively train their pets. This system provides a consistent service, from accepting users' registration information, to training with their pets, and providing feedback and expert support after training is complete. An embodiment of the present invention will be described in detail below.

[1918] Hardware and Software Configuration

[1919] This system operates by combining multiple hardware and software components. The main hardware components include the user access terminals (e.g., PCs and smartphones), the VR headset (e.g., Oculus Quest 2), and the server. The main software components include the web application, the VR application, and the algorithms that run the generative AI model.

[1920] Processing flow

[1921] When a user opens the service's website or app, the device displays a registration screen. The user enters the required information, such as name, email address, and password, and presses the Register button. The server receives the information and stores it in a secure format in the user database. The server then sends a registration completion email, and the user clicks the link in the email to activate their account. If the user enters authentication information on the login screen, the server verifies the authentication information, and if the login is successful, the device displays a dashboard.

[1922] When a user clicks on the "Profile Settings" tab on the dashboard, the device displays a form for entering the pet's type, age, name, and characteristics. Once the user enters the information and presses the save button, the server saves the information to a database and completes the profile settings.

[1923] Next, when the user clicks the "Start a new training session" button on the dashboard, the server sends the user's profile data to the generation AI module. The generation AI generates an optimal training scenario based on the characteristics of the user and their pet, and the scenario is sent from the server to the device. The user then puts on a VR headset and launches a dedicated VR application. The training scenario received from the server is loaded there, and the user presses the start session button when ready.

[1924] During a training session, the user interacts with the virtual pet in VR, issuing voice commands (e.g., "sit"). The device uses voice recognition technology to interpret the commands and transmit them to the virtual pet. Generative AI controls the virtual pet's movements and provides real-time feedback to the user. At the same time, the server monitors the training progress and updates the data as needed.

[1925] After a training session, the user presses the end button, and the device notifies the server that the session has ended. The server then retrieves analytical data from the generated AI and provides detailed feedback to the user, including specific advice on areas for improvement and the next training session. Furthermore, when the user clicks the "Support" tab on the dashboard, the device displays a chat window where the user can enter a question. The question is then forwarded to an expert via the server, who then provides the user with an answer.

[1926] Specific examples

[1927] For example, when a novice pet owner trains a new puppy, the following steps are taken: First, the user registers and logs in. Next, they enter the puppy's information on the profile settings screen. When the user clicks the "Start New Training Session" button, the server sends the puppy's profile information to the generation AI, which then generates an optimal training scenario. This scenario is sent to the device, and the user puts on a VR headset to start the session. During the training session, the user issues voice commands to the virtual pet and receives real-time feedback based on its responses. After the session ends, the user receives detailed feedback based on analytical data, which can be used for the next training session. The user can also ask experts any questions and receive appropriate advice.

[1928] In a specific example, when a user inputs a prompt sentence into a generative AI model to generate a training scenario, an example of the prompt sentence is, "Please create a basic obedience training scenario for a 5-year-old Labrador Retriever."

[1929] This invention allows users to effectively train their pets and deepen their relationships with them. Real-time feedback and expert support improve training effectiveness and are expected to improve pet behavior.

[1930] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1931] Step 1:

[1932] The user opens the service's website or app.

[1933] Input: A user action to open a website or app.

[1934] Output: The terminal displays the new registration screen.

[1935] What happens: The device's browser loads a web page and displays a sign-up form on the screen, where the user enters information such as their name, email address, and password.

[1936] Step 2:

[1937] The user enters the required information and presses the registration button.

[1938] Input: The user enters their name, email address, password, etc. and clicks the Register button.

[1939] Output: Information is sent to the server.

[1940] What it does: The device collects the information entered and sends it to the server using the HTTPS protocol. The server receives it, encrypts it, and stores it in a database.

[1941] Step 3:

[1942] The server sends a registration completion email.

[1943] Input: User's email address and registration information.

[1944] Output: A registration completion email will be sent to the user's email address.

[1945] Specific operation: The server uses the email sending API to send a registration confirmation email to the email address specified by the user, which includes a link to activate the account.

[1946] Step 4:

[1947] The user clicks the link in the email to activate their account.

[1948] Input: A user clicks on a link in an email.

[1949] Output: The account is activated and the user is redirected to the login screen.

[1950] What happens: When the user clicks the link, the server updates the account status to be activated, and then the user is taken to the login screen.

[1951] Step 5:

[1952] The user enters their credentials on the login screen.

[1953] Input: The user enters their email address and password and clicks the login button.

[1954] Output: The server verifies the credentials and if the login is successful, displays the dashboard.

[1955] Specific operation: The device sends the entered authentication information to the server, which checks it against the database. If it matches, authentication is successful and the user's dashboard screen is displayed.

[1956] Step 6:

[1957] The user clicks the Profile Settings tab on the dashboard.

[1958] Input: User clicks on the "Profile Settings" tab.

[1959] Output: The device displays the profile setup form.

[1960] What happens: The device loads the form associated with the "Profile Settings" tab and displays it on the screen.

[1961] Step 7:

[1962] The user enters the pet's type, age, name, and characteristics and presses the save button.

[1963] Input: The user enters pet information and presses the save button.

[1964] Output: The information is sent to the server and stored in a database.

[1965] Specific operation: The terminal sends the entered pet information to the server as an HTTP request, and the server receives the information and stores it in a database.

[1966] Step 8:

[1967] User clicks the "Start New Training Session" button.

[1968] Input: User clicks the "Start New Training Session" button.

[1969] Output: The server sends the user profile data to the generation AI module.

[1970] Specific operation: The server collects the user's profile data and sends it to the generation AI module, which then inputs a prompt to generate the optimal training scenario.

[1971] Step 9:

[1972] Generative AI generates optimal training scenarios.

[1973] Input: Profile data based on user and pet characteristics and a prompt.

[1974] Output: The generated training scenario.

[1975] How it works: The generation AI analyzes the profile data and prompts, and uses the learning model to generate optimal training scenarios. The generated scenario data is then sent to the server.

[1976] Step 10:

[1977] The server transmits the generated training scenario to the terminal.

[1978] Input: Training scenario data sent from the generative AI.

[1979] Output: The terminal displays the training scenario to the user.

[1980] Specific operation: The server sends training scenario data to the terminal, which then renders it into a format that is displayed to the user.

[1981] Step 11:

[1982] The user puts on the VR headset and starts the session.

[1983] Input: The user puts on a VR headset and launches an application.

[1984] Output: A training session begins in the VR environment.

[1985] Specific operation: The user puts on the VR headset and launches the dedicated application. The application loads the training scenario received from the server.

[1986] Step 12:

[1987] The user issues voice commands to the virtual pet.

[1988] Input: The user's voice command (e.g., "sit").

[1989] Output: The virtual pet responds to voice commands.

[1990] Specific operation: The device uses voice recognition technology to analyze the user's voice commands and transmit them to the virtual pet. The generated AI controls the pet's movements based on those instructions.

[1991] Step 13:

[1992] Generative AI provides real-time feedback.

[1993] Input: User behavior and pet reaction data.

[1994] Output: Real-time feedback to the user.

[1995] Specific operation: The generative AI analyzes the collected data and provides appropriate feedback to the user via screen or voice.

[1996] Step 14:

[1997] The server monitors the training progress and updates the data as needed.

[1998] Input: The data being trained.

[1999] Output: Updated progress data.

[2000] What it does: The server monitors training session data in real time and updates the information in the database as needed.

[2001] Step 15:

[2002] The user presses the end training session button.

[2003] Input: The user presses the end session button.

[2004] Output: A session termination notification is sent to the server.

[2005] Specific operation: The terminal sends a session termination request to the server, and the server terminates the session.

[2006] Step 16:

[2007] The server obtains analytical data from the generated AI and provides detailed feedback to the user.

[2008] Input: Analysis data from the generative AI.

[2009] Output: Detailed feedback to the user.

[2010] Specific behavior: The server obtains analytical data from the generated AI and uses it to provide detailed feedback to the user, including specific advice on areas for improvement and the next training session.

[2011] Step 17:

[2012] User clicks on the Support tab on the dashboard.

[2013] Input: User clicks on the Support tab.

[2014] Output: The terminal displays a chat window.

[2015] Specific behavior: The device displays a support chat window, allowing the user to enter questions.

[2016] Step 18:

[2017] The user enters a question and the server forwards it to an expert.

[2018] Input: The user's question.

[2019] Output: The question is forwarded to an expert.

[2020] Specific operation: The device receives the user's question and sends it to the server, which then forwards the question to an expert.

[2021] Step 19:

[2022] The expert enters the answer and the server sends the answer to the user.

[2023] Input: Expert answers.

[2024] Output: The answer to the user.

[2025] Specific operation: The expert enters the answer, and the server sends the answer to the user's device. The user can check the answer and incorporate it into the next training if necessary.

[2026] (Application example 1)

[2027] 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."

[2028] Modern factories require new workers to quickly and effectively master advanced robot operation and maintenance. However, operating actual robots is expensive and poses a high risk of operational errors, making it difficult to provide adequate training using traditional educational methods. Furthermore, there are limited means for workers to learn while receiving real-time feedback. To address this challenge, an efficient training system using virtual reality environments and generative artificial intelligence is needed.

[2029] 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.

[2030] In this invention, the server includes means for accepting user registration information, means for setting a user profile, means for interacting with and educating a training subject using a virtual reality environment, means for generating an instruction plan optimized for the user and the training subject using generative artificial intelligence, means for providing feedback to the user in real time, and means for executing a specific training scenario using a virtual reality device, thereby enabling a user to safely and effectively acquire robot operation and maintenance skills in the virtual reality environment.

[2031] "Means for accepting user registration information" refers to a mechanism for inputting and saving basic user information (such as name, email address, and password).

[2032] The "means for setting a user profile" is a mechanism by which a user sets and saves details of the training subject (robot model, experience level, etc.).

[2033] "Means for interacting with and educating training subjects using a virtual reality environment" refers to a system in which a user interacts with a training subject (robot) in a virtual environment using a VR device, and receives training in operation and maintenance.

[2034] "Means for generating an educational plan optimized for the user and training subject using generative artificial intelligence" refers to AI technology for generating optimal training scenarios based on profile information of the user and training subject.

[2035] "Means of providing feedback to users in real time" refers to a system in which AI provides on-the-spot improvements and advice based on the user's operations and instructions during training.

[2036] "Means for executing a specific training scenario using a virtual reality device" refers to a mechanism for executing a generated training scenario in a virtual environment using a VR headset or related device.

[2037] MODE FOR CARRYING OUT THE INVENTION

[2038] System Overview

[2039] This invention is an educational system that enables factory workers to effectively learn robot operation and maintenance in a virtual reality (VR) environment. It includes user registration, profile setup, training in the VR environment, real-time feedback provided by generative artificial intelligence (AI), feedback after training, and expert support.

[2040] System Configuration

[2041] The system uses the following hardware and software:

[2042] Hardware: VR headset (e.g. Oculus Quest 2), computer, network connection equipment

[2043] Software: VR framework, generative AI module, server application (e.g., Flask, Django)

[2044] 1. User Registration and Login

[2045] The server first provides a means to accept user registration information. The user registers with the system and enters basic information (name, email address, password, etc.). The entered information is saved on the server, and the user is sent a registration completion email. The user then clicks the link in the email to activate their account and enters their login information to access the system.

[2046] 2. Setting up your user profile

[2047] After registration, the user uses the profile setting means to enter details of the robot they wish to train (robot model, experience level, etc.) The server receives this information and stores it in a database.

[2048] 3. Start your training session

[2049] When a user clicks the "Start a new training session" button on the dashboard, the server retrieves the user's profile data and sends it to the Generative AI module, which then generates an optimal training plan for the user and training subject and sends it to the user via the server.

[2050] 4. Setting up the VR environment

[2051] The user puts on a VR headset and executes a specific training scenario, and the server uses the VR framework to load the scenario. Once the user is ready, the training session begins.

[2052] 5. Training execution and real-time feedback

[2053] In the virtual reality environment, the user issues instructions to the robot, and the generative AI provides real-time feedback based on these instructions. For example, if the user issues an instruction such as "set welding points," the AI ​​will provide the execution results and appropriate feedback in real time.

[2054] 6. Post-training feedback

[2055] When the user presses the end button for the training session, the server records the end of the session and provides detailed feedback based on the analysis data from the generative AI, including an evaluation of the user's actions and suggestions for improvement next time.

[2056] 7. Expert Support

[2057] If the user needs additional support, the server provides a means of communication to receive support from experts. The user can enter a question, and the expert will provide an answer, which the user can use to help with their next training.

[2058] Examples of concrete examples and prompts

[2059] As a concrete example, consider a scenario in which new factory worker B is learning to operate a new welding robot. After completing registration and profile setup, B issues the command "set welding point" in the VR environment. The generating AI provides real-time feedback based on this command. After completing the training, B receives detailed feedback from the generating AI, which can be used to improve the next training session.

[2060] Examples of prompts include:

[2061] "Create a new training scenario. The target robot is 'Weld-2000' and the operation skill level is 'Beginner'. Create a scenario that includes the following elements: 1. Safety check 2. Basic operation 3. Weld point setting 4. Real-time feedback"

[2062] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2063] Step 1:

[2064] User Registration and Login

[2065] The server receives the user's name, email address, and password and stores them in a database. It processes the information entered by the user and automatically generates and sends a confirmation email. If the user clicks on the link in the email to activate their account, the server verifies the authentication information and allows them to log in.

[2066] Input: Name, Email Address, Password

[2067] Output: Sending a registration completion email, saving authentication information

[2068] What happens: A user enters their name, email address, and password into a web form and presses the submit button. The server receives this information, stores it in a database, and sends a confirmation email to the user.

[2069] Step 2:

[2070] User Profile Settings

[2071] The server receives information such as the robot model and experience level input by the user through the user profile setting means, and stores this profile information in a database.

[2072] Input: Robot model, experience level

[2073] Output: Save profile information

[2074] Specific operation: The user selects the robot model "Weld-2000" and the experience level "Beginner" on the dashboard and presses the save button. The server receives this and saves it in the database.

[2075] Step 3:

[2076] Starting a training session

[2077] When a user clicks the "Start New Training Session" button, the server sends the user's profile data to the Generative AI module, which then generates an optimal training scenario and sends it to the user.

[2078] Input: User profile data

[2079] Output: Optimal training scenario

[2080] Specific operation: The server obtains the user's profile information and sends it to the generation AI. The generation AI generates a "Weld-2000 training scenario for beginners," and the server sends it to the user's device.

[2081] Step 4:

[2082] Setting up the VR environment

[2083] The user puts on the VR headset, the server loads the training scenario through the VR framework, and when the user is ready, they press the "Start Session" button.

[2084] Input: Training scenario

[2085] Output: Loaded VR training environment

[2086] Specific operation: The user starts the VR headset, the server provides guidance to load the received training scenario into the VR framework, and the user presses the start session button.

[2087] Step 5:

[2088] Training execution and real-time feedback

[2089] When a user issues an instruction to the robot in VR, the AI ​​analyzes it and provides real-time feedback. For example, it determines whether an instruction such as "set welding points" is accurate and provides on-the-spot advice.

[2090] Input: User instructions

[2091] Output: Real-time feedback

[2092] Specific operation: The user gives instructions in VR such as "Set welding points," and the generated AI analyzes them. For example, if the welding points are appropriate, it will return feedback such as "This is the correct position."

[2093] Step 6:

[2094] Post-training feedback

[2095] When the user presses the end button for the training session, the server records the end of the session and provides detailed feedback based on the analysis data from the generated AI, including an overall evaluation and suggestions for improvement next time.

[2096] Input: Training session data

[2097] Output: Detailed feedback

[2098] How it works: When a user presses the end session button, the server collects session data, and the AI ​​analyzes it to create detailed feedback. Improvements and next steps are displayed on the user's device.

[2099] Step 7:

[2100] Expert support

[2101] If the user needs further assistance, the server provides a means of communication to forward the user's question to an expert, and receives the expert's answer and sends it to the user.

[2102] Input: User question

[2103] Output: Expert Answers

[2104] How it works: A user clicks on the "Support" tab on the dashboard and enters a question. The server forwards it to an expert, who then enters an answer and sends it back to the server. Finally, the answer is displayed to the user.

[2105] 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.

[2106] The present invention is a system that combines a virtual reality environment and a pet training experience system that uses generative artificial intelligence to enable users to effectively train their pets with an emotion engine that recognizes the user's emotions. Specific embodiments of this system are described below.

[2107] System Overview

[2108] After users register and set up their profile, the system allows them to train with a virtual pet in a virtual reality (VR) environment. Generative artificial intelligence (AI) provides real-time feedback to improve the user's training skills. Furthermore, it uses an emotion engine that recognizes the user's emotions in real time and dynamically adjusts the feedback and training scenarios. After completing training, users can receive detailed feedback and expert support.

[2109] Program processing

[2110] 1. User Registration and Login

[2111] When a user opens the service's website or app, the device displays a new registration screen. The user enters the required information, such as name, email address, and password, and presses the registration button.

[2112] The server receives the entered information and saves it in the user database. After saving, the server sends the user a registration completion email.

[2113] The user activates their account by clicking the link in the email and enters their credentials on the login screen. The server verifies the credentials and, if the login is successful, instructs the device to display the dashboard.

[2114] 2. Setting up your user profile

[2115] When a user clicks on the "Profile Settings" tab on the dashboard, the device displays a form where they can enter their pet's type, age, name, and characteristics.

[2116] The user enters the pet's information and presses the save button, and the server saves this information to the database, completing the profile setup.

[2117] 3. Start your training session

[2118] The user clicks the "Start a new training session" button on the dashboard. The server retrieves the user's profile data and sends it to the generation AI module.

[2119] The AI ​​generates the optimal training scenario based on the characteristics of the user and their pet. The server sends the generated scenario to the device and displays it to the user.

[2120] 4. Setting up the VR environment

[2121] The user puts on the VR headset, launches the VR application on the device, loads the training scenario received from the server, and presses the start session button when the user is ready.

[2122] 5. Training execution

[2123] The user interacts with the virtual pet in VR and gives instructions. For example, if the user commands "sit," the device recognizes the voice command and sends the instruction to the virtual pet.

[2124] The virtual pet reacts according to the algorithms of the generative AI, which determines whether to follow the user's commands, and provides real-time feedback based on the user's training methods and the pet's responses.

[2125] The emotion engine recognizes the user's emotions and feeds that data back to the generative AI, which then adjusts the feedback and advice it provides to take the user's emotions into account.

[2126] The emotion engine analyzes the emotional data and dynamically changes the training scenario according to the user's stress and satisfaction.

[2127] 6. Feedback at the end of training

[2128] When the user presses the end training session button, the terminal notifies the server that the session is over.

[2129] The server retrieves analytical data from the generative AI and emotion engine and provides detailed feedback to the user, including suggestions for improvement and the next training session.

[2130] 7. Expert Support

[2131] When a user clicks on the "Support" tab in the dashboard, the device displays a chat window where the user can type in their question and the server will forward it to an expert.

[2132] The expert enters the answer to the question, and the server sends the answer to the user, who can then check the answer and use it for the next training session if necessary.

[2133] Specific examples

[2134] For example, consider the case of a novice pet owner, Mr. A, training a new puppy.

[2135] 1. Person A registers with the system and logs in. On the profile settings screen, he enters the breed, age, and name of the puppy.

[2136] 2. Person A clicks the "Start a new training session" button on the dashboard. The server sends the puppy's profile information to the generation AI.

[2137] 3. The generation AI generates a training scenario optimized for Mr. A's puppy, and the server sends it to the device.

[2138] 4. Person A puts on the VR headset and begins the session. He commands the virtual pet, such as "sit," and the generative AI provides real-time feedback. The emotion engine recognizes Person A's emotions and adjusts the feedback accordingly.

[2139] 5. After the training is complete, feedback based on the analysis data from the generative AI and emotion engine is received and used for the next training.

[2140] 6. If you have further questions, get help from a professional.

[2141] Through this process, the system can help beginner pet owners and trainers effectively train their pets and deepen their relationships with them. It also recognizes the user's emotions and provides appropriate feedback, improving user satisfaction and training effectiveness.

[2142] The processing flow will be explained below.

[2143] ---

[2144] User Registration and Login

[2145] Step 1:

[2146] The user opens the service's website or app, and the device displays the new registration screen.

[2147] Step 2:

[2148] The user enters the required information such as name, email address, and password, and presses the registration button.

[2149] Step 3:

[2150] The server receives the entered information and stores it in a user database.

[2151] Step 4:

[2152] After the server saves the data, it generates a registration completion email and sends it to the user.

[2153] Step 5:

[2154] The user clicks the link in the email they received to activate their account and enters their email address and password on the login screen.

[2155] Step 6:

[2156] The server checks the entered authentication information and, if authentication is successful, instructs the device to display the dashboard.

[2157] User Profile Settings

[2158] Step 1:

[2159] The user clicks the "Profile Settings" tab on the dashboard. The device displays a form to enter the pet's type, age, name, and characteristics.

[2160] Step 2:

[2161] The user enters the pet's information and presses the save button.

[2162] Step 3:

[2163] The server stores the information you entered in a database and completes your profile setup.

[2164] Starting a training session

[2165] Step 1:

[2166] A user clicks the "Start a New Training Session" button on the dashboard.

[2167] Step 2:

[2168] The server retrieves the user's profile data and sends it to the generation AI module.

[2169] Step 3:

[2170] Generative AI generates optimal training scenarios based on the characteristics of the user and their pet.

[2171] Step 4:

[2172] The server transmits the generated training scenario to the terminal and displays it to the user.

[2173] Setting up the VR environment

[2174] Step 1:

[2175] The user puts on the VR headset.

[2176] Step 2:

[2177] The device launches the VR application and loads the training scenario received from the server.

[2178] Step 3:

[2179] When the user is ready, he presses the Start Session button.

[2180] Training run

[2181] Step 1:

[2182] The user interacts with the virtual pet in VR and gives commands (e.g., "sit").

[2183] Step 2:

[2184] The terminal recognizes the user's voice commands and transmits the instructions to the virtual pet.

[2185] Step 3:

[2186] The virtual pet reacts according to the algorithm of the generating AI and decides whether to follow the user's instructions.

[2187] Step 4:

[2188] Generative AI provides real-time feedback based on your training methods and your pet's reactions.

[2189] Step 5:

[2190] The emotion engine recognizes the user's emotions and feeds that data back to the generative AI, which then adjusts the feedback and advice it provides to take the user's emotions into account.

[2191] Step 6:

[2192] The emotion engine analyzes the emotional data and dynamically changes the training scenario according to the user's stress and satisfaction.

[2193] Feedback at the end of training

[2194] Step 1:

[2195] The user presses the end training session button.

[2196] Step 2:

[2197] The terminal notifies the server of the end of the session.

[2198] Step 3:

[2199] The server obtains analytical data from the generative AI and emotion engine and provides detailed feedback to the user.

[2200] Step 4:

[2201] Feedback includes suggestions for improvement and next training session.

[2202] Expert support

[2203] Step 1:

[2204] User clicks on the Support tab on the dashboard.

[2205] Step 2:

[2206] The terminal displays a chat window and the user types in a question.

[2207] Step 3:

[2208] The server forwards the question to an expert.

[2209] Step 4:

[2210] The expert enters the answer to the question and the server sends the answer to the user.

[2211] Step 5:

[2212] Users can review their answers and use them in future training sessions if necessary.

[2213] ---

[2214] The above is a description of the system program processing divided into specific steps.

[2215] Example 2

[2216] 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."

[2217] Conventional pet training systems require users to have specialized knowledge and experience to effectively train their pets, making them difficult for beginners to use. Furthermore, the effectiveness of training is limited because the system does not provide feedback that takes into account the user's emotions. Furthermore, only a limited number of systems offer support from experts.

[2218] 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.

[2219] In this invention, the server includes means for accepting user registration information, means for setting a user profile, means for interacting with and training a pet using a virtual reality environment, means for generating a training plan optimized for the user and the pet using generative artificial intelligence, means for recognizing voice commands and conveying instructions to the virtual pet, means for recognizing the user's emotions in real time using an emotion engine and dynamically adjusting feedback and training scenarios, and means for providing feedback to the user in real time. This allows even beginners to train effectively and provides feedback that takes the user's emotions into consideration. Furthermore, the quality of training is improved by easily receiving support from experts.

[2220] "Means for accepting user registration information" refers to the function by which a user inputs their own information into the system and the system records that information.

[2221] "Means for setting up a user profile" is a function that allows users to input detailed information about themselves and their pets, and the system then stores and manages that information.

[2222] "Means for interacting with and training a pet using a virtual reality environment" refers to a function that uses virtual reality technology to allow a user to interact with and train a virtual pet.

[2223] "Means for generating training plans optimized for users and their pets using generative artificial intelligence" refers to a function that utilizes artificial intelligence technology to automatically create training plans based on the characteristics of users and their pets.

[2224] The "means for recognizing voice commands and transmitting instructions to a virtual pet" is a function for recognizing the user's voice and transmitting those instructions to a virtual pet.

[2225] "Means of recognizing the user's emotions in real time using an emotion engine and dynamically adjusting feedback and training scenarios" is a function that analyzes the user's facial expressions and voice to determine their emotions and adapts feedback and training content based on that.

[2226] "Means for providing feedback to the user in real time" refers to a function that provides immediate evaluation and advice on the operations and reactions performed by the user during training.

[2227] The present invention is a system that combines a virtual reality environment and a pet training experience system that uses generative artificial intelligence to enable users to effectively train their pets with an emotion engine that recognizes the user's emotions. Specific embodiments of this system are described below.

[2228] User Registration and Login

[2229] When a user opens the service's website or app, the device displays a new registration screen. The user enters the required information, such as name, email address, and password, and presses the registration button. The server receives the entered information and saves it in the user database. After saving, the server sends the user a registration completion email. The user clicks the link in the received email to activate their account and enters their authentication information on the login screen. The server verifies the authentication information, and if login is successful, it instructs the device to display the dashboard.

[2230] User Profile Settings

[2231] When a user clicks on the "Profile Settings" tab on the dashboard, the terminal displays a form to input the pet's type, age, name, and characteristics. The user enters the pet's information and presses the save button. The server saves this information in the database and completes the profile settings.

[2232] Starting a training session

[2233] The user clicks the "Start a new training session" button on the dashboard. The server retrieves the user's profile data and sends it to the generation AI module. The generation AI generates an optimal training scenario based on the characteristics of the user and their pet. The server sends the generated scenario to the device and displays it to the user.

[2234] Setting up the VR environment

[2235] The user puts on the VR headset, the device launches the VR application, loads the training scenario received from the server, and when the user is ready, presses the start session button.

[2236] Training run

[2237] The user interacts with the virtual pet in VR and gives instructions. For example, if the user commands "sit," the device recognizes the voice command and sends the instruction to the virtual pet. The virtual pet responds according to the algorithm of the generation AI and determines whether to follow the user's instructions. The emotion engine recognizes the user's emotions and feeds that data back to the generation AI. The generation AI dynamically adjusts its feedback and advice taking the user's emotions into account.

[2238] Feedback at the end of training

[2239] When the user presses the end button on the device, the device notifies the server that the session is over. The server then receives analytical data from the generative AI and emotion engine and provides detailed feedback to the user, including suggestions for improvement and advice for the next training session.

[2240] Expert support

[2241] When a user clicks the "Support" tab on the dashboard, the device displays a chat window. The user enters a question, which the server forwards to an expert. The expert enters an answer to the question, and the server sends the answer to the user. The user reviews the answer and, if necessary, uses it for future training.

[2242] Hardware and software used

[2243] Hardware: PC, smartphone, VR headset, audio input device

[2244] Software: Web browsers, server software (e.g., Apache, NGINX), databases (e.g., MySQL, PostgreSQL), generative AI modules, emotion engines, VR applications, chat applications

[2245] Specific examples

[2246] For example, consider a novice pet owner training a new puppy. The user registers and logs in to the system. They enter the puppy's breed, age, and name on the profile settings screen. When the user clicks the "Start New Training Session" button on the dashboard, the server sends the puppy's profile information to the generation AI. The generation...

Claims

1. means for accepting user registration information; means for configuring a user profile; A means for interacting with and training a pet using a virtual reality environment; A means for generating a training plan optimized for the user and the pet using a generative artificial intelligence; a means for providing feedback to the user in real time; A system including:

2. 10. The system of claim 1, further comprising means for providing feedback after a training session based on analysis data from the generative artificial intelligence.

3. The system of claim 1 further comprising a communication means for the user to receive support from an expert.

Images