system

Abstract

We provide the system. [Solution] A means for generating individualized training content based on the user's objectives and characteristics, A means of providing the generated training content to the user's device, A means of monitoring user behavior in real time during training and providing feedback, A means of analyzing user training results and improving the next training plan, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Conventional training programs often provide uniform content and it is difficult to sufficiently meet the characteristics and purposes of individual users. Also, when a user conducts training, they cannot receive feedback each time, which hinders efficient skill improvement. Furthermore, it is difficult to effectively reflect the progress of a user's performance in training, and the mechanism for making use of it in the next training plan is insufficient.

Means for Solving the Problems

[0005] This invention provides a means for generating individualized training content based on the user's objectives and characteristics, thereby providing a training plan optimized for each user. Furthermore, by delivering the generated training content to the user's terminal, flexible training is possible without being restricted by location or time. In addition, by analyzing user behavior data collected during training in real time and providing immediate feedback, efficient skill improvement is promoted. Moreover, by providing a mechanism to analyze the results after training and use them to improve the next training plan, it supports the user's continuous growth.

[0006] A "user" is an individual who uses the system to receive personalized training.

[0007] "Purpose" refers to the goals or objectives that a user aims to achieve through training.

[0008] "Characteristics" refer to the basic characteristics and abilities of a user, and are the foundational information used when customizing training content.

[0009] "Training content" refers to the specific learning or training program generated based on the user's objectives and characteristics.

[0010] A "device" refers to a device used by the user, on which the training content is provided.

[0011] "Feedback" refers to evaluations and comments provided during or after training, which are useful information for users to improve their own performance.

[0012] "Analysis" refers to evaluating users' training results and behavioral data, and performing analyses to incorporate those findings into future training sessions. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0014] An example of an embodiment of the system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0019] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] The present invention is an AI-powered virtual training system that enables users to receive individually customized training programs. Embodiments of the present invention are described below.

[0035] This system is primarily composed of three main elements: servers, terminals, and users.

[0036] The server functions as a central processing unit, receiving user-specified objectives and characteristic information, and utilizing an AI model to generate an optimal training program. The generated program undergoes necessary processing on the server to be delivered in a format suitable for the user. The training content is then transmitted to the terminal. The server also has the capability to analyze user data collected in real time during training and generate immediate feedback.

[0037] The terminal is a device used by the user as an interface and can take various forms, such as a PC, smartphone, or VR equipment. This terminal receives training programs sent from the server and displays them so that the user can execute them. In this process, the terminal plays a role in ensuring the immediacy of feedback by continuously sending user input and behavioral data to the server in real time.

[0038] A user is an individual who receives training using this system. After setting their goals within the system, the user begins training. The system provides a program based on the user's goals, and the user trains according to that program. During training, the user receives feedback through a terminal to help improve their skills. After training, the user can reset their goals based on the evaluation results provided by the server and proceed to new training.

[0039] For example, if a user desires training to improve their language skills, the server uses AI to analyze a large amount of learning material and practice problems, generating a learning plan optimized for the user's characteristics. The generated plan is provided to the user in an interactive format via their device, allowing them to learn and check their progress on the spot. The user's answers and learning process are analyzed in real time, and immediate feedback is provided, enabling efficient skill improvement.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The user launches the application and enters their account information on the login screen.

[0043] Step 2:

[0044] The terminal sends the entered login information to the server for authentication. The server verifies the information and grants permission to log in.

[0045] Step 3:

[0046] The user enters their training objectives and personal information (e.g., current skill level and areas of interest).

[0047] Step 4:

[0048] The terminal sends the information entered by the user to the server.

[0049] Step 5:

[0050] The server uses an AI model based on the information it receives to generate a customized training program for each user.

[0051] Step 6:

[0052] The server sends the generated training program to the terminal.

[0053] Step 7:

[0054] The device displays the training program to the user and prepares to begin training.

[0055] Step 8:

[0056] Users can start training at any time they choose and work on the displayed tasks and practice problems.

[0057] Step 9:

[0058] The terminal sends user actions and responses to the server in real time.

[0059] Step 10:

[0060] The server analyzes user data received in real time and generates feedback.

[0061] Step 11:

[0062] The server sends the generated feedback to the terminal and provides it to the user.

[0063] Step 12:

[0064] Users provide feedback, which is then used to adjust the content and approach of the next training session.

[0065] Step 13:

[0066] After training is complete, the server stores the user's training data and uses it to improve the next training plan.

[0067] (Example 1)

[0068] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0069] Conventional training systems have struggled to provide efficient and personalized training programs tailored to each user's individual goals and characteristics, and have also made it difficult to obtain real-time feedback. As a result, the learning effect of users has been limited, and achieving sustained skill improvement has been a challenge.

[0070] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0071] In this invention, the server includes means for generating an individualized training program using a generated AI model based on the user's goals and attribute information; means for transmitting and displaying the generated program on the user's information processing device; and means for collecting the user's actions in real time during training and providing immediate feedback. This enables flexible and effective training and feedback tailored to the user.

[0072] A "user" is an individual who receives training using this system and is the entity that operates the system to achieve a specific objective.

[0073] A "training program" is a series of instructional materials designed to facilitate learning and skill improvement, generated based on the user's characteristics.

[0074] A "generative AI model" is an algorithm that uses artificial intelligence technology to analyze data and create an optimal training program tailored to user characteristics.

[0075] An "information processing device" is a device that includes an interface that the user directly operates and has the function of displaying and operating programs from a server.

[0076] "Feedback" refers to comments and evaluations provided in real time during a user's training process, and is information used to adjust the direction of learning.

[0077] "Real-time" refers to a data processing method that prioritizes immediacy, where information is processed, transmitted, and received almost simultaneously.

[0078] This invention is a system for providing training programs tailored to the individual needs of users, and consists of three elements: a server, a terminal, and a user.

[0079] The server plays a central role in this system. The server collects user-defined goals and attribute information and generates individual training programs using advanced generative AI models. These AI models are implemented using widely used frameworks such as TENSORFLOW® and PyTorch. The generated programs are formatted to the optimal format according to the user's attributes and sent to the terminal.

[0080] For example, if a user enters a prompt such as "Generate a customized training plan to improve my language skills," the server will analyze a large amount of data based on that request and select the most suitable learning materials and practice problems for the user.

[0081] The terminal is an information processing device that the user directly operates, and can take the form of a PC, smartphone, or VR device. The terminal receives training programs sent from the server and presents them to the user in a visual and interactive format. While the user is performing the training, the terminal plays the role of sending the user's input and behavioral data back to the server in real time.

[0082] The user is the entity that utilizes this system to achieve their objectives. The user sets their own training goals for the system and practices training according to the provided program. During training, the user receives real-time feedback from the server via their terminal, allowing them to adjust their learning direction and effectively improve their skills. Upon completion of training, the user can set their next goals based on the evaluation results generated by the server and strive for continuous skill improvement.

[0083] This invention enables users to learn efficiently and effectively through the provision of personalized training programs and real-time immediate feedback.

[0084] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0085] Step 1:

[0086] The user uses a terminal to input their training goals and attribute information. This data includes information such as "improving language skills" and "flexible learning style." The terminal formats this information into a prompt and sends it to the server.

[0087] Step 2:

[0088] The server activates a generative AI model based on the received prompt message. The AI ​​model analyzes the input user information and constructs an optimal training plan for the user from a large dataset. This process uses a learning algorithm to select content that matches the user's attributes. The generated plan is then reformatted and sent to the terminal.

[0089] Step 3:

[0090] The device receives the training plan sent from the server and displays it to the user in a visual and interactive format. The user begins training according to the presented program, making selections and inputs as instructed. At this stage, learning progresses through interactive content in audio, video, and text formats.

[0091] Step 4:

[0092] During training, the device records the user's input and selected options in real time and sends them to the server. This data includes the user's response history and learning progress, which the server uses to generate immediate feedback for the user.

[0093] Step 5:

[0094] The server analyzes the collected user data and adjusts the training program to help improve the user's skills. Feedback is sent to the terminal in real time and shown to the user. Based on this feedback, the user adjusts their next learning step as they progress.

[0095] Step 6:

[0096] Upon completing the training, users receive their final evaluation results from the server and set their next goals. The server then optimizes the next training plan based on the evaluation, becoming a platform that supports the user's continuous skill improvement.

[0097] (Application Example 1)

[0098] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0099] Conventional training systems have faced challenges in providing optimal training content tailored to individual user characteristics and objectives, as well as insufficient real-time feedback. In particular, when aiming to improve specific movements such as those in sports, there is a need to instantly analyze the user's movements and provide accurate advice.

[0100] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0101] In this invention, the server includes means for generating individualized training content based on the user's objectives and characteristics, means for providing the generated training content to a receiving device, and means for analyzing the user's actions in real time and generating immediate feedback. This enables the provision of optimal training tailored to the individual user's characteristics in real time, allowing for effective skill improvement.

[0102] A "user" is an individual who aims to improve their skills and abilities by using a training system.

[0103] "Means for generating individualized training content based on objectives and characteristics" refers to a function for creating training programs optimized for the different goals and characteristics of each user.

[0104] "Means of providing to the receiving device" refers to a method of distributing training content generated on the server to a device that the user accesses.

[0105] "A means of analyzing actions in real time and generating immediate feedback" refers to a function that analyzes the user's actions during training and instantly provides advice and improvement suggestions based on the results.

[0106] "A means of monitoring and providing feedback in real time" refers to a function that monitors user behavior and provides immediate feedback based on the results.

[0107] As a specific embodiment of this invention, an AI-powered training system is constructed. The server generates individual training programs based on the user's objectives and characteristics. The server analyzes a large amount of data using a generated AI model, generates a program optimized for the user's specific needs, and provides it to the terminal.

[0108] The server uses AI modeling libraries (e.g., TensorFlow or PyTorch) to design the optimal training to achieve the user's objectives. The training content is provided to the user via a device, which includes smartphones, tablets, and head-mounted displays. The device collects user behavior data in real time and sends it to the server.

[0109] The server performs motion analysis based on the collected data and uses computer vision tools (e.g., OpenCV) to analyze the details of the user's movements. An immediate feedback function quickly provides the user with insights gained during training and identifies areas for improvement.

[0110] As a concrete example, suppose a user wants to improve their soccer dribbling skills. The server analyzes the user's motion data and provides effective feedback in real time. This helps improve their technique by advising the user on which direction they should control the ball.

[0111] An example prompt is: "Use the AI ​​training system to generate a custom program to improve the user's motor skills. In particular, enhance the analysis of dribbling movements and the ability to provide immediate feedback." Through this prompt, the system designs the optimal approach.

[0112] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0113] Step 1:

[0114] The server receives purpose and characteristic information as input from the user. This received information is treated as initial data for analysis by the generative AI model. Using this data, the server prepares to generate individual training programs.

[0115] Step 2:

[0116] The server uses a generative AI model to analyze user input data and generate an optimal training program. Specifically, it determines the skill set and practice content necessary for the user to achieve their goals. The training program obtained through this process is output as data to be sent to the terminal.

[0117] Step 3:

[0118] The terminal receives the training program sent from the server and displays it to the user through an interface. The user then begins training based on this displayed program. The terminal continuously collects user behavior data and sends it to the server.

[0119] Step 4:

[0120] The server analyzes real-time user behavior data collected from the terminal. Computer vision tools are used to evaluate the quality of user actions and areas for improvement. Based on these evaluation results, feedback is generated immediately and sent to the terminal.

[0121] Step 5:

[0122] The device displays feedback sent from the server to the user. Based on this feedback, the user can continuously correct their actions on the spot. Since feedback is received in real time, the training progress can be adjusted as needed.

[0123] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0124] This invention is a virtual training system that combines an emotion engine that recognizes the user's emotional state, and provides a training experience tailored to the user's characteristics. This embodiment will be described in detail.

[0125] This system includes servers, terminals, users, and an emotion engine as its main components.

[0126] The server functions as a central processing unit responsible for generating and controlling training programs and processing user data. The server receives sentiment analysis results from the sentiment engine along with user characteristic information, and dynamically adjusts the training program based on this. It also receives user behavior data during training and performs analysis to provide real-time feedback.

[0127] The terminal is a device that the user uses for training, and can be a smartphone, PC, or VR device. During training, the terminal collects the user's input and actions, and the emotion engine obtains the emotional state inferred from the user's facial expressions and voice, and sends it to the server.

[0128] The user is the one who operates this system and receives training. The training is delivered via a terminal, and the user works on individual tasks in real time, receives feedback, and checks their progress.

[0129] An emotion engine is a device or software that analyzes a user's voice and facial expression data to identify their emotional state. This engine analyzes emotions based on data transmitted from the terminal and transmits the results to a server. This information is used to adjust training.

[0130] For example, when a user undergoes training to improve their presentation skills, the emotion engine can recognize the user's emotions, such as anxiety or nervousness. Based on this information, the server adapts the training content in real time. For instance, if the system detects that the user is nervous, it can temporarily lower the difficulty level of the training or provide feedback to encourage relaxation. In this way, a flexible training experience tailored to the user's emotional state can be provided.

[0131] The following describes the processing flow.

[0132] Step 1:

[0133] The user launches the application and logs into the device using their account information.

[0134] Step 2:

[0135] The device sends user information to the server, and past training data and profiles are retrieved from the server.

[0136] Step 3:

[0137] Users set their training objectives and goals, and update their characteristic information as needed.

[0138] Step 4:

[0139] The terminal sends user input information to the server and issues instructions to activate the emotion engine.

[0140] Step 5:

[0141] The emotion engine analyzes the user's emotional state based on facial expression and voice data collected from the device.

[0142] Step 6:

[0143] The server receives data from the emotion engine, combines it with user characteristic information, and generates an appropriate training program.

[0144] Step 7:

[0145] The server sends the generated training program and initial feedback to the terminal.

[0146] Step 8:

[0147] The device displays the training content to the user and prompts them to start the training.

[0148] Step 9:

[0149] The user begins training and works on the assigned tasks.

[0150] Step 10:

[0151] The device records the user's behavior and emotional state in real time during training and sends the data to the server.

[0152] Step 11:

[0153] Based on the data received by the server, it generates real-time feedback tailored to the user's emotional state and sends it to the device.

[0154] Step 12:

[0155] The device displays feedback to the user in real time and adjusts the training content as needed.

[0156] Step 13:

[0157] After training is complete, the server stores the user's training data and uses it to improve the next training plan.

[0158] (Example 2)

[0159] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0160] Modern training systems often lack sufficient customization to consider individual user characteristics and emotional states, instead offering only uniform content. This makes it difficult to provide a flexible training experience that responds to user emotional changes, resulting in challenges in achieving effective results.

[0161] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0162] In this invention, the server includes means for generating individualized training content based on the user's characteristics and emotional state, means for providing the generated training content to an information processing device used by the user, and means for collecting the user's voice and facial expression data during training and identifying the user's emotional state using an emotion engine. This makes it possible to provide individually tailored training content to each user and realize an effective and flexible training experience that responds to the user's emotional state.

[0163] A "user" is an individual who uses this system to receive individual training.

[0164] "Characteristics" refer to individual features of a user, including their personality, abilities, and past achievements.

[0165] "Emotional state" refers to the emotional state a user is experiencing at a particular moment, and is captured through voice and facial expression data.

[0166] "Training content" refers to the collective set of tasks and exercises provided by the system to help users improve specific abilities.

[0167] "Information processing equipment" refers to devices such as smartphones, PCs, and VR devices used by users in a training system.

[0168] "Voice and facial expression data" refers to data that includes information about the user's tone of voice and facial expressions, and forms the basis for analysis by the emotion engine.

[0169] An "emotion engine" is a device or software that analyzes a user's voice and facial expression data to identify their emotional state.

[0170] A "server" is a data processing system that acts as a central processing unit, generating and adjusting training programs based on the results of emotional state analysis.

[0171] This invention provides a system for conducting individually tailored training according to the user's characteristics and emotional state. This system mainly consists of a server, a terminal, and an emotion engine.

[0172] The server functions as the central processing unit of this system. Based on the user's characteristic information, it receives analysis results of emotional states sent from the emotion engine. The server integrates this information and dynamically generates training content that is tailored to the user's real-time emotional state. It also sends the generated training content to the terminal, providing adaptive feedback to the user. The software used includes AI algorithms with advanced data processing capabilities.

[0173] The terminal is an information processing device for users to receive training. Smartphones, PCs, VR devices, etc., are used, and they play a role in transmitting the user's voice and facial expression data to the emotion engine. The terminal also displays feedback sent from the server to the user, supporting continued training. It is equipped with various sensors and communication modules to enable real-time data collection.

[0174] Users work on training tasks provided through their devices and adjust their training based on feedback from the server. Voice and facial expression data collected during training are analyzed in real time by an emotion engine, and the user's emotional state is transmitted to the server.

[0175] The emotion engine consists of software for analyzing the user's voice and facial expression data. It captures the characteristics of the voice and facial expressions to identify the user's current emotional state and sends this information to the server. Specifically, it uses an AI model to quantify the type and intensity of emotions, and uses the results to adjust the training content.

[0176] As a concrete example, consider a scenario where a user is undergoing training to improve their presentation skills. When the user simulates a presentation using their device, the emotion engine detects tremors in the user's voice and tension in their facial expressions. The server uses this information to temporarily modify the presentation content or provide feedback to encourage relaxation.

[0177] An example of a prompt message would be, "Please identify in real time any tension the user exhibits during the presentation and suggest ways to alleviate it." This allows for a flexible and personalized training experience.

[0178] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0179] Step 1:

[0180] The user activates the device and begins a training session. The device prepares to collect voice and facial expression data and establishes an interface for sending data to the emotion engine. The input consists of the user's voice and facial expression data, and the output is this data sent to the emotion engine in real time. Specifically, the device's microphone and camera activate to capture the user's voice and facial expressions.

[0181] Step 2:

[0182] The device transmits the collected user voice and facial expression data to the emotion engine. The emotion engine analyzes this data to identify the user's emotional state. The input is the user's raw data (voice, facial expressions), and the output is an analysis result indicating the emotional state. The emotional state is quantified, and the type and intensity of the emotion the user is experiencing are determined. Specifically, an AI algorithm identifies data features and evaluates the emotional state.

[0183] Step 3:

[0184] The server receives the results of an emotional state analysis sent from the emotion engine. Next, it dynamically generates and adjusts the training content by combining this with the user's characteristic information (past training results, goals, etc.). The input includes the emotional analysis results and user characteristic information, and the output is an adjusted training program. The server integrates this information and performs specific actions to create user-specific training content in real time.

[0185] Step 4:

[0186] The server sends the generated training program and feedback to the terminal. The terminal presents the received information to the user visually and audibly, supporting the progress of the training. The input is the training program and feedback from the server, and the output is a visual and audible display to the user. Specific actions include displaying training instructions on the terminal's display and playing audio feedback.

[0187] Step 5:

[0188] Users train by following tasks provided through the device. They receive feedback and use it to improve their subsequent efforts. Input is feedback and training instructions from the device, and output is the improvement in the user's training performance and emotional state. Specifically, users perform the presented tasks and adaptively incorporate the feedback.

[0189] (Application Example 2)

[0190] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0191] Training conducted without considering user emotions makes it difficult to provide an effective learning experience. This is especially true in retail settings such as customer service, where understanding staff emotional states and providing appropriate feedback is crucial. However, current systems struggle to adapt in real time. Therefore, the challenge lies in enabling the provision of flexible training plans that respond to emotional states.

[0192] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0193] In this invention, the server includes means for recognizing the user's emotional state and flexibly adjusting the first section based on it, means for generating individualized training content based on the user's purpose and characteristics, and means for monitoring the user's behavior in real time during training and providing emotionally appropriate feedback. This makes it possible to provide an optimal training experience tailored to the user's emotions and to support the improvement of customer service skills in physical stores in real time.

[0194] "Emotional state" refers to the user's psychological and emotional state, and is primarily identified through the analysis of voice and facial expressions.

[0195] "Training content" refers to a series of activities and instructions aimed at improving the user's abilities, and is customized based on the user's goals and characteristics.

[0196] "Device" refers to the device that the user uses for training, and includes smartphones, PCs, VR devices, etc.

[0197] "Monitoring" refers to the process of observing and analyzing user behavior and reactions in real time.

[0198] "Feedback" refers to the reactions and advice provided to the user, and is conducted in real time with the aim of assisting the progress of the training.

[0199] This invention functions as a system to support training in the service industry. The server is responsible for recognizing the user's emotional state in real time and adjusting the training content accordingly. The emotional state is analyzed from voice and facial expression data collected by a terminal. The terminal can be, for example, smart glasses or a smartphone. These terminals capture the user's voice and facial expressions, and the data is sent to the server.

[0200] On the server, Python's sentiment analysis library and OpenCV are used for sentiment analysis, and Google's Cloud Speech-to-Text API is used for speech analysis. Based on behavioral and sentiment data, real-time feedback is generated and provided to the user.

[0201] For example, if a customer service staff member feels nervous around a customer, the server generates advice to help alleviate that tension. For instance, feedback such as "Try speaking a little more slowly" might be displayed on the smart glasses' screen.

[0202] An example of a prompt might be, "Analyze the emotional state of the current customer service interaction and provide the staff with the most appropriate advice in real time." Using this prompt, the AI ​​model can be instructed to generate feedback based on the results of the emotional analysis.

[0203] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0204] Step 1:

[0205] The device captures the user's voice and facial expressions in real time. It acquires the user's video and audio data as input, analyzes facial expressions using OpenCV, and converts the audio to text using the Google Cloud Speech-to-Text API. This generates initial emotional state data.

[0206] Step 2:

[0207] The server receives emotional state data sent from the terminal. The inputs here are analyzed facial expression data and voice-to-text data. The server uses a Python emotion analysis library to estimate the emotional state and stores the results in internal data. The output generates the user's tension level and the ratio of positive to negative emotions.

[0208] Step 3:

[0209] The server adjusts the training content based on the emotional state. The input is the emotional state data obtained in step 2. A generative AI model is used to create training feedback based on the obtained emotional data. The output is a feedback message, which is displayed in the user interface.

[0210] Step 4:

[0211] Feedback is provided to the user through the device. The input here is the feedback message generated in step 3. Visual feedback is immediately displayed on the device's display, for example, the screen of smart glasses. The user can then use this to improve their training.

[0212] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0213] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0214] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0215] [Second Embodiment]

[0216] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0217] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0218] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0219] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0220] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0221] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0222] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0223] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0224] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0225] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0226] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0227] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0228] The present invention is an AI-powered virtual training system that enables users to receive individually customized training programs. Embodiments of the present invention are described below.

[0229] This system is primarily composed of three main elements: servers, terminals, and users.

[0230] The server functions as a central processing unit, receiving user-specified objectives and characteristic information, and utilizing an AI model to generate an optimal training program. The generated program undergoes necessary processing on the server to be delivered in a format suitable for the user. The training content is then transmitted to the terminal. The server also has the capability to analyze user data collected in real time during training and generate immediate feedback.

[0231] The terminal is a device used by the user as an interface and can take various forms, such as a PC, smartphone, or VR equipment. This terminal receives training programs sent from the server and displays them so that the user can execute them. In this process, the terminal plays a role in ensuring the immediacy of feedback by continuously sending user input and behavioral data to the server in real time.

[0232] A user is an individual who receives training using this system. After setting their goals within the system, the user begins training. The system provides a program based on the user's goals, and the user trains according to that program. During training, the user receives feedback through a terminal to help improve their skills. After training, the user can reset their goals based on the evaluation results provided by the server and proceed to new training.

[0233] For example, if a user desires training to improve their language skills, the server uses AI to analyze a large amount of learning material and practice problems, generating a learning plan optimized for the user's characteristics. The generated plan is provided to the user in an interactive format via their device, allowing them to learn and check their progress on the spot. The user's answers and learning process are analyzed in real time, and immediate feedback is provided, enabling efficient skill improvement.

[0234] The following describes the processing flow.

[0235] Step 1:

[0236] The user launches the application and enters their account information on the login screen.

[0237] Step 2:

[0238] The terminal sends the entered login information to the server for authentication. The server verifies the information and grants permission to log in.

[0239] Step 3:

[0240] The user enters their training objectives and personal information (e.g., current skill level and areas of interest).

[0241] Step 4:

[0242] The terminal sends the information entered by the user to the server.

[0243] Step 5:

[0244] The server uses an AI model based on the information it receives to generate a customized training program for each user.

[0245] Step 6:

[0246] The server sends the generated training program to the terminal.

[0247] Step 7:

[0248] The device displays the training program to the user and prepares to begin training.

[0249] Step 8:

[0250] Users can start training at any time they choose and work on the displayed tasks and practice problems.

[0251] Step 9:

[0252] The terminal sends user actions and responses to the server in real time.

[0253] Step 10:

[0254] The server analyzes user data received in real time and generates feedback.

[0255] Step 11:

[0256] The server sends the generated feedback to the terminal and provides it to the user.

[0257] Step 12:

[0258] Users provide feedback, which is then used to adjust the content and approach of the next training session.

[0259] Step 13:

[0260] After training is complete, the server stores the user's training data and uses it to improve the next training plan.

[0261] (Example 1)

[0262] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0263] Conventional training systems have struggled to provide efficient and personalized training programs tailored to each user's individual goals and characteristics, and have also made it difficult to obtain real-time feedback. As a result, the learning effect of users has been limited, and achieving sustained skill improvement has been a challenge.

[0264] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0265] In this invention, the server includes means for generating an individualized training program using a generated AI model based on the user's goals and attribute information; means for transmitting and displaying the generated program on the user's information processing device; and means for collecting the user's actions in real time during training and providing immediate feedback. This enables flexible and effective training and feedback tailored to the user.

[0266] A "user" is an individual who receives training using this system and is the entity that operates the system to achieve a specific objective.

[0267] A "training program" is a series of instructional materials designed to facilitate learning and skill improvement, generated based on the user's characteristics.

[0268] A "generative AI model" is an algorithm that uses artificial intelligence technology to analyze data and create an optimal training program tailored to user characteristics.

[0269] An "information processing device" is a device that includes an interface that the user directly operates and has the function of displaying and operating programs from a server.

[0270] "Feedback" refers to comments and evaluations provided in real time during a user's training process, and is information used to adjust the direction of learning.

[0271] "Real-time" refers to a data processing method that prioritizes immediacy, where information is processed, transmitted, and received almost simultaneously.

[0272] This invention is a system for providing training programs tailored to the individual needs of users, and consists of three elements: a server, a terminal, and a user.

[0273] The server plays a central role in this system. The server collects user-defined goals and attribute information and generates individual training programs using advanced generative AI models. These AI models are implemented using widely used frameworks such as TensorFlow and PyTorch. The generated programs are formatted to the optimal format according to the user's attributes and sent to the terminal.

[0274] For example, if a user enters a prompt such as "Generate a customized training plan to improve my language skills," the server will analyze a large amount of data based on that request and select the most suitable learning materials and practice problems for the user.

[0275] The terminal is an information processing device that the user directly operates, and can take the form of a PC, smartphone, or VR device. The terminal receives training programs sent from the server and presents them to the user in a visual and interactive format. While the user is performing the training, the terminal plays the role of sending the user's input and behavioral data back to the server in real time.

[0276] The user is the entity that utilizes this system to achieve their objectives. The user sets their own training goals for the system and practices training according to the provided program. During training, the user receives real-time feedback from the server via their terminal, allowing them to adjust their learning direction and effectively improve their skills. Upon completion of training, the user can set their next goals based on the evaluation results generated by the server and strive for continuous skill improvement.

[0277] This invention enables users to learn efficiently and effectively through the provision of personalized training programs and real-time immediate feedback.

[0278] The flow of the specific process in Example 1 will be described using FIG. 11.

[0279] Step 1:

[0280] The user uses the terminal to input their training goals and attribute information. The input data includes information such as "improvement of language skills" and "flexible learning style". The terminal formats this information as a prompt sentence and sends it to the server.

[0281] Step 2:

[0282] The server activates the generated AI model based on the received prompt sentence. The AI model analyzes the input user information and constructs an optimal training plan for the user from a large dataset. In this process, a learning algorithm is used, and content that matches the user's attributes is selected. The generated plan is reformatted and sent to the terminal.

[0283] Step 3:

[0284] The terminal receives the training plan sent from the server and displays it in a visual and operable form for the user. The user starts training according to the presented program and makes selections and inputs according to the instructions. At this stage, learning progresses through interactive content in audio, video, and text formats.

[0285] Step 4:

[0286] During training, the terminal records the user's inputs and selected options in real time and sends them to the server. This data includes the user's answer history and learning progress information, and the server generates immediate feedback to the user based on this.

[0287] Step 5:

[0288] The server analyzes the collected user data and adjusts the training program to help improve the user's skills. Feedback is sent to the terminal in real time and shown to the user. Based on this feedback, the user adjusts their next learning step as they progress.

[0289] Step 6:

[0290] Upon completing the training, users receive their final evaluation results from the server and set their next goals. The server then optimizes the next training plan based on the evaluation, becoming a platform that supports the user's continuous skill improvement.

[0291] (Application Example 1)

[0292] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0293] Conventional training systems have faced challenges in providing optimal training content tailored to individual user characteristics and objectives, as well as insufficient real-time feedback. In particular, when aiming to improve specific movements such as those in sports, there is a need to instantly analyze the user's movements and provide accurate advice.

[0294] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0295] In this invention, the server includes means for generating individualized training content based on the user's objectives and characteristics, means for providing the generated training content to a receiving device, and means for analyzing the user's actions in real time and generating immediate feedback. This enables the provision of optimal training tailored to the individual user's characteristics in real time, allowing for effective skill improvement.

[0296] A "user" is an individual who aims to improve their skills and abilities by using a training system.

[0297] "Means for generating individualized training content based on objectives and characteristics" refers to a function for creating training programs optimized for the different goals and characteristics of each user.

[0298] "Means of providing to the receiving device" refers to a method of distributing training content generated on the server to a device that the user accesses.

[0299] "A means of analyzing actions in real time and generating immediate feedback" refers to a function that analyzes the user's actions during training and instantly provides advice and improvement suggestions based on the results.

[0300] "A means of monitoring and providing feedback in real time" refers to a function that monitors user behavior and provides immediate feedback based on the results.

[0301] As a specific embodiment of this invention, an AI-powered training system is constructed. The server generates individual training programs based on the user's objectives and characteristics. The server analyzes a large amount of data using a generated AI model, generates a program optimized for the user's specific needs, and provides it to the terminal.

[0302] The server uses AI modeling libraries (e.g., TensorFlow or PyTorch) to design the optimal training to achieve the user's objectives. The training content is provided to the user via a device, which includes smartphones, tablets, and head-mounted displays. The device collects user behavior data in real time and sends it to the server.

[0303] The server performs motion analysis based on the collected data and uses a computer vision tool (e.g., OpenCV) to analyze the details of the user's movements. The real-time feedback function quickly provides the insights that the user gains during training and identifies areas for improvement.

[0304] As a specific example, assume the user wants to improve their soccer dribbling skills. The server analyzes the user's motion data and provides effective real-time feedback. This supports the improvement of the technique by advising the user on which direction to control the ball.

[0305] As an example prompt sentence, there is "Please generate a custom program for improving the user's sports skills using an AI training system. In particular, strengthen the analysis of dribbling motions and the real-time feedback function." Through this sentence, the system designs for realizing the optimal approach.

[0306] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0307] Step 1:

[0308] The server receives the objective and characteristic information as input information from the user. The received information is treated as initial data for analysis by the generated AI model. Using this data, the server prepares to generate an individual training program.

[0309] Step 2:

[0310] The server uses the generated AI model to analyze the user's input data and generate an optimal training program. Specifically, it formulates the skill set and practice content necessary to achieve the user's objective. The training program obtained in this process is output as data to be transmitted to the terminal.

[0311] Step 3:

[0312] The terminal receives the training program sent from the server and displays it to the user through an interface. The user then begins training based on this displayed program. The terminal continuously collects user behavior data and sends it to the server.

[0313] Step 4:

[0314] The server analyzes real-time user behavior data collected from the terminal. Computer vision tools are used to evaluate the quality of user actions and areas for improvement. Based on these evaluation results, feedback is generated immediately and sent to the terminal.

[0315] Step 5:

[0316] The device displays feedback sent from the server to the user. Based on this feedback, the user can continuously correct their actions on the spot. Since feedback is received in real time, the training progress can be adjusted as needed.

[0317] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0318] This invention is a virtual training system that combines an emotion engine that recognizes the user's emotional state, and provides a training experience tailored to the user's characteristics. This embodiment will be described in detail.

[0319] This system includes servers, terminals, users, and an emotion engine as its main components.

[0320] The server functions as a central processing unit responsible for generating and controlling training programs and processing user data. The server receives sentiment analysis results from the sentiment engine along with user characteristic information, and dynamically adjusts the training program based on this. It also receives user behavior data during training and performs analysis to provide real-time feedback.

[0321] The terminal is a device that the user uses for training, and can be a smartphone, PC, or VR device. During training, the terminal collects the user's input and actions, and the emotion engine obtains the emotional state inferred from the user's facial expressions and voice, and sends it to the server.

[0322] The user is the one who operates this system and receives training. The training is delivered via a terminal, and the user works on individual tasks in real time, receives feedback, and checks their progress.

[0323] An emotion engine is a device or software that analyzes a user's voice and facial expression data to identify their emotional state. This engine analyzes emotions based on data transmitted from the terminal and transmits the results to a server. This information is used to adjust training.

[0324] For example, when a user undergoes training to improve their presentation skills, the emotion engine can recognize the user's emotions, such as anxiety or nervousness. Based on this information, the server adapts the training content in real time. For instance, if the system detects that the user is nervous, it can temporarily lower the difficulty level of the training or provide feedback to encourage relaxation. In this way, a flexible training experience tailored to the user's emotional state can be provided.

[0325] The following describes the processing flow.

[0326] Step 1:

[0327] The user launches the application and logs into the device using their account information.

[0328] Step 2:

[0329] The device sends user information to the server, and past training data and profiles are retrieved from the server.

[0330] Step 3:

[0331] Users set their training objectives and goals, and update their characteristic information as needed.

[0332] Step 4:

[0333] The terminal sends user input information to the server and issues instructions to activate the emotion engine.

[0334] Step 5:

[0335] The emotion engine analyzes the user's emotional state based on facial expression and voice data collected from the device.

[0336] Step 6:

[0337] The server receives data from the emotion engine, combines it with user characteristic information, and generates an appropriate training program.

[0338] Step 7:

[0339] The server sends the generated training program and initial feedback to the terminal.

[0340] Step 8:

[0341] The device displays the training content to the user and prompts them to start the training.

[0342] Step 9:

[0343] The user begins training and works on the assigned tasks.

[0344] Step 10:

[0345] The device records the user's behavior and emotional state in real time during training and sends the data to the server.

[0346] Step 11:

[0347] Based on the data received by the server, it generates real-time feedback tailored to the user's emotional state and sends it to the device.

[0348] Step 12:

[0349] The device displays feedback to the user in real time and adjusts the training content as needed.

[0350] Step 13:

[0351] After training is complete, the server stores the user's training data and uses it to improve the next training plan.

[0352] (Example 2)

[0353] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0354] Modern training systems often lack sufficient customization to consider individual user characteristics and emotional states, instead offering only uniform content. This makes it difficult to provide a flexible training experience that responds to user emotional changes, resulting in challenges in achieving effective results.

[0355] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0356] In this invention, the server includes means for generating individualized training content based on the user's characteristics and emotional state, means for providing the generated training content to an information processing device used by the user, and means for collecting the user's voice and facial expression data during training and identifying the user's emotional state using an emotion engine. This makes it possible to provide individually tailored training content to each user and realize an effective and flexible training experience that responds to the user's emotional state.

[0357] A "user" is an individual who uses this system to receive individual training.

[0358] "Characteristics" refer to individual features of a user, including their personality, abilities, and past achievements.

[0359] "Emotional state" refers to the emotional state a user is experiencing at a particular moment, and is captured through voice and facial expression data.

[0360] "Training content" refers to the collective set of tasks and exercises provided by the system to help users improve specific abilities.

[0361] "Information processing equipment" refers to devices such as smartphones, PCs, and VR devices used by users in a training system.

[0362] "Voice and facial expression data" refers to data that includes information about the user's tone of voice and facial expressions, and forms the basis for analysis by the emotion engine.

[0363] An "emotion engine" is a device or software that analyzes a user's voice and facial expression data to identify their emotional state.

[0364] A "server" is a data processing system that acts as a central processing unit, generating and adjusting training programs based on the results of emotional state analysis.

[0365] This invention provides a system for conducting individually tailored training according to the user's characteristics and emotional state. This system mainly consists of a server, a terminal, and an emotion engine.

[0366] The server functions as the central processing unit of this system. Based on the user's characteristic information, it receives analysis results of emotional states sent from the emotion engine. The server integrates this information and dynamically generates training content that is tailored to the user's real-time emotional state. It also sends the generated training content to the terminal, providing adaptive feedback to the user. The software used includes AI algorithms with advanced data processing capabilities.

[0367] The terminal is an information processing device for users to receive training. Smartphones, PCs, VR devices, etc., are used, and they play a role in transmitting the user's voice and facial expression data to the emotion engine. The terminal also displays feedback sent from the server to the user, supporting continued training. It is equipped with various sensors and communication modules to enable real-time data collection.

[0368] Users work on training tasks provided through their devices and adjust their training based on feedback from the server. Voice and facial expression data collected during training are analyzed in real time by an emotion engine, and the user's emotional state is transmitted to the server.

[0369] The emotion engine consists of software for analyzing the user's voice and facial expression data. It captures the characteristics of the voice and facial expressions to identify the user's current emotional state and sends this information to the server. Specifically, it uses an AI model to quantify the type and intensity of emotions, and uses the results to adjust the training content.

[0370] As a concrete example, consider a scenario where a user is undergoing training to improve their presentation skills. When the user simulates a presentation using their device, the emotion engine detects tremors in the user's voice and tension in their facial expressions. The server uses this information to temporarily modify the presentation content or provide feedback to encourage relaxation.

[0371] An example of a prompt message would be, "Please identify in real time any tension the user exhibits during the presentation and suggest ways to alleviate it." This allows for a flexible and personalized training experience.

[0372] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0373] Step 1:

[0374] The user activates the device and begins a training session. The device prepares to collect voice and facial expression data and establishes an interface for sending data to the emotion engine. The input consists of the user's voice and facial expression data, and the output is this data sent to the emotion engine in real time. Specifically, the device's microphone and camera activate to capture the user's voice and facial expressions.

[0375] Step 2:

[0376] The device transmits the collected user voice and facial expression data to the emotion engine. The emotion engine analyzes this data to identify the user's emotional state. The input is the user's raw data (voice, facial expressions), and the output is an analysis result indicating the emotional state. The emotional state is quantified, and the type and intensity of the emotion the user is experiencing are determined. Specifically, an AI algorithm identifies data features and evaluates the emotional state.

[0377] Step 3:

[0378] The server receives the results of an emotional state analysis sent from the emotion engine. Next, it dynamically generates and adjusts the training content by combining this with the user's characteristic information (past training results, goals, etc.). The input includes the emotional analysis results and user characteristic information, and the output is an adjusted training program. The server integrates this information and performs specific actions to create user-specific training content in real time.

[0379] Step 4:

[0380] The server sends the generated training program and feedback to the terminal. The terminal presents the received information to the user visually and audibly, supporting the progress of the training. The input is the training program and feedback from the server, and the output is a visual and audible display to the user. Specific actions include displaying training instructions on the terminal's display and playing audio feedback.

[0381] Step 5:

[0382] Users train by following tasks provided through the device. They receive feedback and use it to improve their subsequent efforts. Input is feedback and training instructions from the device, and output is the improvement in the user's training performance and emotional state. Specifically, users perform the presented tasks and adaptively incorporate the feedback.

[0383] (Application Example 2)

[0384] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0385] Training conducted without considering user emotions makes it difficult to provide an effective learning experience. This is especially true in retail settings such as customer service, where understanding staff emotional states and providing appropriate feedback is crucial. However, current systems struggle to adapt in real time. Therefore, the challenge lies in enabling the provision of flexible training plans that respond to emotional states.

[0386] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0387] In this invention, the server includes means for recognizing the user's emotional state and flexibly adjusting the first section based on it, means for generating individualized training content based on the user's purpose and characteristics, and means for monitoring the user's behavior in real time during training and providing emotionally appropriate feedback. This makes it possible to provide an optimal training experience tailored to the user's emotions and to support the improvement of customer service skills in physical stores in real time.

[0388] "Emotional state" refers to the user's psychological and emotional state, and is primarily identified through the analysis of voice and facial expressions.

[0389] "Training content" refers to a series of activities and instructions aimed at improving the user's abilities, and is customized based on the user's goals and characteristics.

[0390] "Device" refers to the device that the user uses for training, and includes smartphones, PCs, VR devices, etc.

[0391] "Monitoring" refers to the process of observing and analyzing user behavior and reactions in real time.

[0392] "Feedback" refers to the reactions and advice provided to the user, and is conducted in real time with the aim of assisting the progress of the training.

[0393] This invention functions as a system to support training in the service industry. The server is responsible for recognizing the user's emotional state in real time and adjusting the training content accordingly. The emotional state is analyzed from voice and facial expression data collected by a terminal. The terminal can be, for example, smart glasses or a smartphone. These terminals capture the user's voice and facial expressions, and the data is sent to the server.

[0394] On the server, Python's sentiment analysis library and OpenCV are used for sentiment analysis, and the Google Cloud Speech-to-Text API is used for speech analysis. Based on behavioral and sentiment data, real-time feedback is generated and provided to the user.

[0395] For example, if a customer service staff member feels nervous around a customer, the server generates advice to help alleviate that tension. For instance, feedback such as "Try speaking a little more slowly" might be displayed on the smart glasses' screen.

[0396] An example of a prompt might be, "Analyze the emotional state of the current customer service interaction and provide the staff with the most appropriate advice in real time." Using this prompt, the AI ​​model can be instructed to generate feedback based on the results of the emotional analysis.

[0397] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0398] Step 1:

[0399] The device captures the user's voice and facial expressions in real time. It acquires the user's video and audio data as input, analyzes facial expressions using OpenCV, and converts the audio to text using the Google Cloud Speech-to-Text API. This generates initial emotional state data.

[0400] Step 2:

[0401] The server receives emotional state data sent from the terminal. The inputs here are analyzed facial expression data and voice-to-text data. The server uses a Python emotion analysis library to estimate the emotional state and stores the results in internal data. The output generates the user's tension level and the ratio of positive to negative emotions.

[0402] Step 3:

[0403] The server adjusts the training content based on the emotional state. The input is the emotional state data obtained in step 2. A generative AI model is used to create training feedback based on the obtained emotional data. The output is a feedback message, which is displayed in the user interface.

[0404] Step 4:

[0405] Feedback is provided to the user through the device. The input here is the feedback message generated in step 3. Visual feedback is immediately displayed on the device's display, for example, the screen of smart glasses. The user can then use this to improve their training.

[0406] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0407] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0408] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0409] [Third Embodiment]

[0410] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0411] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0412] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0413] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0414] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0415] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0416] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0417] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0418] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0419] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0420] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0421] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0422] The present invention is an AI-powered virtual training system that enables users to receive individually customized training programs. Embodiments of the present invention are described below.

[0423] This system is primarily composed of three main elements: servers, terminals, and users.

[0424] The server functions as a central processing unit, receiving user-specified objectives and characteristic information, and utilizing an AI model to generate an optimal training program. The generated program undergoes necessary processing on the server to be delivered in a format suitable for the user. The training content is then transmitted to the terminal. The server also has the capability to analyze user data collected in real time during training and generate immediate feedback.

[0425] The terminal is a device used by the user as an interface and can take various forms, such as a PC, smartphone, or VR equipment. This terminal receives training programs sent from the server and displays them so that the user can execute them. In this process, the terminal plays a role in ensuring the immediacy of feedback by continuously sending user input and behavioral data to the server in real time.

[0426] A user is an individual who receives training using this system. After setting their goals within the system, the user begins training. The system provides a program based on the user's goals, and the user trains according to that program. During training, the user receives feedback through a terminal to help improve their skills. After training, the user can reset their goals based on the evaluation results provided by the server and proceed to new training.

[0427] For example, if a user desires training to improve their language skills, the server uses AI to analyze a large amount of learning material and practice problems, generating a learning plan optimized for the user's characteristics. The generated plan is provided to the user in an interactive format via their device, allowing them to learn and check their progress on the spot. The user's answers and learning process are analyzed in real time, and immediate feedback is provided, enabling efficient skill improvement.

[0428] The following describes the processing flow.

[0429] Step 1:

[0430] The user launches the application and enters their account information on the login screen.

[0431] Step 2:

[0432] The terminal sends the entered login information to the server for authentication. The server verifies the information and grants permission to log in.

[0433] Step 3:

[0434] The user enters their training objectives and personal information (e.g., current skill level and areas of interest).

[0435] Step 4:

[0436] The terminal sends the information entered by the user to the server.

[0437] Step 5:

[0438] The server uses an AI model based on the information it receives to generate a customized training program for each user.

[0439] Step 6:

[0440] The server sends the generated training program to the terminal.

[0441] Step 7:

[0442] The device displays the training program to the user and prepares to begin training.

[0443] Step 8:

[0444] Users can start training at any time they choose and work on the displayed tasks and practice problems.

[0445] Step 9:

[0446] The terminal sends user actions and responses to the server in real time.

[0447] Step 10:

[0448] The server analyzes user data received in real time and generates feedback.

[0449] Step 11:

[0450] The server sends the generated feedback to the terminal and provides it to the user.

[0451] Step 12:

[0452] Users provide feedback, which is then used to adjust the content and approach of the next training session.

[0453] Step 13:

[0454] After training is complete, the server stores the user's training data and uses it to improve the next training plan.

[0455] (Example 1)

[0456] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0457] Conventional training systems have struggled to provide efficient and personalized training programs tailored to each user's individual goals and characteristics, and have also made it difficult to obtain real-time feedback. As a result, the learning effect of users has been limited, and achieving sustained skill improvement has been a challenge.

[0458] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0459] In this invention, the server includes means for generating an individualized training program using a generated AI model based on the user's goals and attribute information; means for transmitting and displaying the generated program on the user's information processing device; and means for collecting the user's actions in real time during training and providing immediate feedback. This enables flexible and effective training and feedback tailored to the user.

[0460] A "user" is an individual who receives training using this system and is the entity that operates the system to achieve a specific objective.

[0461] A "training program" is a series of instructional materials designed to facilitate learning and skill improvement, generated based on the user's characteristics.

[0462] A "generative AI model" is an algorithm that uses artificial intelligence technology to analyze data and create an optimal training program tailored to user characteristics.

[0463] An "information processing device" is a device that includes an interface that the user directly operates and has the function of displaying and operating programs from a server.

[0464] "Feedback" refers to comments and evaluations provided in real time during a user's training process, and is information used to adjust the direction of learning.

[0465] "Real-time" refers to a data processing method that prioritizes immediacy, where information is processed, transmitted, and received almost simultaneously.

[0466] This invention is a system for providing training programs tailored to the individual needs of users, and consists of three elements: a server, a terminal, and a user.

[0467] The server plays a central role in this system. The server collects user-defined goals and attribute information and generates individual training programs using advanced generative AI models. These AI models are implemented using widely used frameworks such as TensorFlow and PyTorch. The generated programs are formatted to the optimal format according to the user's attributes and sent to the terminal.

[0468] For example, if a user enters a prompt such as "Generate a customized training plan to improve my language skills," the server will analyze a large amount of data based on that request and select the most suitable learning materials and practice problems for the user.

[0469] The terminal is an information processing device that the user directly operates, and can take the form of a PC, smartphone, or VR device. The terminal receives training programs sent from the server and presents them to the user in a visual and interactive format. While the user is performing the training, the terminal plays the role of sending the user's input and behavioral data back to the server in real time.

[0470] The user is the entity that utilizes this system to achieve their objectives. The user sets their own training goals for the system and practices training according to the provided program. During training, the user receives real-time feedback from the server via their terminal, allowing them to adjust their learning direction and effectively improve their skills. Upon completion of training, the user can set their next goals based on the evaluation results generated by the server and strive for continuous skill improvement.

[0471] This invention enables users to learn efficiently and effectively through the provision of personalized training programs and real-time immediate feedback.

[0472] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0473] Step 1:

[0474] The user uses a terminal to input their training goals and attribute information. This data includes information such as "improving language skills" and "flexible learning style." The terminal formats this information into a prompt and sends it to the server.

[0475] Step 2:

[0476] The server activates a generative AI model based on the received prompt message. The AI ​​model analyzes the input user information and constructs an optimal training plan for the user from a large dataset. This process uses a learning algorithm to select content that matches the user's attributes. The generated plan is then reformatted and sent to the terminal.

[0477] Step 3:

[0478] The device receives the training plan sent from the server and displays it to the user in a visual and interactive format. The user begins training according to the presented program, making selections and inputs as instructed. At this stage, learning progresses through interactive content in audio, video, and text formats.

[0479] Step 4:

[0480] During training, the device records the user's input and selected options in real time and sends them to the server. This data includes the user's response history and learning progress, which the server uses to generate immediate feedback for the user.

[0481] Step 5:

[0482] The server analyzes the collected user data and adjusts the training program to help improve the user's skills. Feedback is sent to the terminal in real time and shown to the user. Based on this feedback, the user adjusts their next learning step as they progress.

[0483] Step 6:

[0484] Upon completing the training, users receive their final evaluation results from the server and set their next goals. The server then optimizes the next training plan based on the evaluation, becoming a platform that supports the user's continuous skill improvement.

[0485] (Application Example 1)

[0486] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0487] Conventional training systems have faced challenges in providing optimal training content tailored to individual user characteristics and objectives, as well as insufficient real-time feedback. In particular, when aiming to improve specific movements such as those in sports, there is a need to instantly analyze the user's movements and provide accurate advice.

[0488] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0489] In this invention, the server includes means for generating individualized training content based on the user's objectives and characteristics, means for providing the generated training content to a receiving device, and means for analyzing the user's actions in real time and generating immediate feedback. This enables the provision of optimal training tailored to the individual user's characteristics in real time, allowing for effective skill improvement.

[0490] A "user" is an individual who aims to improve their skills and abilities by using a training system.

[0491] "Means for generating individualized training content based on objectives and characteristics" refers to a function for creating training programs optimized for the different goals and characteristics of each user.

[0492] "Means of providing to the receiving device" refers to a method of distributing training content generated on the server to a device that the user accesses.

[0493] "A means of analyzing actions in real time and generating immediate feedback" refers to a function that analyzes the user's actions during training and instantly provides advice and improvement suggestions based on the results.

[0494] "A means of monitoring and providing feedback in real time" refers to a function that monitors user behavior and provides immediate feedback based on the results.

[0495] As a specific embodiment of this invention, an AI-powered training system is constructed. The server generates individual training programs based on the user's objectives and characteristics. The server analyzes a large amount of data using a generated AI model, generates a program optimized for the user's specific needs, and provides it to the terminal.

[0496] The server uses AI modeling libraries (e.g., TensorFlow or PyTorch) to design the optimal training to achieve the user's objectives. The training content is provided to the user via a device, which includes smartphones, tablets, and head-mounted displays. The device collects user behavior data in real time and sends it to the server.

[0497] The server performs motion analysis based on the collected data and uses computer vision tools (e.g., OpenCV) to analyze the details of the user's movements. An immediate feedback function quickly provides the user with insights gained during training and identifies areas for improvement.

[0498] As a concrete example, suppose a user wants to improve their soccer dribbling skills. The server analyzes the user's motion data and provides effective feedback in real time. This helps improve their technique by advising the user on which direction they should control the ball.

[0499] An example prompt is: "Use the AI ​​training system to generate a custom program to improve the user's motor skills. In particular, enhance the analysis of dribbling movements and the ability to provide immediate feedback." Through this prompt, the system designs the optimal approach.

[0500] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0501] Step 1:

[0502] The server receives purpose and characteristic information as input from the user. This received information is treated as initial data for analysis by the generative AI model. Using this data, the server prepares to generate individual training programs.

[0503] Step 2:

[0504] The server uses a generative AI model to analyze user input data and generate an optimal training program. Specifically, it determines the skill set and practice content necessary for the user to achieve their goals. The training program obtained through this process is output as data to be sent to the terminal.

[0505] Step 3:

[0506] The terminal receives the training program sent from the server and displays it to the user through an interface. The user then begins training based on this displayed program. The terminal continuously collects user behavior data and sends it to the server.

[0507] Step 4:

[0508] The server analyzes real-time user behavior data collected from the terminal. Computer vision tools are used to evaluate the quality of user actions and areas for improvement. Based on these evaluation results, feedback is generated immediately and sent to the terminal.

[0509] Step 5:

[0510] The device displays feedback sent from the server to the user. Based on this feedback, the user can continuously correct their actions on the spot. Since feedback is received in real time, the training progress can be adjusted as needed.

[0511] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0512] This invention is a virtual training system that combines an emotion engine that recognizes the user's emotional state, and provides a training experience tailored to the user's characteristics. This embodiment will be described in detail.

[0513] This system includes servers, terminals, users, and an emotion engine as its main components.

[0514] The server functions as a central processing unit responsible for generating and controlling training programs and processing user data. The server receives sentiment analysis results from the sentiment engine along with user characteristic information, and dynamically adjusts the training program based on this. It also receives user behavior data during training and performs analysis to provide real-time feedback.

[0515] The terminal is a device that the user uses for training, and can be a smartphone, PC, or VR device. During training, the terminal collects the user's input and actions, and the emotion engine obtains the emotional state inferred from the user's facial expressions and voice, and sends it to the server.

[0516] The user is the one who operates this system and receives training. The training is delivered via a terminal, and the user works on individual tasks in real time, receives feedback, and checks their progress.

[0517] An emotion engine is a device or software that analyzes a user's voice and facial expression data to identify their emotional state. This engine analyzes emotions based on data transmitted from the terminal and transmits the results to a server. This information is used to adjust training.

[0518] For example, when a user undergoes training to improve their presentation skills, the emotion engine can recognize the user's emotions, such as anxiety or nervousness. Based on this information, the server adapts the training content in real time. For instance, if the system detects that the user is nervous, it can temporarily lower the difficulty level of the training or provide feedback to encourage relaxation. In this way, a flexible training experience tailored to the user's emotional state can be provided.

[0519] The following describes the processing flow.

[0520] Step 1:

[0521] The user launches the application and logs into the device using their account information.

[0522] Step 2:

[0523] The device sends user information to the server, and past training data and profiles are retrieved from the server.

[0524] Step 3:

[0525] Users set their training objectives and goals, and update their characteristic information as needed.

[0526] Step 4:

[0527] The terminal sends user input information to the server and issues instructions to activate the emotion engine.

[0528] Step 5:

[0529] The emotion engine analyzes the user's emotional state based on facial expression and voice data collected from the device.

[0530] Step 6:

[0531] The server receives data from the emotion engine, combines it with user characteristic information, and generates an appropriate training program.

[0532] Step 7:

[0533] The server sends the generated training program and initial feedback to the terminal.

[0534] Step 8:

[0535] The device displays the training content to the user and prompts them to start the training.

[0536] Step 9:

[0537] The user begins training and works on the assigned tasks.

[0538] Step 10:

[0539] The device records the user's behavior and emotional state in real time during training and sends the data to the server.

[0540] Step 11:

[0541] Based on the data received by the server, it generates real-time feedback tailored to the user's emotional state and sends it to the device.

[0542] Step 12:

[0543] The device displays feedback to the user in real time and adjusts the training content as needed.

[0544] Step 13:

[0545] After training is complete, the server stores the user's training data and uses it to improve the next training plan.

[0546] (Example 2)

[0547] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0548] Modern training systems often lack sufficient customization to consider individual user characteristics and emotional states, instead offering only uniform content. This makes it difficult to provide a flexible training experience that responds to user emotional changes, resulting in challenges in achieving effective results.

[0549] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0550] In this invention, the server includes means for generating individualized training content based on the user's characteristics and emotional state, means for providing the generated training content to an information processing device used by the user, and means for collecting the user's voice and facial expression data during training and identifying the user's emotional state using an emotion engine. This makes it possible to provide individually tailored training content to each user and realize an effective and flexible training experience that responds to the user's emotional state.

[0551] A "user" is an individual who uses this system to receive individual training.

[0552] "Characteristics" refer to individual features of a user, including their personality, abilities, and past achievements.

[0553] "Emotional state" refers to the emotional state a user is experiencing at a particular moment, and is captured through voice and facial expression data.

[0554] "Training content" refers to the collective set of tasks and exercises provided by the system to help users improve specific abilities.

[0555] "Information processing equipment" refers to devices such as smartphones, PCs, and VR devices used by users in a training system.

[0556] "Voice and facial expression data" refers to data that includes information about the user's tone of voice and facial expressions, and forms the basis for analysis by the emotion engine.

[0557] An "emotion engine" is a device or software that analyzes a user's voice and facial expression data to identify their emotional state.

[0558] A "server" is a data processing system that acts as a central processing unit, generating and adjusting training programs based on the results of emotional state analysis.

[0559] This invention provides a system for conducting individually tailored training according to the user's characteristics and emotional state. This system mainly consists of a server, a terminal, and an emotion engine.

[0560] The server functions as the central processing unit of this system. Based on the user's characteristic information, it receives analysis results of emotional states sent from the emotion engine. The server integrates this information and dynamically generates training content that is tailored to the user's real-time emotional state. It also sends the generated training content to the terminal, providing adaptive feedback to the user. The software used includes AI algorithms with advanced data processing capabilities.

[0561] The terminal is an information processing device for users to receive training. Smartphones, PCs, VR devices, etc., are used, and they play a role in transmitting the user's voice and facial expression data to the emotion engine. The terminal also displays feedback sent from the server to the user, supporting continued training. It is equipped with various sensors and communication modules to enable real-time data collection.

[0562] Users work on training tasks provided through their devices and adjust their training based on feedback from the server. Voice and facial expression data collected during training are analyzed in real time by an emotion engine, and the user's emotional state is transmitted to the server.

[0563] The emotion engine consists of software for analyzing the user's voice and facial expression data. It captures the characteristics of the voice and facial expressions to identify the user's current emotional state and sends this information to the server. Specifically, it uses an AI model to quantify the type and intensity of emotions, and uses the results to adjust the training content.

[0564] As a concrete example, consider a scenario where a user is undergoing training to improve their presentation skills. When the user simulates a presentation using their device, the emotion engine detects tremors in the user's voice and tension in their facial expressions. The server uses this information to temporarily modify the presentation content or provide feedback to encourage relaxation.

[0565] An example of a prompt message would be, "Please identify in real time any tension the user exhibits during the presentation and suggest ways to alleviate it." This allows for a flexible and personalized training experience.

[0566] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0567] Step 1:

[0568] The user activates the device and begins a training session. The device prepares to collect voice and facial expression data and establishes an interface for sending data to the emotion engine. The input consists of the user's voice and facial expression data, and the output is this data sent to the emotion engine in real time. Specifically, the device's microphone and camera activate to capture the user's voice and facial expressions.

[0569] Step 2:

[0570] The device transmits the collected user voice and facial expression data to the emotion engine. The emotion engine analyzes this data to identify the user's emotional state. The input is the user's raw data (voice, facial expressions), and the output is an analysis result indicating the emotional state. The emotional state is quantified, and the type and intensity of the emotion the user is experiencing are determined. Specifically, an AI algorithm identifies data features and evaluates the emotional state.

[0571] Step 3:

[0572] The server receives the results of an emotional state analysis sent from the emotion engine. Next, it dynamically generates and adjusts the training content by combining this with the user's characteristic information (past training results, goals, etc.). The input includes the emotional analysis results and user characteristic information, and the output is an adjusted training program. The server integrates this information and performs specific actions to create user-specific training content in real time.

[0573] Step 4:

[0574] The server sends the generated training program and feedback to the terminal. The terminal presents the received information to the user visually and audibly, supporting the progress of the training. The input is the training program and feedback from the server, and the output is a visual and audible display to the user. Specific actions include displaying training instructions on the terminal's display and playing audio feedback.

[0575] Step 5:

[0576] Users train by following tasks provided through the device. They receive feedback and use it to improve their subsequent efforts. Input is feedback and training instructions from the device, and output is the improvement in the user's training performance and emotional state. Specifically, users perform the presented tasks and adaptively incorporate the feedback.

[0577] (Application Example 2)

[0578] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0579] Training conducted without considering user emotions makes it difficult to provide an effective learning experience. This is especially true in retail settings such as customer service, where understanding staff emotional states and providing appropriate feedback is crucial. However, current systems struggle to adapt in real time. Therefore, the challenge lies in enabling the provision of flexible training plans that respond to emotional states.

[0580] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0581] In this invention, the server includes means for recognizing the user's emotional state and flexibly adjusting the first section based on it, means for generating individualized training content based on the user's purpose and characteristics, and means for monitoring the user's behavior in real time during training and providing emotionally appropriate feedback. This makes it possible to provide an optimal training experience tailored to the user's emotions and to support the improvement of customer service skills in physical stores in real time.

[0582] "Emotional state" refers to the user's psychological and emotional state, and is primarily identified through the analysis of voice and facial expressions.

[0583] "Training content" refers to a series of activities and instructions aimed at improving the user's abilities, and is customized based on the user's goals and characteristics.

[0584] "Device" refers to the device that the user uses for training, and includes smartphones, PCs, VR devices, etc.

[0585] "Monitoring" refers to the process of observing and analyzing user behavior and reactions in real time.

[0586] "Feedback" refers to the reactions and advice provided to the user, and is conducted in real time with the aim of assisting the progress of the training.

[0587] This invention functions as a system to support training in the service industry. The server is responsible for recognizing the user's emotional state in real time and adjusting the training content accordingly. The emotional state is analyzed from voice and facial expression data collected by a terminal. The terminal can be, for example, smart glasses or a smartphone. These terminals capture the user's voice and facial expressions, and the data is sent to the server.

[0588] On the server, Python's sentiment analysis library and OpenCV are used for sentiment analysis, and the Google Cloud Speech-to-Text API is used for speech analysis. Based on behavioral and sentiment data, real-time feedback is generated and provided to the user.

[0589] For example, if a customer service staff member feels nervous around a customer, the server generates advice to help alleviate that tension. For instance, feedback such as "Try speaking a little more slowly" might be displayed on the smart glasses' screen.

[0590] An example of a prompt might be, "Analyze the emotional state of the current customer service interaction and provide the staff with the most appropriate advice in real time." Using this prompt, the AI ​​model can be instructed to generate feedback based on the results of the emotional analysis.

[0591] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0592] Step 1:

[0593] The device captures the user's voice and facial expressions in real time. It acquires the user's video and audio data as input, analyzes facial expressions using OpenCV, and converts the audio to text using the Google Cloud Speech-to-Text API. This generates initial emotional state data.

[0594] Step 2:

[0595] The server receives emotional state data sent from the terminal. The inputs here are analyzed facial expression data and voice-to-text data. The server uses a Python emotion analysis library to estimate the emotional state and stores the results in internal data. The output generates the user's tension level and the ratio of positive to negative emotions.

[0596] Step 3:

[0597] The server adjusts the training content based on the emotional state. The input is the emotional state data obtained in step 2. A generative AI model is used to create training feedback based on the obtained emotional data. The output is a feedback message, which is displayed in the user interface.

[0598] Step 4:

[0599] Feedback is provided to the user through the device. The input here is the feedback message generated in step 3. Visual feedback is immediately displayed on the device's display, for example, the screen of smart glasses. The user can then use this to improve their training.

[0600] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0601] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0602] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0603] [Fourth Embodiment]

[0604] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0605] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0606] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0607] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0608] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0609] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0610] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0611] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0612] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0613] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0614] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0615] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0616] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0617] The present invention is an AI-powered virtual training system that enables users to receive individually customized training programs. Embodiments of the present invention are described below.

[0618] This system is primarily composed of three main elements: servers, terminals, and users.

[0619] The server functions as a central processing unit, receiving user-specified objectives and characteristic information, and utilizing an AI model to generate an optimal training program. The generated program undergoes necessary processing on the server to be delivered in a format suitable for the user. The training content is then transmitted to the terminal. The server also has the capability to analyze user data collected in real time during training and generate immediate feedback.

[0620] The terminal is a device used by the user as an interface and can take various forms, such as a PC, smartphone, or VR equipment. This terminal receives training programs sent from the server and displays them so that the user can execute them. In this process, the terminal plays a role in ensuring the immediacy of feedback by continuously sending user input and behavioral data to the server in real time.

[0621] A user is an individual who receives training using this system. After setting their goals within the system, the user begins training. The system provides a program based on the user's goals, and the user trains according to that program. During training, the user receives feedback through a terminal to help improve their skills. After training, the user can reset their goals based on the evaluation results provided by the server and proceed to new training.

[0622] For example, if a user desires training to improve their language skills, the server uses AI to analyze a large amount of learning material and practice problems, generating a learning plan optimized for the user's characteristics. The generated plan is provided to the user in an interactive format via their device, allowing them to learn and check their progress on the spot. The user's answers and learning process are analyzed in real time, and immediate feedback is provided, enabling efficient skill improvement.

[0623] The following describes the processing flow.

[0624] Step 1:

[0625] The user launches the application and enters their account information on the login screen.

[0626] Step 2:

[0627] The terminal sends the entered login information to the server for authentication. The server verifies the information and grants permission to log in.

[0628] Step 3:

[0629] The user enters their training objectives and personal information (e.g., current skill level and areas of interest).

[0630] Step 4:

[0631] The terminal sends the information entered by the user to the server.

[0632] Step 5:

[0633] The server uses an AI model based on the information it receives to generate a customized training program for each user.

[0634] Step 6:

[0635] The server sends the generated training program to the terminal.

[0636] Step 7:

[0637] The device displays the training program to the user and prepares to begin training.

[0638] Step 8:

[0639] Users can start training at any time they choose and work on the displayed tasks and practice problems.

[0640] Step 9:

[0641] The terminal sends user actions and responses to the server in real time.

[0642] Step 10:

[0643] The server analyzes user data received in real time and generates feedback.

[0644] Step 11:

[0645] The server sends the generated feedback to the terminal and provides it to the user.

[0646] Step 12:

[0647] Users provide feedback, which is then used to adjust the content and approach of the next training session.

[0648] Step 13:

[0649] After training is complete, the server stores the user's training data and uses it to improve the next training plan.

[0650] (Example 1)

[0651] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0652] Conventional training systems have struggled to provide efficient and personalized training programs tailored to each user's individual goals and characteristics, and have also made it difficult to obtain real-time feedback. As a result, the learning effect of users has been limited, and achieving sustained skill improvement has been a challenge.

[0653] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0654] In this invention, the server includes means for generating an individualized training program using a generated AI model based on the user's goals and attribute information; means for transmitting and displaying the generated program on the user's information processing device; and means for collecting the user's actions in real time during training and providing immediate feedback. This enables flexible and effective training and feedback tailored to the user.

[0655] A "user" is an individual who receives training using this system and is the entity that operates the system to achieve a specific objective.

[0656] A "training program" is a series of instructional materials designed to facilitate learning and skill improvement, generated based on the user's characteristics.

[0657] A "generative AI model" is an algorithm that uses artificial intelligence technology to analyze data and create an optimal training program tailored to user characteristics.

[0658] An "information processing device" is a device that includes an interface that the user directly operates and has the function of displaying and operating programs from a server.

[0659] "Feedback" refers to comments and evaluations provided in real time during a user's training process, and is information used to adjust the direction of learning.

[0660] "Real-time" refers to a data processing method that prioritizes immediacy, where information is processed, transmitted, and received almost simultaneously.

[0661] This invention is a system for providing training programs tailored to the individual needs of users, and consists of three elements: a server, a terminal, and a user.

[0662] The server plays a central role in this system. The server collects user-defined goals and attribute information and generates individual training programs using advanced generative AI models. These AI models are implemented using widely used frameworks such as TensorFlow and PyTorch. The generated programs are formatted to the optimal format according to the user's attributes and sent to the terminal.

[0663] For example, if a user enters a prompt such as "Generate a customized training plan to improve my language skills," the server will analyze a large amount of data based on that request and select the most suitable learning materials and practice problems for the user.

[0664] The terminal is an information processing device that the user directly operates, and can take the form of a PC, smartphone, or VR device. The terminal receives training programs sent from the server and presents them to the user in a visual and interactive format. While the user is performing the training, the terminal plays the role of sending the user's input and behavioral data back to the server in real time.

[0665] The user is the entity that utilizes this system to achieve their objectives. The user sets their own training goals for the system and practices training according to the provided program. During training, the user receives real-time feedback from the server via their terminal, allowing them to adjust their learning direction and effectively improve their skills. Upon completion of training, the user can set their next goals based on the evaluation results generated by the server and strive for continuous skill improvement.

[0666] This invention enables users to learn efficiently and effectively through the provision of personalized training programs and real-time immediate feedback.

[0667] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0668] Step 1:

[0669] The user uses a terminal to input their training goals and attribute information. This data includes information such as "improving language skills" and "flexible learning style." The terminal formats this information into a prompt and sends it to the server.

[0670] Step 2:

[0671] The server activates a generative AI model based on the received prompt message. The AI ​​model analyzes the input user information and constructs an optimal training plan for the user from a large dataset. This process uses a learning algorithm to select content that matches the user's attributes. The generated plan is then reformatted and sent to the terminal.

[0672] Step 3:

[0673] The device receives the training plan sent from the server and displays it to the user in a visual and interactive format. The user begins training according to the presented program, making selections and inputs as instructed. At this stage, learning progresses through interactive content in audio, video, and text formats.

[0674] Step 4:

[0675] During training, the device records the user's input and selected options in real time and sends them to the server. This data includes the user's response history and learning progress, which the server uses to generate immediate feedback for the user.

[0676] Step 5:

[0677] The server analyzes the collected user data and adjusts the training program to help improve the user's skills. Feedback is sent to the terminal in real time and shown to the user. Based on this feedback, the user adjusts their next learning step as they progress.

[0678] Step 6:

[0679] Upon completing the training, users receive their final evaluation results from the server and set their next goals. The server then optimizes the next training plan based on the evaluation, becoming a platform that supports the user's continuous skill improvement.

[0680] (Application Example 1)

[0681] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0682] Conventional training systems have faced challenges in providing optimal training content tailored to individual user characteristics and objectives, as well as insufficient real-time feedback. In particular, when aiming to improve specific movements such as those in sports, there is a need to instantly analyze the user's movements and provide accurate advice.

[0683] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0684] In this invention, the server includes means for generating individualized training content based on the user's objectives and characteristics, means for providing the generated training content to a receiving device, and means for analyzing the user's actions in real time and generating immediate feedback. This enables the provision of optimal training tailored to the individual user's characteristics in real time, allowing for effective skill improvement.

[0685] A "user" is an individual who aims to improve their skills and abilities by using a training system.

[0686] "Means for generating individualized training content based on objectives and characteristics" refers to a function for creating training programs optimized for the different goals and characteristics of each user.

[0687] "Means of providing to the receiving device" refers to a method of distributing training content generated on the server to a device that the user accesses.

[0688] "A means of analyzing actions in real time and generating immediate feedback" refers to a function that analyzes the user's actions during training and instantly provides advice and improvement suggestions based on the results.

[0689] "A means of monitoring and providing feedback in real time" refers to a function that monitors user behavior and provides immediate feedback based on the results.

[0690] As a specific embodiment of this invention, an AI-powered training system is constructed. The server generates individual training programs based on the user's objectives and characteristics. The server analyzes a large amount of data using a generated AI model, generates a program optimized for the user's specific needs, and provides it to the terminal.

[0691] The server uses AI modeling libraries (e.g., TensorFlow or PyTorch) to design the optimal training to achieve the user's objectives. The training content is provided to the user via a device, which includes smartphones, tablets, and head-mounted displays. The device collects user behavior data in real time and sends it to the server.

[0692] The server performs motion analysis based on the collected data and uses computer vision tools (e.g., OpenCV) to analyze the details of the user's movements. An immediate feedback function quickly provides the user with insights gained during training and identifies areas for improvement.

[0693] As a concrete example, suppose a user wants to improve their soccer dribbling skills. The server analyzes the user's motion data and provides effective feedback in real time. This helps improve their technique by advising the user on which direction they should control the ball.

[0694] An example prompt is: "Use the AI ​​training system to generate a custom program to improve the user's motor skills. In particular, enhance the analysis of dribbling movements and the ability to provide immediate feedback." Through this prompt, the system designs the optimal approach.

[0695] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0696] Step 1:

[0697] The server receives purpose and characteristic information as input from the user. This received information is treated as initial data for analysis by the generative AI model. Using this data, the server prepares to generate individual training programs.

[0698] Step 2:

[0699] The server uses a generative AI model to analyze user input data and generate an optimal training program. Specifically, it determines the skill set and practice content necessary for the user to achieve their goals. The training program obtained through this process is output as data to be sent to the terminal.

[0700] Step 3:

[0701] The terminal receives the training program sent from the server and displays it to the user through an interface. The user then begins training based on this displayed program. The terminal continuously collects user behavior data and sends it to the server.

[0702] Step 4:

[0703] The server analyzes real-time user behavior data collected from the terminal. Computer vision tools are used to evaluate the quality of user actions and areas for improvement. Based on these evaluation results, feedback is generated immediately and sent to the terminal.

[0704] Step 5:

[0705] The device displays feedback sent from the server to the user. Based on this feedback, the user can continuously correct their actions on the spot. Since feedback is received in real time, the training progress can be adjusted as needed.

[0706] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0707] This invention is a virtual training system that combines an emotion engine that recognizes the user's emotional state, and provides a training experience tailored to the user's characteristics. This embodiment will be described in detail.

[0708] This system includes servers, terminals, users, and an emotion engine as its main components.

[0709] The server functions as a central processing unit responsible for generating and controlling training programs and processing user data. The server receives sentiment analysis results from the sentiment engine along with user characteristic information, and dynamically adjusts the training program based on this. It also receives user behavior data during training and performs analysis to provide real-time feedback.

[0710] The terminal is a device that the user uses for training, and can be a smartphone, PC, or VR device. During training, the terminal collects the user's input and actions, and the emotion engine obtains the emotional state inferred from the user's facial expressions and voice, and sends it to the server.

[0711] The user is the one who operates this system and receives training. The training is delivered via a terminal, and the user works on individual tasks in real time, receives feedback, and checks their progress.

[0712] An emotion engine is a device or software that analyzes a user's voice and facial expression data to identify their emotional state. This engine analyzes emotions based on data transmitted from the terminal and transmits the results to a server. This information is used to adjust training.

[0713] For example, when a user undergoes training to improve their presentation skills, the emotion engine can recognize the user's emotions, such as anxiety or nervousness. Based on this information, the server adapts the training content in real time. For instance, if the system detects that the user is nervous, it can temporarily lower the difficulty level of the training or provide feedback to encourage relaxation. In this way, a flexible training experience tailored to the user's emotional state can be provided.

[0714] The following describes the processing flow.

[0715] Step 1:

[0716] The user launches the application and logs into the device using their account information.

[0717] Step 2:

[0718] The device sends user information to the server, and past training data and profiles are retrieved from the server.

[0719] Step 3:

[0720] Users set their training objectives and goals, and update their characteristic information as needed.

[0721] Step 4:

[0722] The terminal sends user input information to the server and issues instructions to activate the emotion engine.

[0723] Step 5:

[0724] The emotion engine analyzes the user's emotional state based on facial expression and voice data collected from the device.

[0725] Step 6:

[0726] The server receives data from the emotion engine, combines it with user characteristic information, and generates an appropriate training program.

[0727] Step 7:

[0728] The server sends the generated training program and initial feedback to the terminal.

[0729] Step 8:

[0730] The device displays the training content to the user and prompts them to start the training.

[0731] Step 9:

[0732] The user begins training and works on the assigned tasks.

[0733] Step 10:

[0734] The device records the user's behavior and emotional state in real time during training and sends the data to the server.

[0735] Step 11:

[0736] Based on the data received by the server, it generates real-time feedback tailored to the user's emotional state and sends it to the device.

[0737] Step 12:

[0738] The device displays feedback to the user in real time and adjusts the training content as needed.

[0739] Step 13:

[0740] After training is complete, the server stores the user's training data and uses it to improve the next training plan.

[0741] (Example 2)

[0742] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0743] Modern training systems often lack sufficient customization to consider individual user characteristics and emotional states, instead offering only uniform content. This makes it difficult to provide a flexible training experience that responds to user emotional changes, resulting in challenges in achieving effective results.

[0744] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0745] In this invention, the server includes means for generating individualized training content based on the user's characteristics and emotional state, means for providing the generated training content to an information processing device used by the user, and means for collecting the user's voice and facial expression data during training and identifying the user's emotional state using an emotion engine. This makes it possible to provide individually tailored training content to each user and realize an effective and flexible training experience that responds to the user's emotional state.

[0746] A "user" is an individual who uses this system to receive individual training.

[0747] "Characteristics" refer to individual features of a user, including their personality, abilities, and past achievements.

[0748] "Emotional state" refers to the emotional state a user is experiencing at a particular moment, and is captured through voice and facial expression data.

[0749] "Training content" refers to the collective set of tasks and exercises provided by the system to help users improve specific abilities.

[0750] "Information processing equipment" refers to devices such as smartphones, PCs, and VR devices used by users in a training system.

[0751] "Voice and facial expression data" refers to data that includes information about the user's tone of voice and facial expressions, and forms the basis for analysis by the emotion engine.

[0752] An "emotion engine" is a device or software that analyzes a user's voice and facial expression data to identify their emotional state.

[0753] A "server" is a data processing system that acts as a central processing unit, generating and adjusting training programs based on the results of emotional state analysis.

[0754] This invention provides a system for conducting individually tailored training according to the user's characteristics and emotional state. This system mainly consists of a server, a terminal, and an emotion engine.

[0755] The server functions as the central processing unit of this system. Based on the user's characteristic information, it receives analysis results of emotional states sent from the emotion engine. The server integrates this information and dynamically generates training content that is tailored to the user's real-time emotional state. It also sends the generated training content to the terminal, providing adaptive feedback to the user. The software used includes AI algorithms with advanced data processing capabilities.

[0756] The terminal is an information processing device for users to receive training. Smartphones, PCs, VR devices, etc., are used, and they play a role in transmitting the user's voice and facial expression data to the emotion engine. The terminal also displays feedback sent from the server to the user, supporting continued training. It is equipped with various sensors and communication modules to enable real-time data collection.

[0757] Users work on training tasks provided through their devices and adjust their training based on feedback from the server. Voice and facial expression data collected during training are analyzed in real time by an emotion engine, and the user's emotional state is transmitted to the server.

[0758] The emotion engine consists of software for analyzing the user's voice and facial expression data. It captures the characteristics of the voice and facial expressions to identify the user's current emotional state and sends this information to the server. Specifically, it uses an AI model to quantify the type and intensity of emotions, and uses the results to adjust the training content.

[0759] As a concrete example, consider a scenario where a user is undergoing training to improve their presentation skills. When the user simulates a presentation using their device, the emotion engine detects tremors in the user's voice and tension in their facial expressions. The server uses this information to temporarily modify the presentation content or provide feedback to encourage relaxation.

[0760] An example of a prompt message would be, "Please identify in real time any tension the user exhibits during the presentation and suggest ways to alleviate it." This allows for a flexible and personalized training experience.

[0761] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0762] Step 1:

[0763] The user activates the device and begins a training session. The device prepares to collect voice and facial expression data and establishes an interface for sending data to the emotion engine. The input consists of the user's voice and facial expression data, and the output is this data sent to the emotion engine in real time. Specifically, the device's microphone and camera activate to capture the user's voice and facial expressions.

[0764] Step 2:

[0765] The device transmits the collected user voice and facial expression data to the emotion engine. The emotion engine analyzes this data to identify the user's emotional state. The input is the user's raw data (voice, facial expressions), and the output is an analysis result indicating the emotional state. The emotional state is quantified, and the type and intensity of the emotion the user is experiencing are determined. Specifically, an AI algorithm identifies data features and evaluates the emotional state.

[0766] Step 3:

[0767] The server receives the results of an emotional state analysis sent from the emotion engine. Next, it dynamically generates and adjusts the training content by combining this with the user's characteristic information (past training results, goals, etc.). The input includes the emotional analysis results and user characteristic information, and the output is an adjusted training program. The server integrates this information and performs specific actions to create user-specific training content in real time.

[0768] Step 4:

[0769] The server sends the generated training program and feedback to the terminal. The terminal presents the received information to the user visually and audibly, supporting the progress of the training. The input is the training program and feedback from the server, and the output is a visual and audible display to the user. Specific actions include displaying training instructions on the terminal's display and playing audio feedback.

[0770] Step 5:

[0771] Users train by following tasks provided through the device. They receive feedback and use it to improve their subsequent efforts. Input is feedback and training instructions from the device, and output is the improvement in the user's training performance and emotional state. Specifically, users perform the presented tasks and adaptively incorporate the feedback.

[0772] (Application Example 2)

[0773] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0774] Training conducted without considering user emotions makes it difficult to provide an effective learning experience. This is especially true in retail settings such as customer service, where understanding staff emotional states and providing appropriate feedback is crucial. However, current systems struggle to adapt in real time. Therefore, the challenge lies in enabling the provision of flexible training plans that respond to emotional states.

[0775] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0776] In this invention, the server includes means for recognizing the user's emotional state and flexibly adjusting the first section based on it, means for generating individualized training content based on the user's purpose and characteristics, and means for monitoring the user's behavior in real time during training and providing emotionally appropriate feedback. This makes it possible to provide an optimal training experience tailored to the user's emotions and to support the improvement of customer service skills in physical stores in real time.

[0777] "Emotional state" refers to the user's psychological and emotional state, and is primarily identified through the analysis of voice and facial expressions.

[0778] "Training content" refers to a series of activities and instructions aimed at improving the user's abilities, and is customized based on the user's goals and characteristics.

[0779] "Device" refers to the device that the user uses for training, and includes smartphones, PCs, VR devices, etc.

[0780] "Monitoring" refers to the process of observing and analyzing user behavior and reactions in real time.

[0781] "Feedback" refers to the reactions and advice provided to the user, and is conducted in real time with the aim of assisting the progress of the training.

[0782] This invention functions as a system to support training in the service industry. The server is responsible for recognizing the user's emotional state in real time and adjusting the training content accordingly. The emotional state is analyzed from voice and facial expression data collected by a terminal. The terminal can be, for example, smart glasses or a smartphone. These terminals capture the user's voice and facial expressions, and the data is sent to the server.

[0783] On the server, Python's sentiment analysis library and OpenCV are used for sentiment analysis, and the Google Cloud Speech-to-Text API is used for speech analysis. Based on behavioral and sentiment data, real-time feedback is generated and provided to the user.

[0784] For example, if a customer service staff member feels nervous around a customer, the server generates advice to help alleviate that tension. For instance, feedback such as "Try speaking a little more slowly" might be displayed on the smart glasses' screen.

[0785] An example of a prompt might be, "Analyze the emotional state of the current customer service interaction and provide the staff with the most appropriate advice in real time." Using this prompt, the AI ​​model can be instructed to generate feedback based on the results of the emotional analysis.

[0786] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0787] Step 1:

[0788] The device captures the user's voice and facial expressions in real time. It acquires the user's video and audio data as input, analyzes facial expressions using OpenCV, and converts the audio to text using the Google Cloud Speech-to-Text API. This generates initial emotional state data.

[0789] Step 2:

[0790] The server receives emotional state data sent from the terminal. The inputs here are analyzed facial expression data and voice-to-text data. The server uses a Python emotion analysis library to estimate the emotional state and stores the results in internal data. The output generates the user's tension level and the ratio of positive to negative emotions.

[0791] Step 3:

[0792] The server adjusts the training content based on the emotional state. The input is the emotional state data obtained in step 2. A generative AI model is used to create training feedback based on the obtained emotional data. The output is a feedback message, which is displayed in the user interface.

[0793] Step 4:

[0794] Feedback is provided to the user through the device. The input here is the feedback message generated in step 3. Visual feedback is immediately displayed on the device's display, for example, the screen of smart glasses. The user can then use this to improve their training.

[0795] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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

[0797] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0798] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0799] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0800] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0801] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0802] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0803] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0804] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0805] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0806] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0807] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0808] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0809] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0810] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0811] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0812] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0813] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0814] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0815] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0816] The following is further disclosed regarding the embodiments described above.

[0817] (Claim 1)

[0818] A means for generating individualized training content based on the user's objectives and characteristics,

[0819] A means of providing the generated training content to the user's device,

[0820] A means of monitoring user behavior in real time during training and providing feedback,

[0821] A means of analyzing user training results and improving the next training plan,

[0822] A system that includes this.

[0823] (Claim 2)

[0824] The system according to claim 1, comprising means for receiving user goal setting information and customizing training content based on that information.

[0825] (Claim 3)

[0826] The system according to claim 1, comprising means for analyzing user behavior data collected in real time and providing interactive feedback during training.

[0827] "Example 1"

[0828] (Claim 1)

[0829] A means for generating an individualized training program using an AI model based on the user's goals and attribute information,

[0830] Means for transmitting and displaying the generated program on the user's information processing device,

[0831] A means of collecting user actions in real time during training and providing immediate feedback,

[0832] The collected data is analyzed to improve the next training plan,

[0833] A system that includes this.

[0834] (Claim 2)

[0835] The system according to claim 1, comprising means for receiving user objective setting information and adjusting the training plan based thereon.

[0836] (Claim 3)

[0837] The system according to claim 1, comprising means for analyzing user behavior data acquired in real time and providing bidirectional feedback during training.

[0838] "Application Example 1"

[0839] (Claim 1)

[0840] A means for generating individualized training content based on the user's objectives and characteristics,

[0841] Means for providing the generated training content to a receiving device,

[0842] A means of monitoring user behavior in real time during training and providing feedback,

[0843] A means to analyze the user's training results and improve the next training plan,

[0844] A means of analyzing user behavior in real time and generating immediate feedback,

[0845] A system that includes this.

[0846] (Claim 2)

[0847] The system according to claim 1, comprising means for receiving user goal setting information and optimizing training content based on that information.

[0848] (Claim 3)

[0849] The system according to claim 1, comprising means for analyzing collected user behavior data and providing interactive feedback during training.

[0850] "Example 2 of combining an emotion engine"

[0851] (Claim 1)

[0852] A means for generating individualized training content based on the user's characteristics and emotional state,

[0853] A means for providing the generated training content to an information processing device used by the user,

[0854] A means of collecting user voice and facial expression data during training and identifying the user's emotional state using an emotion engine,

[0855] A means by which the server receives the user's emotional state, adjusts the training content in real time, and generates feedback,

[0856] A system that includes this.

[0857] (Claim 2)

[0858] The system according to claim 1, comprising means for receiving user goal setting information and emotional state, and customizing training content based thereon.

[0859] (Claim 3)

[0860] The system according to claim 1, comprising means for analyzing user voice and facial expression data collected in real time and providing interactive feedback based on training content adjusted by the server.

[0861] "Application example 2 when combining with an emotional engine"

[0862] (Claim 1)

[0863] A means for recognizing the user's emotional state and flexibly adjusting the first section based on that,

[0864] A means for generating individualized training content based on the user's objectives and characteristics,

[0865] A means of providing the generated training content to the user's device,

[0866] A means of monitoring user behavior in real time during training and providing emotionally responsive feedback,

[0867] A means of analyzing user training results and improving the next training plan,

[0868] A system that includes this.

[0869] (Claim 2)

[0870] The system according to claim 1, comprising means for receiving user goal setting information and customizing training content based on that information.

[0871] (Claim 3)

[0872] The system according to claim 1, comprising means for analyzing user behavior data and emotional data collected in real time and providing interactive feedback during training. [Explanation of Symbols]

[0873] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for generating individualized training content based on the user's objectives and characteristics, A means of providing the generated training content to the user's device, A means of monitoring user behavior in real time during training and providing feedback, A means of analyzing user training results and improving the next training plan, A system that includes this.

2. The system according to claim 1, comprising means for receiving user goal setting information and customizing training content based on that information.

3. The system according to claim 1, comprising means for analyzing user behavior data collected in real time and providing interactive feedback during training.

Images