system

Abstract

We provide the system. [Solution] A means of presenting instructional videos selected by the user, Means for recording user imitation behavior, A means for comparing recorded imitative behavior with instructional videos to generate evaluations and areas for improvement, A means of presenting evaluations and areas for improvement to users, When imitative behavior meets the evaluation criteria, a means of providing a setting for interactive training is provided. 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 persona chatbot control method 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 chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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] In the training of salespersons, educators, and new employees, it is required to evaluate and improve performance effectively and objectively. However, in the current training methods, it is difficult to practice according to individual progress, and there is a problem that feedback is insufficient. Also, in the improvement of individual skills such as those aspiring to be voice actors, there is a problem that the means for objectively analyzing one's own performance is limited.

Means for Solving the Problems

[0005] This invention provides means for presenting a user-selected instructional video and recording the user's actions as they imitate it. The recorded imitative actions are compared with the instructional video, and speech recognition and image analysis technologies are used to generate a scored evaluation and specific areas for improvement. These evaluations and areas for improvement are then presented to the user, providing effective feedback. Furthermore, if the imitative actions meet the evaluation criteria, an interactive training environment can be provided to improve practical skills. This supports individual skill improvement and enables efficient and continuous skill acquisition.

[0006] A "user" is an entity that uses the system to watch instructional videos and then imitates those videos.

[0007] "Instructional videos" refer to video content presented as examples for users to imitate.

[0008] "Imitative behavior" refers to a series of actions in which a user attempts to imitate and reproduce the content of an instructional video, such as the tone of voice and facial expressions.

[0009] "Recording" refers to the process of saving the user's mimicked behavior through the camera and microphone.

[0010] "Comparison" refers to the process of comparing recorded imitation behavior data with instructional video data and analyzing similarities and differences.

[0011] "Evaluation" refers to the process of providing scores and feedback based on established criteria for imitative behavior.

[0012] "Areas for improvement" refers to specific points or suggestions provided to improve imitation behavior.

[0013] "Interactive training" refers to providing users with opportunities to practice interacting with a virtual customer. [Brief explanation of the drawing]

[0014] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. <L [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined. [[ID=LL

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

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

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

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

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

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

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

[0022] [First Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0035] This invention is a training system for users to improve specific skills and abilities. This system is implemented by connecting a user terminal to a server via a network. The user uses their terminal to select and view the necessary instructional videos.

[0036] The server streams instructional videos selected by the user to the device. These videos include both audio and video, allowing the user to learn from the content, tone of voice, facial expressions, and other details. The user then imitates what they have learned and records their imitated actions using the camera and microphone on their device.

[0037] Recorded imitation behaviors are sent from the terminal to the server. On the server side, speech recognition and image analysis technologies are used to compare the imitation behaviors with instructional videos and analyze their accuracy and similarity. Based on the results of this analysis, the server generates an evaluation of the imitation behavior and specific areas for improvement.

[0038] The evaluation results and areas for improvement are sent to the user's device, allowing them to visually review them. This enables the user to understand their progress and initiate further improvements.

[0039] Furthermore, if the imitation accuracy meets a certain standard, the server will provide an interactive training session. In this session, the AI ​​avatar will interact with the user as a customer, allowing for practice in realistic explanations and presentations.

[0040] For example, if a new sales employee wants to improve their skills in introducing a new product to a customer, they would first watch a training video on product introductions and then practice by imitating it. The system would then evaluate the results and suggest areas for improvement, providing training that more closely resembles a real-world situation. This allows users to objectively analyze their individual performance and efficiently improve their skills.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The user logs into the system using their device. The server verifies the user's authentication information and provides the device with a list of instructional videos that the user can access.

[0044] Step 2:

[0045] The user selects an instructional video through their device. The server generates a streaming URL for the selected video and sends it to the device. The device then uses this URL to play the video.

[0046] Step 3:

[0047] Users watch instructional videos, learning from the content, tone of voice, and facial expressions. They then use their own camera and microphone to record themselves imitating the actions shown in the videos.

[0048] Step 4:

[0049] The device records the user's mimicked behavior and sends the data to the server. The server stores the received recorded data.

[0050] Step 5:

[0051] The server uses speech recognition and image analysis technologies to compare recorded data with instructional videos. Specifically, it analyzes voice tone, speaking speed, and the degree of matching of facial expressions.

[0052] Step 6:

[0053] Based on the analysis results, the server scores and evaluates the user's imitation behavior. It also generates specific points for improvement.

[0054] Step 7:

[0055] The server sends evaluation results and areas for improvement to the terminal. The terminal displays this information to the user, allowing the user to check their own performance.

[0056] Step 8:

[0057] Based on feedback from the server, users set their next practice goals and work towards further improvement.

[0058] Step 9:

[0059] If the user's mimicry meets certain criteria, the server prepares to start an interactive training session using the AI ​​avatar.

[0060] Step 10:

[0061] The device provides users with an interactive practice environment through an AI avatar, enhancing their smooth communication skills in realistic scenarios.

[0062] (Example 1)

[0063] 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."

[0064] Traditional skill improvement systems primarily rely on self-assessment by users, making it difficult to receive objective feedback or detailed improvement strategies. Furthermore, the accuracy of imitation is unclear, making it difficult to determine how further improvement is possible. As a result, there is a problem in that users' skill improvement is not efficient.

[0065] 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.

[0066] In this invention, the server includes means for providing educational videos selected by the user, means for recording the user's imitation behavior, means for comparing the recorded imitation behavior with the educational videos and generating evaluations and areas for improvement, means for providing the evaluations and areas for improvement to the user, and means for generating specific feedback using generative AI technology. This allows the user to objectively understand the accuracy of their imitation and obtain detailed feedback and areas for improvement.

[0067] "Educational videos" are videos containing visual and auditory information designed to help learners acquire specific skills or knowledge.

[0068] "Imitative behavior" refers to self-expressive behaviors such as actions and speech that users perform based on the content of educational videos.

[0069] "Recording" refers to the act of saving imitative behavior as digital data, which is usually done using devices such as cameras and microphones.

[0070] "Evaluation criteria" refer to standards set to quantitatively or qualitatively measure imitative behavior, and serve as criteria for judging accuracy and effectiveness.

[0071] "Interactive learning" refers to a form of learning activity in which users engage interactively, usually through virtual interaction.

[0072] "Generative AI technology" refers to artificial intelligence technology that generates new information based on large amounts of data, and is used to automatically generate specific feedback.

[0073] "Feedback" is the process of evaluating a user's performance and providing suggestions for improvement and advice.

[0074] In this invention, the user first selects and watches an educational video related to a specific skill from a list of videos provided on their device. The device uses its camera and microphone to record the user's imitation behavior. The recorded data is temporarily stored in the device's storage in real time.

[0075] The server provides educational videos selected by the user using streaming technology. Specifically, it uses the H.264 codec for streaming to ensure smooth video playback. In addition, it receives recorded data from the terminal and uses speech recognition APIs and image analysis libraries (such as Google® Speech-to-Text API, OpenCV, and TENSORFLOW®) to analyze the accuracy of imitation behavior and its similarity to the educational video.

[0076] Based on the analysis results, the server utilizes generative AI technology to generate an evaluation of the user's mimicked behavior and specific areas for improvement. The generated evaluation is quantified, and the user can visually view it on their device. Based on the feedback, the user can clearly identify areas for improvement and efficiently improve their skills. By using AI technology, the goal is to make the quality of the feedback equivalent to, or even better than, that provided by human professionals.

[0077] Furthermore, if the user's imitation behavior meets certain evaluation criteria, the server provides an opportunity for interactive learning using an AI avatar. In this interactive session, the AI ​​avatar acts as a virtual customer or questioner, engaging in real-time question-and-answer sessions with the user. For example, in a scenario simulating a new product presentation, the user can practice explaining the product's features.

[0078] When using a generative AI model, you can use prompts like the following: "Which educational videos should I prioritize learning to improve my sales skills?" This prompt can help select educational content that meets the user's specific needs.

[0079] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0080] Step 1:

[0081] The user selects a video related to improving a specific skill from a list of educational videos provided on their device. The input is the user's selection, and the output is a link to a specific educational video based on the user's selection. The device then sends a streaming request to the server based on this link.

[0082] Step 2:

[0083] The server receives streaming requests from users and transmits the corresponding educational videos. The input is the video request received from the user, and the output is compressed video data. The server uses H.264 codec technology to stream the video and ensure smooth playback.

[0084] Step 3:

[0085] The user watches a streamed educational video on their device and imitates its content. The input is the educational video, and the user's actions are imitated. The output is a video and audio recording of the user's imitated actions, recorded using the device's camera and microphone. This data is temporarily stored in the device's storage.

[0086] Step 4:

[0087] The device sends recorded mimicry data to the server. The input is the mimicry data stored on the device, and the output is the data sent to the server. The transmission is done via the HTTPS protocol, ensuring data privacy and security.

[0088] Step 5:

[0089] The server performs speech recognition and image analysis on the received imitation behavior data. The input is imitation behavior data, and the output generates accuracy comparison results with educational videos and similarity analysis results. The server uses technologies such as Google Speech-to-Text API, OpenCV, and TensorFlow to precisely evaluate the imitation behavior.

[0090] Step 6:

[0091] The server utilizes a generated AI model based on the analysis results to evaluate the mimicked behavior and generate specific areas for improvement. The input is the analysis results, and the output is an evaluation score and improvement suggestions. This automatically generates feedback.

[0092] Step 7:

[0093] The evaluation results and improvement suggestions from the server are sent to the user's terminal. The input is the evaluation results and improvement suggestions, and the output is information that the user can visually confirm. Based on this information, the user can improve their actions in the next step.

[0094] Step 8:

[0095] If the imitation behavior meets certain evaluation criteria, the server provides an interactive learning session with an AI avatar. The input is the user's evaluation score, and the output is the initiation of an interactive session. The AI ​​avatar functions as a virtual conversation partner, providing an environment for, for example, a virtual product presentation.

[0096] (Application Example 1)

[0097] 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."

[0098] In modern workplaces, employees are required to acquire new skills quickly and efficiently. Furthermore, the lack of immediate feedback on the job and the inefficiency of traditional training methods are problematic. This invention aims to enable employees to rapidly improve their skills by providing an interactive training method.

[0099] 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.

[0100] In this invention, the server includes a device that presents instructional videos selected by the user, a device that records the user's imitative actions, a device that compares the recorded imitative actions with the instructional videos and generates evaluations and points for improvement, a device that presents the evaluations and points for improvement to the user, a device that provides interactive training with an artificial construct when the imitative actions meet evaluation criteria, and a device that is installed on a visual display device and performs action imitation and evaluation in real time. This enables employees to efficiently learn new technologies, immediately grasp their level of proficiency, and receive specific feedback for further improvement.

[0101] A "device that presents user-selected instructional videos" is a device that allows users to watch videos on their own devices to improve specific skills or abilities.

[0102] A "device for recording user imitation behavior" is a device that records a user's actions and vocalizations as they imitate instructional videos.

[0103] A "device that compares recorded imitative behavior with instructional videos and generates evaluations and areas for improvement" is a device that analyzes the similarity between the imitative behavior recorded by the user and the instructional video, and generates feedback based on the results.

[0104] A "device for presenting evaluations and areas for improvement to users" is a device that presents evaluations based on analysis results and specific improvement suggestions to users visually or audibly.

[0105] A "device that provides interactive training with an artificial construct" is a device that enables an AI or virtual agent to conduct various types of training through real-time interaction with a user.

[0106] A "device installed in a visual display device that performs motion imitation and evaluation in real time" refers to a device installed in smart glasses or a headset that records the user's movements in real time and performs immediate evaluation.

[0107] The system designed to realize this invention basically consists of a user's visual display device and a server. Smart glasses or a headset are used as the visual display device. When the user selects an instructional video, the server streams this video in real time and presents it to the user's visual display device.

[0108] When a user performs an imitative action, the camera and microphone on the visual display device record the action and sound. Once recording is complete, the data is sent to a server. The server then uses speech recognition and image analysis technologies to compare the imitative action with the instructional video. Specifically, Google Cloud Speech-to-Text is used for speech recognition, and OpenCV is applied for image analysis.

[0109] The server generates evaluations and areas for improvement based on these comparison results. These evaluations and areas for improvement are returned to the user's visual display device, allowing the user to visually confirm them. If certain criteria are met, the next step is for the server to provide an interactive training session using the generated AI model. This session allows the user to receive more detailed feedback and interactive training.

[0110] A concrete example is when a factory worker learns how to operate a new machine. The worker watches a training video, imitates the movements, and records them using a visual display device. Afterwards, they receive feedback based on how closely their movements match the original video, and if they perform well, they can receive detailed interactive training from AI.

[0111] An example of a prompt message might be: "I need to learn how to operate the new welding machine. I would like to practice with an AI avatar to find out what specific areas for improvement are needed."

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

[0113] Step 1:

[0114] The user wears a visual display device and selects training instructional videos from a server via a terminal. The server streams the selected instructional videos to the terminal. The input data is the user's selection information, and the output data is the instructional videos displayed on the user's terminal.

[0115] Step 2:

[0116] The user imitates the actions while watching instructional videos. The device records these imitations using a camera and microphone mounted on the visual display device. The input is the user's actions and vocalizations, and the recorded imitation data is the output.

[0117] Step 3:

[0118] The recorded imitation data is sent from the terminal to the server. The server converts the audio data into text data using speech recognition technology (e.g., Google Cloud Speech-to-Text) and extracts motion features from the video data using image analysis technology (e.g., OpenCV). As a result, the imitation data is output as numerically represented analysis data.

[0119] Step 4:

[0120] The server compares the analyzed data with the instructional video and evaluates their similarity. Based on the comparison results, it calculates specific evaluations and areas for improvement. The inputs are the analyzed data and the feature data of the instructional video, and the output is the evaluation and areas for improvement.

[0121] Step 5:

[0122] The server sends data to the terminal to present the calculated evaluation and areas for improvement to the user. The user reviews the feedback through a visual display device. The input is the evaluation and areas for improvement, and the output is their visual display.

[0123] Step 6:

[0124] If the evaluation meets certain criteria, the server starts an interactive conversational training session with an artificial construct using the generated AI model. The user and the AI ​​avatar interact through voice and video. The input is the user's progress, and the output is the experience gained from the conversational training.

[0125] 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.

[0126] This invention combines an imitation training system using instructional videos with an emotion engine that recognizes user emotions, enabling more personalized feedback and interaction. This system operates with a terminal and server working together via a network. Users log into the system using their terminal and select and view instructional videos.

[0127] The server streams the instructional video selected by the user to the device. The user watches it, learns the content, tone of voice, and facial expressions, and records themselves imitating the actions. The recorded data is then sent from the device to the server.

[0128] The server compares this data with instructional videos, using speech recognition and image analysis technologies to evaluate the accuracy of the imitated behavior. It then generates a scored evaluation of the imitated behavior and specific areas for improvement, which are presented to the user on their device. During this process, an emotion engine is used to analyze the user's emotional state, and the evaluation and improvement points are adjusted based on this analysis.

[0129] The emotion engine infers emotions from the user's facial expressions, tone of voice, and other factors. Based on the recognized emotion information, the server optimizes the feedback content for the user. For example, if it determines that the user is tense, it can include suggestions for relaxation techniques. In this way, the evaluation takes the user's emotional state into account, resulting in more flexible and personalized content.

[0130] Furthermore, if the user's mimicked behavior meets the evaluation criteria, the server provides interactive training using an AI avatar. In this training, the AI ​​avatar adjusts its interaction with the user based on emotional information. For example, if the user is feeling down, the avatar is configured to offer words of encouragement.

[0131] As a concrete example, consider a new sales employee practicing customer service. Using this system, the user can understand how their explanation skills, facial expressions, and voice are perceived by customers. Furthermore, the practice content is tailored to the user's emotions at the time, allowing them to steadily hone their skills. In this way, this system supports efficient and effective skill improvement by precisely addressing individual needs.

[0132] The following describes the processing flow.

[0133] Step 1:

[0134] The user logs into the system using their device and selects the video they want to watch from a list of instructional videos. The server then prepares to stream this video data to the device.

[0135] Step 2:

[0136] The device plays instructional videos received from the server. The user watches these videos and learns from their content, tone of voice, facial expressions, and other information.

[0137] Step 3:

[0138] The user uses their own camera and microphone to record themselves imitating the instructional video. The device then sends the recorded data to the server.

[0139] Step 4:

[0140] The server receives the recorded data and begins analysis to compare it with the instructional video. It uses speech recognition technology to evaluate voice tone and speaking speed, and image analysis technology to analyze similarities in facial expressions and movements.

[0141] Step 5:

[0142] By utilizing an emotion engine, the server recognizes emotions from the user's recorded data. This is estimated from the user's voice and facial expressions.

[0143] Step 6:

[0144] The server combines the evaluation of the imitated behavior with the user's emotional state to generate a scored evaluation and specific areas for improvement. It also adjusts the feedback based on the user's emotions.

[0145] Step 7:

[0146] The terminal visually displays the evaluation results and areas for improvement received from the server to the user. The user reviews this and understands their own performance.

[0147] Step 8:

[0148] Users will conduct imitation training again as needed and strive to further improve their skills based on the evaluation.

[0149] Step 9:

[0150] If the user's mimicked behavior meets the set criteria, the server will set up an interactive training session using the AI ​​avatar.

[0151] Step 10:

[0152] The device runs an AI avatar and begins interactive training with the user. Leveraging the recognition results of the emotion engine, the avatar's dialogue is adjusted to match the user's emotions.

[0153] (Example 2)

[0154] 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".

[0155] Traditional imitation training systems simply evaluate user behavior without providing feedback that takes into account the individual user's emotional state. As a result, users found it difficult to make effective improvements, and skill development tailored to individual needs was challenging.

[0156] 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.

[0157] In this invention, the server includes means for presenting educational videos selected by the user, means for recording the user's imitation behavior, means for analyzing the recorded imitation behavior by comparing it with the educational videos and generating evaluation and improvement guidelines, means for performing sentiment analysis to infer the user's emotional state, and means for adjusting feedback based on the emotional state. This makes it possible to provide feedback optimized for each user and realize effective skill improvement that takes into account individual emotional states.

[0158] "Educational videos" are video content that includes visual and auditory information and is provided for users to watch for the purpose of learning and skill imitation.

[0159] "Imitative behavior" refers to users recreating their own actions based on educational videos and recording these actions with a camera or microphone.

[0160] "Evaluation and Improvement Guidelines" refer to evaluations of the accuracy of user imitation behavior, generated by comparing it with educational videos, as well as specific advice and suggestions for further technical improvement.

[0161] "Emotional analysis" is a technology that infers a user's emotional state from their facial expressions, tone of voice, and other factors, and analyzes the user's inner state.

[0162] "Feedback adjustment" refers to the process of adapting the evaluations and improvement guidelines provided to users, based on the results of sentiment analysis, to their individual emotional states.

[0163] This invention supports efficient skill improvement in an imitation training system that utilizes educational videos by recognizing the user's emotions and providing personalized feedback. The system consists of terminals and servers, which operate in cooperation with each other via a network.

[0164] First, the user logs into the system using their device. During login, the user's authentication information is sent to the server for access authentication. Next, the user selects an educational video from the available options. The selection information is sent from the device to the server, and the server streams the selected educational video to the device.

[0165] Users watch streamed video and learn the content, tone of voice, and actions. After watching, users begin to imitate the actions, and the device records the user's actions using its camera and microphone. The recorded data is sent from the device to a server, which analyzes the data using high-precision speech recognition and image analysis technologies. The analysis evaluates how well the user's imitated actions match the educational video, and generates a scored evaluation and specific improvement guidelines.

[0166] Before the generated feedback is presented to the user, an emotion analysis engine infers the user's emotional state from their facial expressions and tone of voice. Based on this, the content of the feedback is adjusted to suit the individual user's emotional state. This adapted feedback is then presented to the user on the device.

[0167] For example, if a new sales employee uses this system to practice customer service, they can objectively understand how their explanation skills, facial expressions, and language are perceived by customers. As a result, users can effectively improve their skills.

[0168] An example of a prompt might be, "Describe a system used in customer service training for new sales staff that evaluates user mimicry and provides feedback based on their emotional state." This helps the generative AI model create user-optimized feedback.

[0169] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0170] Step 1:

[0171] The user logs into the system using their terminal. During this process, the user enters their user ID and password. The terminal sends this information to the server, which then performs authentication by referencing its authentication database. Upon receiving the authentication result, the terminal verifies whether the user was successfully authenticated. The user then receives a message indicating whether the login was successful or unsuccessful.

[0172] Step 2:

[0173] The user selects an educational video on their device. The device sends the ID of the selected video to the server. The server retrieves the corresponding educational video from its database and begins streaming it to the device. The input is the video ID, and the output is the streaming video.

[0174] Step 3:

[0175] The user watches an educational video on their device and begins to imitate the behavior. The device uses its camera and microphone to record the user's actions and voice. The recorded data is generated as video and audio files, which serve as input for transmission to the server.

[0176] Step 4:

[0177] The server receives recorded data sent from the terminal. It analyzes the audio file using speech recognition technology and converts the content into text. It analyzes the video data using image analysis technology and classifies user actions as specific actions. The output is the analysis results of actions and audio.

[0178] Step 5:

[0179] The server compares the user's imitation behavior with educational videos based on the analysis results. Using a generative AI model, it evaluates the analyzed data and generates a scored evaluation and specific improvement guidelines. This is the output of the step.

[0180] Step 6:

[0181] The server uses an emotion analysis engine to analyze the user's facial expressions and voice tone during recording to infer the user's emotional state. The input is the recorded data, and the output is the inferred emotion information. Based on this information, feedback is adjusted.

[0182] Step 7:

[0183] The device receives and displays tailored feedback to the user. This feedback includes evaluation results, specific improvement guidelines, and advice based on emotional state. Through this output, users can improve their behavior and enhance their skills.

[0184] (Application Example 2)

[0185] 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".

[0186] Traditional imitation training systems lack feedback that takes into account the user's emotional state, making it difficult for users to obtain appropriate improvement strategies that align with their own feelings. Furthermore, the difficulty in real-time evaluation in real-world environments results in a lack of responsiveness in on-site situations.

[0187] 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.

[0188] In this invention, the server includes means for analyzing the user's emotional state and optimizing feedback, means for comparing and evaluating imitation behavior with instructional videos, and means for evaluating behavior in a real environment in real time. This enables the user to receive appropriate feedback according to their emotional state while performing effective training in a real environment.

[0189] "Means for displaying user-selected instructional videos" refers to means for displaying training videos arbitrarily selected by the user on the device.

[0190] "Means for recording user imitation behavior" refers to methods of recording a user's actions when they imitate a video using devices such as cameras and microphones.

[0191] "Means for comparing recorded imitation behavior with instructional videos and generating evaluations and areas for improvement" refers to a means of comparing recorded user behavior with pre-prepared standard videos, evaluating the accuracy of that behavior, and identifying areas for improvement.

[0192] "Means of presenting evaluations and areas for improvement to users" refers to means of presenting the results of behavioral evaluations and improvement suggestions to users through visual and auditory means.

[0193] "Means for analyzing the user's emotional state and optimizing feedback" refers to methods for analyzing the user's emotions from their facial expressions and tone of voice, and providing appropriate feedback that corresponds to those emotions.

[0194] "Means of providing interactive training when imitative behavior meets evaluation criteria" refers to a means of providing interactive exercises as a higher-level training when the user's performance meets the criteria.

[0195] "Means for evaluating behavior in a real-world environment in real time" refers to means for sequentially evaluating user behavior in a real environment and providing immediate feedback on the results.

[0196] As part of this invention, the server streams instructional videos for imitation training selected by the user to the terminal. The user records their actions using smart glasses or other video recording devices. The recorded video data is analyzed using speech recognition and image analysis technologies and sent to the server.

[0197] The server compares recorded imitated behavior with instructional videos, evaluates the accuracy of the behavior, and generates areas for improvement. For further analysis, the server analyzes the user's emotional state using an emotion engine and tailors the feedback to individual needs. The feedback is transmitted to the terminal and presented to the user. For example, if an emotional state indicating tension is detected, relaxation methods are suggested.

[0198] If the imitation behavior meets the evaluation criteria, the server provides AI-driven interactive training, which can be adjusted based on user interaction. Users can receive evaluation results and training in real time via smart glasses or mobile devices. For this purpose, analysis software such as OpenCV and TensorFlow, as well as the Emotion Recognition API, are used.

[0199] As a concrete example, consider a scenario where sales staff are being trained on how to explain a new product. Using this system, users can evaluate their own explanation skills and nonverbal communication in a simulated environment and identify areas for improvement. Based on a prompt such as, "You are undergoing product introduction training. If you feel nervous, how would you suggest ways to relax?", the user is presented with feedback provided by a generative AI model.

[0200] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0201] Step 1:

[0202] The user logs into the system using a terminal and selects a training instruction video. The terminal sends the selected video as a request to the server. The input is the identification information of the selected instruction video, and the output is the streaming request to the server.

[0203] Step 2:

[0204] The server streams instructional videos to the terminal. The terminal receives the video and displays it to the user. The input here is the data stream of the instructional video, and the output is the video that the user can visually confirm. The server uses streaming technology to convert the video into an appropriate format.

[0205] Step 3:

[0206] The user records their mimicked actions using smart glasses or another capture device. The input is the user's behavior data, and the output is the recorded video and audio data. The user saves this to the device's memory.

[0207] Step 4:

[0208] The terminal sends recorded mimicry data to the server. The server receives this data and prepares it for analysis. The input is recorded behavior data, and the output is a dataset for analysis.

[0209] Step 5:

[0210] The server uses speech recognition and image analysis to compare recorded imitation behavior with instructional videos and generate an evaluation. Necessary data processing includes motion position detection and speech intonation analysis. Inputs are user behavior data and instructional videos, and output is an evaluation result regarding the accuracy of the behavior.

[0211] Step 6:

[0212] The server uses an emotion engine to analyze the user's emotional state and optimize the feedback. Input is the user's facial expressions and voice tone data, and output is the emotion evaluation result. Based on the emotion evaluation, a generative AI model is used to adjust the feedback.

[0213] Step 7:

[0214] The server generates suggestions for improvement based on the final evaluation and emotional state, and presents them to the user via the terminal. The input here is evaluation and emotional data, and the output is a personalized feedback message. The feedback is displayed as audio or visual.

[0215] Step 8:

[0216] If the imitation behavior meets the criteria, the server provides interactive training using an AI avatar. The user experiences this training on their device. The input is the evaluation result, and the output is the interactive training session. The AI ​​avatar interacts with the user using prompts, offering encouragement and guidance.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] [Second Embodiment]

[0221] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0222] 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.

[0223] 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).

[0224] 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.

[0225] 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.

[0226] 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).

[0227] 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.

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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.

[0232] 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".

[0233] This invention is a training system for users to improve specific skills and abilities. This system is implemented by connecting a user terminal to a server via a network. The user uses their terminal to select and view the necessary instructional videos.

[0234] The server streams instructional videos selected by the user to the device. These videos include both audio and video, allowing the user to learn from the content, tone of voice, facial expressions, and other details. The user then imitates what they have learned and records their imitated actions using the camera and microphone on their device.

[0235] Recorded imitation behaviors are sent from the terminal to the server. On the server side, speech recognition and image analysis technologies are used to compare the imitation behaviors with instructional videos and analyze their accuracy and similarity. Based on the results of this analysis, the server generates an evaluation of the imitation behavior and specific areas for improvement.

[0236] The evaluation results and areas for improvement are sent to the user's device, allowing them to visually review them. This enables the user to understand their progress and initiate further improvements.

[0237] Furthermore, if the imitation accuracy meets a certain standard, the server will provide an interactive training session. In this session, the AI ​​avatar will interact with the user as a customer, allowing for practice in realistic explanations and presentations.

[0238] For example, if a new sales employee wants to improve their skills in introducing a new product to a customer, they would first watch a training video on product introductions and then practice by imitating it. The system would then evaluate the results and suggest areas for improvement, providing training that more closely resembles a real-world situation. This allows users to objectively analyze their individual performance and efficiently improve their skills.

[0239] The following describes the processing flow.

[0240] Step 1:

[0241] The user logs into the system using their device. The server verifies the user's authentication information and provides the device with a list of instructional videos that the user can access.

[0242] Step 2:

[0243] The user selects an instructional video through their device. The server generates a streaming URL for the selected video and sends it to the device. The device then uses this URL to play the video.

[0244] Step 3:

[0245] Users watch instructional videos, learning from the content, tone of voice, and facial expressions. They then use their own camera and microphone to record themselves imitating the actions shown in the videos.

[0246] Step 4:

[0247] The device records the user's mimicked behavior and sends the data to the server. The server stores the received recorded data.

[0248] Step 5:

[0249] The server uses speech recognition and image analysis technologies to compare recorded data with instructional videos. Specifically, it analyzes voice tone, speaking speed, and the degree of matching of facial expressions.

[0250] Step 6:

[0251] Based on the analysis results, the server scores and evaluates the user's imitation behavior. It also generates specific points for improvement.

[0252] Step 7:

[0253] The server sends evaluation results and areas for improvement to the terminal. The terminal displays this information to the user, allowing the user to check their own performance.

[0254] Step 8:

[0255] Based on feedback from the server, users set their next practice goals and work towards further improvement.

[0256] Step 9:

[0257] If the user's mimicry meets certain criteria, the server prepares to start an interactive training session using the AI ​​avatar.

[0258] Step 10:

[0259] The device provides users with an interactive practice environment through an AI avatar, enhancing their smooth communication skills in realistic scenarios.

[0260] (Example 1)

[0261] 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."

[0262] Traditional skill improvement systems primarily rely on self-assessment by users, making it difficult to receive objective feedback or detailed improvement strategies. Furthermore, the accuracy of imitation is unclear, making it difficult to determine how further improvement is possible. As a result, there is a problem in that users' skill improvement is not efficient.

[0263] 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.

[0264] In this invention, the server includes means for providing educational videos selected by the user, means for recording the user's imitation behavior, means for comparing the recorded imitation behavior with the educational videos and generating evaluations and areas for improvement, means for providing the evaluations and areas for improvement to the user, and means for generating specific feedback using generative AI technology. This allows the user to objectively understand the accuracy of their imitation and obtain detailed feedback and areas for improvement.

[0265] "Educational videos" are videos containing visual and auditory information designed to help learners acquire specific skills or knowledge.

[0266] "Imitative behavior" refers to self-expressive behaviors such as actions and speech that users perform based on the content of educational videos.

[0267] "Recording" refers to the act of saving imitative behavior as digital data, which is usually done using devices such as cameras and microphones.

[0268] "Evaluation criteria" refer to standards set to quantitatively or qualitatively measure imitative behavior, and serve as criteria for judging accuracy and effectiveness.

[0269] "Interactive learning" refers to a form of learning activity in which users engage interactively, usually through virtual interaction.

[0270] "Generative AI technology" refers to artificial intelligence technology that generates new information based on large amounts of data, and is used to automatically generate specific feedback.

[0271] "Feedback" is the process of evaluating a user's performance and providing suggestions for improvement and advice.

[0272] In this invention, the user first selects and watches an educational video related to a specific skill from a list of videos provided on their device. The device uses its camera and microphone to record the user's imitation behavior. The recorded data is temporarily stored in the device's storage in real time.

[0273] The server provides educational videos selected by the user using streaming technology. Specifically, it uses the H.264 codec for streaming to ensure smooth video playback. In addition, it receives recorded data from the terminal and uses a speech recognition API and image analysis libraries (such as Google Speech-to-Text API, OpenCV, and TensorFlow) to analyze the accuracy of the imitation behavior and its similarity to the educational video.

[0274] Based on the analysis results, the server utilizes generative AI technology to generate an evaluation of the user's mimicked behavior and specific areas for improvement. The generated evaluation is quantified, and the user can visually view it on their device. Based on the feedback, the user can clearly identify areas for improvement and efficiently improve their skills. By using AI technology, the goal is to make the quality of the feedback equivalent to, or even better than, that provided by human professionals.

[0275] Furthermore, if the user's imitation behavior meets certain evaluation criteria, the server provides an opportunity for interactive learning using an AI avatar. In this interactive session, the AI ​​avatar acts as a virtual customer or questioner, engaging in real-time question-and-answer sessions with the user. For example, in a scenario simulating a new product presentation, the user can practice explaining the product's features.

[0276] When using a generative AI model, you can use prompts like the following: "Which educational videos should I prioritize learning to improve my sales skills?" This prompt can help select educational content that meets the user's specific needs.

[0277] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0278] Step 1:

[0279] The user selects a video related to improving a specific skill from a list of educational videos provided on their device. The input is the user's selection, and the output is a link to a specific educational video based on the user's selection. The device then sends a streaming request to the server based on this link.

[0280] Step 2:

[0281] The server receives a streaming request from the user and transmits the corresponding educational video. The input is the video request received from the user, and the output is the compressed video data. The server uses the H.264 codec technology to stream the video and achieve smooth playback.

[0282] Step 3:

[0283] The user watches the streamed educational video using the terminal and imitates its content. The input is the presented educational video, and the user's actions are imitated. The output is the video and audio recording of the user's imitation actions, which are recorded using the camera and microphone of the terminal. This data is temporarily stored in the terminal storage.

[0284] Step 4:

[0285] The terminal transmits the recorded imitation action data to the server. The input is the imitation action data stored in the terminal, and the output is the data transmitted to the server. The transmission is carried out through the HTTPS protocol to ensure the privacy and security of the data.

[0286] Step 5:

[0287] The server performs speech recognition and image analysis on the received imitation action data. The input is the imitation action data, and the output is to generate the accuracy comparison result and similarity analysis result with the educational video. The server uses technologies such as Google Speech-to-Text API, OpenCV, and TensorFlow to precisely evaluate the imitation actions.

[0288] Step 6:

[0289] The server utilizes the generated AI model based on the analysis results to evaluate the imitation actions and generate specific improvement points. The input is the analysis results, and the output is to obtain an evaluation score and improvement suggestions. Thereby, feedback is automatically generated.

[0290] Step 7:

[0291] The evaluation results and improvement suggestions from the server are sent to the user's terminal. The input is the evaluation results and improvement suggestions, and the output is information that the user can visually confirm. Based on this information, the user can improve their actions in the next step.

[0292] Step 8:

[0293] If the imitation behavior meets certain evaluation criteria, the server provides an interactive learning session with an AI avatar. The input is the user's evaluation score, and the output is the initiation of an interactive session. The AI ​​avatar functions as a virtual conversation partner, providing an environment for, for example, a virtual product presentation.

[0294] (Application Example 1)

[0295] 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."

[0296] In modern workplaces, employees are required to acquire new skills quickly and efficiently. Furthermore, the lack of immediate feedback on the job and the inefficiency of traditional training methods are problematic. This invention aims to enable employees to rapidly improve their skills by providing an interactive training method.

[0297] 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.

[0298] In this invention, the server includes a device that presents instructional videos selected by the user, a device that records the user's imitative actions, a device that compares the recorded imitative actions with the instructional videos and generates evaluations and points for improvement, a device that presents the evaluations and points for improvement to the user, a device that provides interactive training with an artificial construct when the imitative actions meet evaluation criteria, and a device that is installed on a visual display device and performs action imitation and evaluation in real time. This enables employees to efficiently learn new technologies, immediately grasp their level of proficiency, and receive specific feedback for further improvement.

[0299] A "device that presents user-selected instructional videos" is a device that allows users to watch videos on their own devices to improve specific skills or abilities.

[0300] A "device for recording user imitation behavior" is a device that records a user's actions and vocalizations as they imitate instructional videos.

[0301] A "device that compares recorded imitative behavior with instructional videos and generates evaluations and areas for improvement" is a device that analyzes the similarity between the imitative behavior recorded by the user and the instructional video, and generates feedback based on the results.

[0302] A "device for presenting evaluations and areas for improvement to users" is a device that presents evaluations based on analysis results and specific improvement suggestions to users visually or audibly.

[0303] A "device that provides interactive training with an artificial construct" is a device that enables an AI or virtual agent to conduct various types of training through real-time interaction with a user.

[0304] A "device installed in a visual display device that performs motion imitation and evaluation in real time" refers to a device installed in smart glasses or a headset that records the user's movements in real time and performs immediate evaluation.

[0305] The system designed to implement this invention basically consists of a user's visual display device and a server. As the visual display device, smart glasses or headsets are used. When the user selects a guidance video, the server streams this video in real time and presents it to the user's visual display device.

[0306] When the user performs imitation actions, the camera and microphone installed in the visual display device are used to record the actions and voices. When the recording is completed, the data is sent to the server. Then, the server uses speech recognition and image analysis technologies to compare the imitation actions with the guidance video. Specifically, Google Cloud Speech-to-Text is used for speech recognition, and OpenCV is applied to video analysis.

[0307] Based on these comparison results, the server generates evaluations and improvement points. The generated evaluations and improvement points are sent back to the user's visual display device, and the user can visually confirm them through this. When a certain standard is met, as the next step, the server uses the generated AI model to provide an interactive training session with an artificial construct. Through this session, the user can receive training through more detailed feedback and interaction.

[0308] As a specific example, it can be a case where an operator in a factory acquires the operation procedures of a new machine. The operator watches and imitates the guidance video and records the actions with the visual display device. Then, the operator receives feedback based on the degree of match with the original video and can receive detailed interactive training by AI if the evaluation is high.

[0309] As an example of a prompt sentence, the content "I need to learn how to operate a new welding machine. I want to conduct simulation training with an AI avatar and know specific improvement points." can be considered.

[0310] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0311] Step 1:

[0312] The user wears a visual display device and selects training instructional videos from a server via a terminal. The server streams the selected instructional videos to the terminal. The input data is the user's selection information, and the output data is the instructional videos displayed on the user's terminal.

[0313] Step 2:

[0314] The user imitates the actions while watching instructional videos. The device records these imitations using a camera and microphone mounted on the visual display device. The input is the user's actions and vocalizations, and the recorded imitation data is the output.

[0315] Step 3:

[0316] The recorded imitation data is sent from the terminal to the server. The server converts the audio data into text data using speech recognition technology (e.g., Google Cloud Speech-to-Text) and extracts motion features from the video data using image analysis technology (e.g., OpenCV). As a result, the imitation data is output as numerically represented analysis data.

[0317] Step 4:

[0318] The server compares the analyzed data with the instructional video and evaluates their similarity. Based on the comparison results, it calculates specific evaluations and areas for improvement. The inputs are the analyzed data and the feature data of the instructional video, and the output is the evaluation and areas for improvement.

[0319] Step 5:

[0320] The server sends data to the terminal to present the calculated evaluation and areas for improvement to the user. The user reviews the feedback through a visual display device. The input is the evaluation and areas for improvement, and the output is their visual display.

[0321] Step 6:

[0322] If the evaluation meets certain criteria, the server starts an interactive conversational training session with an artificial construct using the generated AI model. The user and the AI ​​avatar interact through voice and video. The input is the user's progress, and the output is the experience gained from the conversational training.

[0323] 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.

[0324] This invention combines an imitation training system using instructional videos with an emotion engine that recognizes user emotions, enabling more personalized feedback and interaction. This system operates with a terminal and server working together via a network. Users log into the system using their terminal and select and view instructional videos.

[0325] The server streams the instructional video selected by the user to the device. The user watches it, learns the content, tone of voice, and facial expressions, and records themselves imitating the actions. The recorded data is then sent from the device to the server.

[0326] The server compares this data with instructional videos, using speech recognition and image analysis technologies to evaluate the accuracy of the imitated behavior. It then generates a scored evaluation of the imitated behavior and specific areas for improvement, which are presented to the user on their device. During this process, an emotion engine is used to analyze the user's emotional state, and the evaluation and improvement points are adjusted based on this analysis.

[0327] The emotion engine infers emotions from the user's facial expressions, tone of voice, and other factors. Based on the recognized emotion information, the server optimizes the feedback content for the user. For example, if it determines that the user is tense, it can include suggestions for relaxation techniques. In this way, the evaluation takes the user's emotional state into account, resulting in more flexible and personalized content.

[0328] Furthermore, if the user's mimicked behavior meets the evaluation criteria, the server provides interactive training using an AI avatar. In this training, the AI ​​avatar adjusts its interaction with the user based on emotional information. For example, if the user is feeling down, the avatar is configured to offer words of encouragement.

[0329] As a concrete example, consider a new sales employee practicing customer service. Using this system, the user can understand how their explanation skills, facial expressions, and voice are perceived by customers. Furthermore, the practice content is tailored to the user's emotions at the time, allowing them to steadily hone their skills. In this way, this system supports efficient and effective skill improvement by precisely addressing individual needs.

[0330] The following describes the processing flow.

[0331] Step 1:

[0332] The user logs into the system using their device and selects the video they want to watch from a list of instructional videos. The server then prepares to stream this video data to the device.

[0333] Step 2:

[0334] The device plays instructional videos received from the server. The user watches these videos and learns from their content, tone of voice, facial expressions, and other information.

[0335] Step 3:

[0336] The user uses their own camera and microphone to record themselves imitating the instructional video. The device then sends the recorded data to the server.

[0337] Step 4:

[0338] The server receives the recorded data and begins analysis to compare it with the instructional video. It uses speech recognition technology to evaluate voice tone and speaking speed, and image analysis technology to analyze similarities in facial expressions and movements.

[0339] Step 5:

[0340] By utilizing an emotion engine, the server recognizes emotions from the user's recorded data. This is estimated from the user's voice and facial expressions.

[0341] Step 6:

[0342] The server combines the evaluation of the imitated behavior with the user's emotional state to generate a scored evaluation and specific areas for improvement. It also adjusts the feedback based on the user's emotions.

[0343] Step 7:

[0344] The terminal visually displays the evaluation results and areas for improvement received from the server to the user. The user reviews this and understands their own performance.

[0345] Step 8:

[0346] Users will conduct imitation training again as needed and strive to further improve their skills based on the evaluation.

[0347] Step 9:

[0348] If the user's mimicked behavior meets the set criteria, the server will set up an interactive training session using the AI ​​avatar.

[0349] Step 10:

[0350] The device runs an AI avatar and begins interactive training with the user. Leveraging the recognition results of the emotion engine, the avatar's dialogue is adjusted to match the user's emotions.

[0351] (Example 2)

[0352] 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".

[0353] Traditional imitation training systems simply evaluate user behavior without providing feedback that takes into account the individual user's emotional state. As a result, users found it difficult to make effective improvements, and skill development tailored to individual needs was challenging.

[0354] 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.

[0355] In this invention, the server includes means for presenting educational videos selected by the user, means for recording the user's imitation behavior, means for analyzing the recorded imitation behavior by comparing it with the educational videos and generating evaluation and improvement guidelines, means for performing sentiment analysis to infer the user's emotional state, and means for adjusting feedback based on the emotional state. This makes it possible to provide feedback optimized for each user and realize effective skill improvement that takes into account individual emotional states.

[0356] "Educational videos" are video content that includes visual and auditory information and is provided for users to watch for the purpose of learning and skill imitation.

[0357] "Imitative behavior" refers to users recreating their own actions based on educational videos and recording these actions with a camera or microphone.

[0358] "Evaluation and Improvement Guidelines" refer to evaluations of the accuracy of user imitation behavior, generated by comparing it with educational videos, as well as specific advice and suggestions for further technical improvement.

[0359] "Emotional analysis" is a technology that infers a user's emotional state from their facial expressions, tone of voice, and other factors, and analyzes the user's inner state.

[0360] "Feedback adjustment" refers to the process of adapting the evaluations and improvement guidelines provided to users, based on the results of sentiment analysis, to their individual emotional states.

[0361] This invention supports efficient skill improvement in an imitation training system that utilizes educational videos by recognizing the user's emotions and providing personalized feedback. The system consists of terminals and servers, which operate in cooperation with each other via a network.

[0362] First, the user logs into the system using their device. During login, the user's authentication information is sent to the server for access authentication. Next, the user selects an educational video from the available options. The selection information is sent from the device to the server, and the server streams the selected educational video to the device.

[0363] Users watch streamed video and learn the content, tone of voice, and actions. After watching, users begin to imitate the actions, and the device records the user's actions using its camera and microphone. The recorded data is sent from the device to a server, which analyzes the data using high-precision speech recognition and image analysis technologies. The analysis evaluates how well the user's imitated actions match the educational video, and generates a scored evaluation and specific improvement guidelines.

[0364] Before the generated feedback is presented to the user, an emotion analysis engine infers the user's emotional state from their facial expressions and tone of voice. Based on this, the content of the feedback is adjusted to suit the individual user's emotional state. This adapted feedback is then presented to the user on the device.

[0365] For example, if a new sales employee uses this system to practice customer service, they can objectively understand how their explanation skills, facial expressions, and language are perceived by customers. As a result, users can effectively improve their skills.

[0366] An example of a prompt might be, "Describe a system used in customer service training for new sales staff that evaluates user mimicry and provides feedback based on their emotional state." This helps the generative AI model create user-optimized feedback.

[0367] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0368] Step 1:

[0369] The user logs into the system using their terminal. During this process, the user enters their user ID and password. The terminal sends this information to the server, which then performs authentication by referencing its authentication database. Upon receiving the authentication result, the terminal verifies whether the user was successfully authenticated. The user then receives a message indicating whether the login was successful or unsuccessful.

[0370] Step 2:

[0371] The user selects an educational video on their device. The device sends the ID of the selected video to the server. The server retrieves the corresponding educational video from its database and begins streaming it to the device. The input is the video ID, and the output is the streaming video.

[0372] Step 3:

[0373] The user watches an educational video on their device and begins to imitate the behavior. The device uses its camera and microphone to record the user's actions and voice. The recorded data is generated as video and audio files, which serve as input for transmission to the server.

[0374] Step 4:

[0375] The server receives recorded data sent from the terminal. It analyzes the audio file using speech recognition technology and converts the content into text. It analyzes the video data using image analysis technology and classifies user actions as specific actions. The output is the analysis results of actions and audio.

[0376] Step 5:

[0377] The server compares the user's imitation behavior with educational videos based on the analysis results. Using a generative AI model, it evaluates the analyzed data and generates a scored evaluation and specific improvement guidelines. This is the output of the step.

[0378] Step 6:

[0379] The server uses an emotion analysis engine to analyze the user's facial expressions and voice tone during recording to infer the user's emotional state. The input is the recorded data, and the output is the inferred emotion information. Based on this information, feedback is adjusted.

[0380] Step 7:

[0381] The device receives and displays tailored feedback to the user. This feedback includes evaluation results, specific improvement guidelines, and advice based on emotional state. Through this output, users can improve their behavior and enhance their skills.

[0382] (Application Example 2)

[0383] 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."

[0384] Traditional imitation training systems lack feedback that takes into account the user's emotional state, making it difficult for users to obtain appropriate improvement strategies that align with their own feelings. Furthermore, the difficulty in real-time evaluation in real-world environments results in a lack of responsiveness in on-site situations.

[0385] 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.

[0386] In this invention, the server includes means for analyzing the user's emotional state and optimizing feedback, means for comparing and evaluating imitation behavior with instructional videos, and means for evaluating behavior in a real environment in real time. This enables the user to receive appropriate feedback according to their emotional state while performing effective training in a real environment.

[0387] "Means for displaying user-selected instructional videos" refers to means for displaying training videos arbitrarily selected by the user on the device.

[0388] "Means for recording user imitation behavior" refers to methods of recording a user's actions when they imitate a video using devices such as cameras and microphones.

[0389] "Means for comparing recorded imitation behavior with instructional videos and generating evaluations and areas for improvement" refers to a means of comparing recorded user behavior with pre-prepared standard videos, evaluating the accuracy of that behavior, and identifying areas for improvement.

[0390] "Means of presenting evaluations and areas for improvement to users" refers to means of presenting the results of behavioral evaluations and improvement suggestions to users through visual and auditory means.

[0391] "Means for analyzing the user's emotional state and optimizing feedback" refers to methods for analyzing the user's emotions from their facial expressions and tone of voice, and providing appropriate feedback that corresponds to those emotions.

[0392] "Means of providing interactive training when imitative behavior meets evaluation criteria" refers to a means of providing interactive exercises as a higher-level training when the user's performance meets the criteria.

[0393] "Means for evaluating behavior in a real-world environment in real time" refers to means for sequentially evaluating user behavior in a real environment and providing immediate feedback on the results.

[0394] As part of this invention, the server streams instructional videos for imitation training selected by the user to the terminal. The user records their actions using smart glasses or other video recording devices. The recorded video data is analyzed using speech recognition and image analysis technologies and sent to the server.

[0395] The server compares recorded imitated behavior with instructional videos, evaluates the accuracy of the behavior, and generates areas for improvement. For further analysis, the server analyzes the user's emotional state using an emotion engine and tailors the feedback to individual needs. The feedback is transmitted to the terminal and presented to the user. For example, if an emotional state indicating tension is detected, relaxation methods are suggested.

[0396] If the imitation behavior meets the evaluation criteria, the server provides AI-driven interactive training, which can be adjusted based on user interaction. Users can receive evaluation results and training in real time via smart glasses or mobile devices. For this purpose, analysis software such as OpenCV and TensorFlow, as well as the Emotion Recognition API, are used.

[0397] As a concrete example, consider a scenario where sales staff are being trained on how to explain a new product. Using this system, users can evaluate their own explanation skills and nonverbal communication in a simulated environment and identify areas for improvement. Based on a prompt such as, "You are undergoing product introduction training. If you feel nervous, how would you suggest ways to relax?", the user is presented with feedback provided by a generative AI model.

[0398] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0399] Step 1:

[0400] The user logs into the system using a terminal and selects a training instruction video. The terminal sends the selected video as a request to the server. The input is the identification information of the selected instruction video, and the output is the streaming request to the server.

[0401] Step 2:

[0402] The server streams instructional videos to the terminal. The terminal receives the video and displays it to the user. The input here is the data stream of the instructional video, and the output is the video that the user can visually confirm. The server uses streaming technology to convert the video into an appropriate format.

[0403] Step 3:

[0404] The user records their mimicked actions using smart glasses or another capture device. The input is the user's behavior data, and the output is the recorded video and audio data. The user saves this to the device's memory.

[0405] Step 4:

[0406] The terminal sends recorded mimicry data to the server. The server receives this data and prepares it for analysis. The input is recorded behavior data, and the output is a dataset for analysis.

[0407] Step 5:

[0408] The server uses speech recognition and image analysis to compare recorded imitation behavior with instructional videos and generate an evaluation. Necessary data processing includes motion position detection and speech intonation analysis. Inputs are user behavior data and instructional videos, and output is an evaluation result regarding the accuracy of the behavior.

[0409] Step 6:

[0410] The server uses an emotion engine to analyze the user's emotional state and optimize the feedback. Input is the user's facial expressions and voice tone data, and output is the emotion evaluation result. Based on the emotion evaluation, a generative AI model is used to adjust the feedback.

[0411] Step 7:

[0412] The server generates suggestions for improvement based on the final evaluation and emotional state, and presents them to the user via the terminal. The input here is evaluation and emotional data, and the output is a personalized feedback message. The feedback is displayed as audio or visual.

[0413] Step 8:

[0414] If the imitation behavior meets the criteria, the server provides interactive training using an AI avatar. The user experiences this training on their device. The input is the evaluation result, and the output is the interactive training session. The AI ​​avatar interacts with the user using prompts, offering encouragement and guidance.

[0415] 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.

[0416] 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.

[0417] 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.

[0418] [Third Embodiment]

[0419] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0420] 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.

[0421] 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).

[0422] 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.

[0423] 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.

[0424] 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).

[0425] 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.

[0426] 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.

[0427] 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.

[0428] 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.

[0429] 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.

[0430] 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".

[0431] This invention is a training system for users to improve specific skills and abilities. This system is implemented by connecting a user terminal to a server via a network. The user uses their terminal to select and view the necessary instructional videos.

[0432] The server streams instructional videos selected by the user to the device. These videos include both audio and video, allowing the user to learn from the content, tone of voice, facial expressions, and other details. The user then imitates what they have learned and records their imitated actions using the camera and microphone on their device.

[0433] Recorded imitation behaviors are sent from the terminal to the server. On the server side, speech recognition and image analysis technologies are used to compare the imitation behaviors with instructional videos and analyze their accuracy and similarity. Based on the results of this analysis, the server generates an evaluation of the imitation behavior and specific areas for improvement.

[0434] The evaluation results and areas for improvement are sent to the user's device, allowing them to visually review them. This enables the user to understand their progress and initiate further improvements.

[0435] Furthermore, if the imitation accuracy meets a certain standard, the server will provide an interactive training session. In this session, the AI ​​avatar will interact with the user as a customer, allowing for practice in realistic explanations and presentations.

[0436] For example, if a new sales employee wants to improve their skills in introducing a new product to a customer, they would first watch a training video on product introductions and then practice by imitating it. The system would then evaluate the results and suggest areas for improvement, providing training that more closely resembles a real-world situation. This allows users to objectively analyze their individual performance and efficiently improve their skills.

[0437] The following describes the processing flow.

[0438] Step 1:

[0439] The user logs into the system using their device. The server verifies the user's authentication information and provides the device with a list of instructional videos that the user can access.

[0440] Step 2:

[0441] The user selects an instructional video through their device. The server generates a streaming URL for the selected video and sends it to the device. The device then uses this URL to play the video.

[0442] Step 3:

[0443] Users watch instructional videos, learning from the content, tone of voice, and facial expressions. They then use their own camera and microphone to record themselves imitating the actions shown in the videos.

[0444] Step 4:

[0445] The device records the user's mimicked behavior and sends the data to the server. The server stores the received recorded data.

[0446] Step 5:

[0447] The server uses speech recognition and image analysis technologies to compare recorded data with instructional videos. Specifically, it analyzes voice tone, speaking speed, and the degree of matching of facial expressions.

[0448] Step 6:

[0449] Based on the analysis results, the server scores and evaluates the user's imitation behavior. It also generates specific points for improvement.

[0450] Step 7:

[0451] The server sends evaluation results and areas for improvement to the terminal. The terminal displays this information to the user, allowing the user to check their own performance.

[0452] Step 8:

[0453] Based on feedback from the server, users set their next practice goals and work towards further improvement.

[0454] Step 9:

[0455] If the user's mimicry meets certain criteria, the server prepares to start an interactive training session using the AI ​​avatar.

[0456] Step 10:

[0457] The device provides users with an interactive practice environment through an AI avatar, enhancing their smooth communication skills in realistic scenarios.

[0458] (Example 1)

[0459] 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."

[0460] Traditional skill improvement systems primarily rely on self-assessment by users, making it difficult to receive objective feedback or detailed improvement strategies. Furthermore, the accuracy of imitation is unclear, making it difficult to determine how further improvement is possible. As a result, there is a problem in that users' skill improvement is not efficient.

[0461] 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.

[0462] In this invention, the server includes means for providing educational videos selected by the user, means for recording the user's imitation behavior, means for comparing the recorded imitation behavior with the educational videos and generating evaluations and areas for improvement, means for providing the evaluations and areas for improvement to the user, and means for generating specific feedback using generative AI technology. This allows the user to objectively understand the accuracy of their imitation and obtain detailed feedback and areas for improvement.

[0463] "Educational videos" are videos containing visual and auditory information designed to help learners acquire specific skills or knowledge.

[0464] "Imitative behavior" refers to self-expressive behaviors such as actions and speech that users perform based on the content of educational videos.

[0465] "Recording" refers to the act of saving imitative behavior as digital data, which is usually done using devices such as cameras and microphones.

[0466] "Evaluation criteria" refer to standards set to quantitatively or qualitatively measure imitative behavior, and serve as criteria for judging accuracy and effectiveness.

[0467] "Interactive learning" refers to a form of learning activity in which users engage interactively, usually through virtual interaction.

[0468] "Generative AI technology" refers to artificial intelligence technology that generates new information based on large amounts of data, and is used to automatically generate specific feedback.

[0469] "Feedback" is the process of evaluating a user's performance and providing suggestions for improvement and advice.

[0470] In this invention, the user first selects and watches an educational video related to a specific skill from a list of videos provided on their device. The device uses its camera and microphone to record the user's imitation behavior. The recorded data is temporarily stored in the device's storage in real time.

[0471] The server provides educational videos selected by the user using streaming technology. Specifically, it uses the H.264 codec for streaming to ensure smooth video playback. In addition, it receives recorded data from the terminal and uses a speech recognition API and image analysis libraries (such as Google Speech-to-Text API, OpenCV, and TensorFlow) to analyze the accuracy of the imitation behavior and its similarity to the educational video.

[0472] Based on the analysis results, the server utilizes generative AI technology to generate an evaluation of the user's mimicked behavior and specific areas for improvement. The generated evaluation is quantified, and the user can visually view it on their device. Based on the feedback, the user can clearly identify areas for improvement and efficiently improve their skills. By using AI technology, the goal is to make the quality of the feedback equivalent to, or even better than, that provided by human professionals.

[0473] Furthermore, if the user's imitation behavior meets certain evaluation criteria, the server provides an opportunity for interactive learning using an AI avatar. In this interactive session, the AI ​​avatar acts as a virtual customer or questioner, engaging in real-time question-and-answer sessions with the user. For example, in a scenario simulating a new product presentation, the user can practice explaining the product's features.

[0474] When using a generative AI model, you can use prompts like the following: "Which educational videos should I prioritize learning to improve my sales skills?" This prompt can help select educational content that meets the user's specific needs.

[0475] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0476] Step 1:

[0477] The user selects a video related to improving a specific skill from a list of educational videos provided on their device. The input is the user's selection, and the output is a link to a specific educational video based on the user's selection. The device then sends a streaming request to the server based on this link.

[0478] Step 2:

[0479] The server receives streaming requests from users and transmits the corresponding educational videos. The input is the video request received from the user, and the output is compressed video data. The server uses H.264 codec technology to stream the video and ensure smooth playback.

[0480] Step 3:

[0481] The user watches a streamed educational video on their device and imitates its content. The input is the educational video, and the user's actions are imitated. The output is a video and audio recording of the user's imitated actions, recorded using the device's camera and microphone. This data is temporarily stored in the device's storage.

[0482] Step 4:

[0483] The device sends recorded mimicry data to the server. The input is the mimicry data stored on the device, and the output is the data sent to the server. The transmission is done via the HTTPS protocol, ensuring data privacy and security.

[0484] Step 5:

[0485] The server performs speech recognition and image analysis on the received imitation behavior data. The input is imitation behavior data, and the output generates accuracy comparison results with educational videos and similarity analysis results. The server uses technologies such as Google Speech-to-Text API, OpenCV, and TensorFlow to precisely evaluate the imitation behavior.

[0486] Step 6:

[0487] The server utilizes a generated AI model based on the analysis results to evaluate the mimicked behavior and generate specific areas for improvement. The input is the analysis results, and the output is an evaluation score and improvement suggestions. This automatically generates feedback.

[0488] Step 7:

[0489] The evaluation results and improvement suggestions from the server are sent to the user's terminal. The input is the evaluation results and improvement suggestions, and the output is information that the user can visually confirm. Based on this information, the user can improve their actions in the next step.

[0490] Step 8:

[0491] If the imitation behavior meets certain evaluation criteria, the server provides an interactive learning session with an AI avatar. The input is the user's evaluation score, and the output is the initiation of an interactive session. The AI ​​avatar functions as a virtual conversation partner, providing an environment for, for example, a virtual product presentation.

[0492] (Application Example 1)

[0493] 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."

[0494] In modern workplaces, employees are required to acquire new skills quickly and efficiently. Furthermore, the lack of immediate feedback on the job and the inefficiency of traditional training methods are problematic. This invention aims to enable employees to rapidly improve their skills by providing an interactive training method.

[0495] 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.

[0496] In this invention, the server includes a device that presents instructional videos selected by the user, a device that records the user's imitative actions, a device that compares the recorded imitative actions with the instructional videos and generates evaluations and points for improvement, a device that presents the evaluations and points for improvement to the user, a device that provides interactive training with an artificial construct when the imitative actions meet evaluation criteria, and a device that is installed on a visual display device and performs action imitation and evaluation in real time. This enables employees to efficiently learn new technologies, immediately grasp their level of proficiency, and receive specific feedback for further improvement.

[0497] A "device that presents user-selected instructional videos" is a device that allows users to watch videos on their own devices to improve specific skills or abilities.

[0498] A "device for recording user imitation behavior" is a device that records a user's actions and vocalizations as they imitate instructional videos.

[0499] A "device that compares recorded imitative behavior with instructional videos and generates evaluations and areas for improvement" is a device that analyzes the similarity between the imitative behavior recorded by the user and the instructional video, and generates feedback based on the results.

[0500] A "device for presenting evaluations and areas for improvement to users" is a device that presents evaluations based on analysis results and specific improvement suggestions to users visually or audibly.

[0501] A "device that provides interactive training with an artificial construct" is a device that enables an AI or virtual agent to conduct various types of training through real-time interaction with a user.

[0502] A "device installed in a visual display device that performs motion imitation and evaluation in real time" refers to a device installed in smart glasses or a headset that records the user's movements in real time and performs immediate evaluation.

[0503] The system designed to realize this invention basically consists of a user's visual display device and a server. Smart glasses or a headset are used as the visual display device. When the user selects an instructional video, the server streams this video in real time and presents it to the user's visual display device.

[0504] When a user performs an imitative action, the camera and microphone on the visual display device record the action and sound. Once recording is complete, the data is sent to a server. The server then uses speech recognition and image analysis technologies to compare the imitative action with the instructional video. Specifically, Google Cloud Speech-to-Text is used for speech recognition, and OpenCV is applied for image analysis.

[0505] The server generates evaluations and areas for improvement based on these comparison results. These evaluations and areas for improvement are returned to the user's visual display device, allowing the user to visually confirm them. If certain criteria are met, the next step is for the server to provide an interactive training session using the generated AI model. This session allows the user to receive more detailed feedback and interactive training.

[0506] A concrete example is when a factory worker learns how to operate a new machine. The worker watches a training video, imitates the movements, and records them using a visual display device. Afterwards, they receive feedback based on how closely their movements match the original video, and if they perform well, they can receive detailed interactive training from AI.

[0507] An example of a prompt message might be: "I need to learn how to operate the new welding machine. I would like to practice with an AI avatar to find out what specific areas for improvement are needed."

[0508] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0509] Step 1:

[0510] The user wears a visual display device and selects training instructional videos from a server via a terminal. The server streams the selected instructional videos to the terminal. The input data is the user's selection information, and the output data is the instructional videos displayed on the user's terminal.

[0511] Step 2:

[0512] The user imitates the actions while watching instructional videos. The device records these imitations using a camera and microphone mounted on the visual display device. The input is the user's actions and vocalizations, and the recorded imitation data is the output.

[0513] Step 3:

[0514] The recorded imitation data is sent from the terminal to the server. The server converts the audio data into text data using speech recognition technology (e.g., Google Cloud Speech-to-Text) and extracts motion features from the video data using image analysis technology (e.g., OpenCV). As a result, the imitation data is output as numerically represented analysis data.

[0515] Step 4:

[0516] The server compares the analyzed data with the instructional video and evaluates their similarity. Based on the comparison results, it calculates specific evaluations and areas for improvement. The inputs are the analyzed data and the feature data of the instructional video, and the output is the evaluation and areas for improvement.

[0517] Step 5:

[0518] The server sends data to the terminal to present the calculated evaluation and areas for improvement to the user. The user reviews the feedback through a visual display device. The input is the evaluation and areas for improvement, and the output is their visual display.

[0519] Step 6:

[0520] If the evaluation meets certain criteria, the server starts an interactive conversational training session with an artificial construct using the generated AI model. The user and the AI ​​avatar interact through voice and video. The input is the user's progress, and the output is the experience gained from the conversational training.

[0521] 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.

[0522] This invention combines an imitation training system using instructional videos with an emotion engine that recognizes user emotions, enabling more personalized feedback and interaction. This system operates with a terminal and server working together via a network. Users log into the system using their terminal and select and view instructional videos.

[0523] The server streams the instructional video selected by the user to the device. The user watches it, learns the content, tone of voice, and facial expressions, and records themselves imitating the actions. The recorded data is then sent from the device to the server.

[0524] The server compares this data with instructional videos, using speech recognition and image analysis technologies to evaluate the accuracy of the imitated behavior. It then generates a scored evaluation of the imitated behavior and specific areas for improvement, which are presented to the user on their device. During this process, an emotion engine is used to analyze the user's emotional state, and the evaluation and improvement points are adjusted based on this analysis.

[0525] The emotion engine infers emotions from the user's facial expressions, tone of voice, and other factors. Based on the recognized emotion information, the server optimizes the feedback content for the user. For example, if it determines that the user is tense, it can include suggestions for relaxation techniques. In this way, the evaluation takes the user's emotional state into account, resulting in more flexible and personalized content.

[0526] Furthermore, if the user's mimicked behavior meets the evaluation criteria, the server provides interactive training using an AI avatar. In this training, the AI ​​avatar adjusts its interaction with the user based on emotional information. For example, if the user is feeling down, the avatar is configured to offer words of encouragement.

[0527] As a concrete example, consider a new sales employee practicing customer service. Using this system, the user can understand how their explanation skills, facial expressions, and voice are perceived by customers. Furthermore, the practice content is tailored to the user's emotions at the time, allowing them to steadily hone their skills. In this way, this system supports efficient and effective skill improvement by precisely addressing individual needs.

[0528] The following describes the processing flow.

[0529] Step 1:

[0530] The user logs into the system using their device and selects the video they want to watch from a list of instructional videos. The server then prepares to stream this video data to the device.

[0531] Step 2:

[0532] The device plays instructional videos received from the server. The user watches these videos and learns from their content, tone of voice, facial expressions, and other information.

[0533] Step 3:

[0534] The user uses their own camera and microphone to record themselves imitating the instructional video. The device then sends the recorded data to the server.

[0535] Step 4:

[0536] The server receives the recorded data and begins analysis to compare it with the instructional video. It uses speech recognition technology to evaluate voice tone and speaking speed, and image analysis technology to analyze similarities in facial expressions and movements.

[0537] Step 5:

[0538] By utilizing an emotion engine, the server recognizes emotions from the user's recorded data. This is estimated from the user's voice and facial expressions.

[0539] Step 6:

[0540] The server combines the evaluation of the imitated behavior with the user's emotional state to generate a scored evaluation and specific areas for improvement. It also adjusts the feedback based on the user's emotions.

[0541] Step 7:

[0542] The terminal visually displays the evaluation results and areas for improvement received from the server to the user. The user reviews this and understands their own performance.

[0543] Step 8:

[0544] Users will conduct imitation training again as needed and strive to further improve their skills based on the evaluation.

[0545] Step 9:

[0546] If the user's mimicked behavior meets the set criteria, the server will set up an interactive training session using the AI ​​avatar.

[0547] Step 10:

[0548] The device runs an AI avatar and begins interactive training with the user. Leveraging the recognition results of the emotion engine, the avatar's dialogue is adjusted to match the user's emotions.

[0549] (Example 2)

[0550] 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."

[0551] Traditional imitation training systems simply evaluate user behavior without providing feedback that takes into account the individual user's emotional state. As a result, users found it difficult to make effective improvements, and skill development tailored to individual needs was challenging.

[0552] 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.

[0553] In this invention, the server includes means for presenting educational videos selected by the user, means for recording the user's imitation behavior, means for analyzing the recorded imitation behavior by comparing it with the educational videos and generating evaluation and improvement guidelines, means for performing sentiment analysis to infer the user's emotional state, and means for adjusting feedback based on the emotional state. This makes it possible to provide feedback optimized for each user and realize effective skill improvement that takes into account individual emotional states.

[0554] "Educational videos" are video content that includes visual and auditory information and is provided for users to watch for the purpose of learning and skill imitation.

[0555] "Imitative behavior" refers to users recreating their own actions based on educational videos and recording these actions with a camera or microphone.

[0556] "Evaluation and Improvement Guidelines" refer to evaluations of the accuracy of user imitation behavior, generated by comparing it with educational videos, as well as specific advice and suggestions for further technical improvement.

[0557] "Emotional analysis" is a technology that infers a user's emotional state from their facial expressions, tone of voice, and other factors, and analyzes the user's inner state.

[0558] "Feedback adjustment" refers to the process of adapting the evaluations and improvement guidelines provided to users, based on the results of sentiment analysis, to their individual emotional states.

[0559] This invention supports efficient skill improvement in an imitation training system that utilizes educational videos by recognizing the user's emotions and providing personalized feedback. The system consists of terminals and servers, which operate in cooperation with each other via a network.

[0560] First, the user logs into the system using their device. During login, the user's authentication information is sent to the server for access authentication. Next, the user selects an educational video from the available options. The selection information is sent from the device to the server, and the server streams the selected educational video to the device.

[0561] Users watch streamed video and learn the content, tone of voice, and actions. After watching, users begin to imitate the actions, and the device records the user's actions using its camera and microphone. The recorded data is sent from the device to a server, which analyzes the data using high-precision speech recognition and image analysis technologies. The analysis evaluates how well the user's imitated actions match the educational video, and generates a scored evaluation and specific improvement guidelines.

[0562] Before the generated feedback is presented to the user, an emotion analysis engine infers the user's emotional state from their facial expressions and tone of voice. Based on this, the content of the feedback is adjusted to suit the individual user's emotional state. This adapted feedback is then presented to the user on the device.

[0563] For example, if a new sales employee uses this system to practice customer service, they can objectively understand how their explanation skills, facial expressions, and language are perceived by customers. As a result, users can effectively improve their skills.

[0564] An example of a prompt might be, "Describe a system used in customer service training for new sales staff that evaluates user mimicry and provides feedback based on their emotional state." This helps the generative AI model create user-optimized feedback.

[0565] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0566] Step 1:

[0567] The user logs into the system using their terminal. During this process, the user enters their user ID and password. The terminal sends this information to the server, which then performs authentication by referencing its authentication database. Upon receiving the authentication result, the terminal verifies whether the user was successfully authenticated. The user then receives a message indicating whether the login was successful or unsuccessful.

[0568] Step 2:

[0569] The user selects an educational video on their device. The device sends the ID of the selected video to the server. The server retrieves the corresponding educational video from its database and begins streaming it to the device. The input is the video ID, and the output is the streaming video.

[0570] Step 3:

[0571] The user watches an educational video on their device and begins to imitate the behavior. The device uses its camera and microphone to record the user's actions and voice. The recorded data is generated as video and audio files, which serve as input for transmission to the server.

[0572] Step 4:

[0573] The server receives recorded data sent from the terminal. It analyzes the audio file using speech recognition technology and converts the content into text. It analyzes the video data using image analysis technology and classifies user actions as specific actions. The output is the analysis results of actions and audio.

[0574] Step 5:

[0575] The server compares the user's imitation behavior with educational videos based on the analysis results. Using a generative AI model, it evaluates the analyzed data and generates a scored evaluation and specific improvement guidelines. This is the output of the step.

[0576] Step 6:

[0577] The server uses an emotion analysis engine to analyze the user's facial expressions and voice tone during recording to infer the user's emotional state. The input is the recorded data, and the output is the inferred emotion information. Based on this information, feedback is adjusted.

[0578] Step 7:

[0579] The device receives and displays tailored feedback to the user. This feedback includes evaluation results, specific improvement guidelines, and advice based on emotional state. Through this output, users can improve their behavior and enhance their skills.

[0580] (Application Example 2)

[0581] 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."

[0582] Traditional imitation training systems lack feedback that takes into account the user's emotional state, making it difficult for users to obtain appropriate improvement strategies that align with their own feelings. Furthermore, the difficulty in real-time evaluation in real-world environments results in a lack of responsiveness in on-site situations.

[0583] 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.

[0584] In this invention, the server includes means for analyzing the user's emotional state and optimizing feedback, means for comparing and evaluating imitation behavior with instructional videos, and means for evaluating behavior in a real environment in real time. This enables the user to receive appropriate feedback according to their emotional state while performing effective training in a real environment.

[0585] "Means for displaying user-selected instructional videos" refers to means for displaying training videos arbitrarily selected by the user on the device.

[0586] "Means for recording user imitation behavior" refers to methods of recording a user's actions when they imitate a video using devices such as cameras and microphones.

[0587] "Means for comparing recorded imitation behavior with instructional videos and generating evaluations and areas for improvement" refers to a means of comparing recorded user behavior with pre-prepared standard videos, evaluating the accuracy of that behavior, and identifying areas for improvement.

[0588] "Means of presenting evaluations and areas for improvement to users" refers to means of presenting the results of behavioral evaluations and improvement suggestions to users through visual and auditory means.

[0589] "Means for analyzing the user's emotional state and optimizing feedback" refers to methods for analyzing the user's emotions from their facial expressions and tone of voice, and providing appropriate feedback that corresponds to those emotions.

[0590] "Means of providing interactive training when imitative behavior meets evaluation criteria" refers to a means of providing interactive exercises as a higher-level training when the user's performance meets the criteria.

[0591] "Means for evaluating behavior in a real-world environment in real time" refers to means for sequentially evaluating user behavior in a real environment and providing immediate feedback on the results.

[0592] As part of this invention, the server streams instructional videos for imitation training selected by the user to the terminal. The user records their actions using smart glasses or other video recording devices. The recorded video data is analyzed using speech recognition and image analysis technologies and sent to the server.

[0593] The server compares recorded imitated behavior with instructional videos, evaluates the accuracy of the behavior, and generates areas for improvement. For further analysis, the server analyzes the user's emotional state using an emotion engine and tailors the feedback to individual needs. The feedback is transmitted to the terminal and presented to the user. For example, if an emotional state indicating tension is detected, relaxation methods are suggested.

[0594] If the imitation behavior meets the evaluation criteria, the server provides AI-driven interactive training, which can be adjusted based on user interaction. Users can receive evaluation results and training in real time via smart glasses or mobile devices. For this purpose, analysis software such as OpenCV and TensorFlow, as well as the Emotion Recognition API, are used.

[0595] As a concrete example, consider a scenario where sales staff are being trained on how to explain a new product. Using this system, users can evaluate their own explanation skills and nonverbal communication in a simulated environment and identify areas for improvement. Based on a prompt such as, "You are undergoing product introduction training. If you feel nervous, how would you suggest ways to relax?", the user is presented with feedback provided by a generative AI model.

[0596] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0597] Step 1:

[0598] The user logs into the system using a terminal and selects a training instruction video. The terminal sends the selected video as a request to the server. The input is the identification information of the selected instruction video, and the output is the streaming request to the server.

[0599] Step 2:

[0600] The server streams instructional videos to the terminal. The terminal receives the video and displays it to the user. The input here is the data stream of the instructional video, and the output is the video that the user can visually confirm. The server uses streaming technology to convert the video into an appropriate format.

[0601] Step 3:

[0602] The user records their mimicked actions using smart glasses or another capture device. The input is the user's behavior data, and the output is the recorded video and audio data. The user saves this to the device's memory.

[0603] Step 4:

[0604] The terminal sends recorded mimicry data to the server. The server receives this data and prepares it for analysis. The input is recorded behavior data, and the output is a dataset for analysis.

[0605] Step 5:

[0606] The server uses speech recognition and image analysis to compare recorded imitation behavior with instructional videos and generate an evaluation. Necessary data processing includes motion position detection and speech intonation analysis. Inputs are user behavior data and instructional videos, and output is an evaluation result regarding the accuracy of the behavior.

[0607] Step 6:

[0608] The server uses an emotion engine to analyze the user's emotional state and optimize the feedback. Input is the user's facial expressions and voice tone data, and output is the emotion evaluation result. Based on the emotion evaluation, a generative AI model is used to adjust the feedback.

[0609] Step 7:

[0610] The server generates suggestions for improvement based on the final evaluation and emotional state, and presents them to the user via the terminal. The input here is evaluation and emotional data, and the output is a personalized feedback message. The feedback is displayed as audio or visual.

[0611] Step 8:

[0612] If the imitation behavior meets the criteria, the server provides interactive training using an AI avatar. The user experiences this training on their device. The input is the evaluation result, and the output is the interactive training session. The AI ​​avatar interacts with the user using prompts, offering encouragement and guidance.

[0613] 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.

[0614] 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.

[0615] 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.

[0616] [Fourth Embodiment]

[0617] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0618] 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.

[0619] 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).

[0620] 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.

[0621] 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.

[0622] 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).

[0623] 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.

[0624] 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.

[0625] 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.

[0626] 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.

[0627] 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.

[0628] 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.

[0629] 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".

[0630] This invention is a training system for users to improve specific skills and abilities. This system is implemented by connecting a user terminal to a server via a network. The user uses their terminal to select and view the necessary instructional videos.

[0631] The server streams instructional videos selected by the user to the device. These videos include both audio and video, allowing the user to learn from the content, tone of voice, facial expressions, and other details. The user then imitates what they have learned and records their imitated actions using the camera and microphone on their device.

[0632] Recorded imitation behaviors are sent from the terminal to the server. On the server side, speech recognition and image analysis technologies are used to compare the imitation behaviors with instructional videos and analyze their accuracy and similarity. Based on the results of this analysis, the server generates an evaluation of the imitation behavior and specific areas for improvement.

[0633] The evaluation results and areas for improvement are sent to the user's device, allowing them to visually review them. This enables the user to understand their progress and initiate further improvements.

[0634] Furthermore, if the imitation accuracy meets a certain standard, the server will provide an interactive training session. In this session, the AI ​​avatar will interact with the user as a customer, allowing for practice in realistic explanations and presentations.

[0635] For example, if a new sales employee wants to improve their skills in introducing a new product to a customer, they would first watch a training video on product introductions and then practice by imitating it. The system would then evaluate the results and suggest areas for improvement, providing training that more closely resembles a real-world situation. This allows users to objectively analyze their individual performance and efficiently improve their skills.

[0636] The following describes the processing flow.

[0637] Step 1:

[0638] The user logs into the system using their device. The server verifies the user's authentication information and provides the device with a list of instructional videos that the user can access.

[0639] Step 2:

[0640] The user selects an instructional video through their device. The server generates a streaming URL for the selected video and sends it to the device. The device then uses this URL to play the video.

[0641] Step 3:

[0642] Users watch instructional videos, learning from the content, tone of voice, and facial expressions. They then use their own camera and microphone to record themselves imitating the actions shown in the videos.

[0643] Step 4:

[0644] The device records the user's mimicked behavior and sends the data to the server. The server stores the received recorded data.

[0645] Step 5:

[0646] The server uses speech recognition and image analysis technologies to compare recorded data with instructional videos. Specifically, it analyzes voice tone, speaking speed, and the degree of matching of facial expressions.

[0647] Step 6:

[0648] Based on the analysis results, the server scores and evaluates the user's imitation behavior. It also generates specific points for improvement.

[0649] Step 7:

[0650] The server sends evaluation results and areas for improvement to the terminal. The terminal displays this information to the user, allowing the user to check their own performance.

[0651] Step 8:

[0652] Based on feedback from the server, users set their next practice goals and work towards further improvement.

[0653] Step 9:

[0654] If the user's mimicry meets certain criteria, the server prepares to start an interactive training session using the AI ​​avatar.

[0655] Step 10:

[0656] The device provides users with an interactive practice environment through an AI avatar, enhancing their smooth communication skills in realistic scenarios.

[0657] (Example 1)

[0658] 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".

[0659] Traditional skill improvement systems primarily rely on self-assessment by users, making it difficult to receive objective feedback or detailed improvement strategies. Furthermore, the accuracy of imitation is unclear, making it difficult to determine how further improvement is possible. As a result, there is a problem in that users' skill improvement is not efficient.

[0660] 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.

[0661] In this invention, the server includes means for providing educational videos selected by the user, means for recording the user's imitation behavior, means for comparing the recorded imitation behavior with the educational videos and generating evaluations and areas for improvement, means for providing the evaluations and areas for improvement to the user, and means for generating specific feedback using generative AI technology. This allows the user to objectively understand the accuracy of their imitation and obtain detailed feedback and areas for improvement.

[0662] "Educational videos" are videos containing visual and auditory information designed to help learners acquire specific skills or knowledge.

[0663] "Imitative behavior" refers to self-expressive behaviors such as actions and speech that users perform based on the content of educational videos.

[0664] "Recording" refers to the act of saving imitative behavior as digital data, which is usually done using devices such as cameras and microphones.

[0665] "Evaluation criteria" refer to standards set to quantitatively or qualitatively measure imitative behavior, and serve as criteria for judging accuracy and effectiveness.

[0666] "Interactive learning" refers to a form of learning activity in which users engage interactively, usually through virtual interaction.

[0667] "Generative AI technology" refers to artificial intelligence technology that generates new information based on large amounts of data, and is used to automatically generate specific feedback.

[0668] "Feedback" is the process of evaluating a user's performance and providing suggestions for improvement and advice.

[0669] In this invention, the user first selects and watches an educational video related to a specific skill from a list of videos provided on their device. The device uses its camera and microphone to record the user's imitation behavior. The recorded data is temporarily stored in the device's storage in real time.

[0670] The server provides educational videos selected by the user using streaming technology. Specifically, it uses the H.264 codec for streaming to ensure smooth video playback. In addition, it receives recorded data from the terminal and uses a speech recognition API and image analysis libraries (such as Google Speech-to-Text API, OpenCV, and TensorFlow) to analyze the accuracy of the imitation behavior and its similarity to the educational video.

[0671] Based on the analysis results, the server utilizes generative AI technology to generate an evaluation of the user's mimicked behavior and specific areas for improvement. The generated evaluation is quantified, and the user can visually view it on their device. Based on the feedback, the user can clearly identify areas for improvement and efficiently improve their skills. By using AI technology, the goal is to make the quality of the feedback equivalent to, or even better than, that provided by human professionals.

[0672] Furthermore, if the user's imitation behavior meets certain evaluation criteria, the server provides an opportunity for interactive learning using an AI avatar. In this interactive session, the AI ​​avatar acts as a virtual customer or questioner, engaging in real-time question-and-answer sessions with the user. For example, in a scenario simulating a new product presentation, the user can practice explaining the product's features.

[0673] When using a generative AI model, you can use prompts like the following: "Which educational videos should I prioritize learning to improve my sales skills?" This prompt can help select educational content that meets the user's specific needs.

[0674] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0675] Step 1:

[0676] The user selects a video related to improving a specific skill from a list of educational videos provided on their device. The input is the user's selection, and the output is a link to a specific educational video based on the user's selection. The device then sends a streaming request to the server based on this link.

[0677] Step 2:

[0678] The server receives streaming requests from users and transmits the corresponding educational videos. The input is the video request received from the user, and the output is compressed video data. The server uses H.264 codec technology to stream the video and ensure smooth playback.

[0679] Step 3:

[0680] The user watches a streamed educational video on their device and imitates its content. The input is the educational video, and the user's actions are imitated. The output is a video and audio recording of the user's imitated actions, recorded using the device's camera and microphone. This data is temporarily stored in the device's storage.

[0681] Step 4:

[0682] The device sends recorded mimicry data to the server. The input is the mimicry data stored on the device, and the output is the data sent to the server. The transmission is done via the HTTPS protocol, ensuring data privacy and security.

[0683] Step 5:

[0684] The server performs speech recognition and image analysis on the received imitation behavior data. The input is imitation behavior data, and the output generates accuracy comparison results with educational videos and similarity analysis results. The server uses technologies such as Google Speech-to-Text API, OpenCV, and TensorFlow to precisely evaluate the imitation behavior.

[0685] Step 6:

[0686] The server utilizes a generated AI model based on the analysis results to evaluate the mimicked behavior and generate specific areas for improvement. The input is the analysis results, and the output is an evaluation score and improvement suggestions. This automatically generates feedback.

[0687] Step 7:

[0688] The evaluation results and improvement suggestions from the server are sent to the user's terminal. The input is the evaluation results and improvement suggestions, and the output is information that the user can visually confirm. Based on this information, the user can improve their actions in the next step.

[0689] Step 8:

[0690] If the imitation behavior meets certain evaluation criteria, the server provides an interactive learning session with an AI avatar. The input is the user's evaluation score, and the output is the initiation of an interactive session. The AI ​​avatar functions as a virtual conversation partner, providing an environment for, for example, a virtual product presentation.

[0691] (Application Example 1)

[0692] 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".

[0693] In modern workplaces, employees are required to acquire new skills quickly and efficiently. Furthermore, the lack of immediate feedback on the job and the inefficiency of traditional training methods are problematic. This invention aims to enable employees to rapidly improve their skills by providing an interactive training method.

[0694] 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.

[0695] In this invention, the server includes a device that presents instructional videos selected by the user, a device that records the user's imitative actions, a device that compares the recorded imitative actions with the instructional videos and generates evaluations and points for improvement, a device that presents the evaluations and points for improvement to the user, a device that provides interactive training with an artificial construct when the imitative actions meet evaluation criteria, and a device that is installed on a visual display device and performs action imitation and evaluation in real time. This enables employees to efficiently learn new technologies, immediately grasp their level of proficiency, and receive specific feedback for further improvement.

[0696] A "device that presents user-selected instructional videos" is a device that allows users to watch videos on their own devices to improve specific skills or abilities.

[0697] A "device for recording user imitation behavior" is a device that records a user's actions and vocalizations as they imitate instructional videos.

[0698] A "device that compares recorded imitative behavior with instructional videos and generates evaluations and areas for improvement" is a device that analyzes the similarity between the imitative behavior recorded by the user and the instructional video, and generates feedback based on the results.

[0699] A "device for presenting evaluations and areas for improvement to users" is a device that presents evaluations based on analysis results and specific improvement suggestions to users visually or audibly.

[0700] A "device that provides interactive training with an artificial construct" is a device that enables an AI or virtual agent to conduct various types of training through real-time interaction with a user.

[0701] A "device installed in a visual display device that performs motion imitation and evaluation in real time" refers to a device installed in smart glasses or a headset that records the user's movements in real time and performs immediate evaluation.

[0702] The system designed to realize this invention basically consists of a user's visual display device and a server. Smart glasses or a headset are used as the visual display device. When the user selects an instructional video, the server streams this video in real time and presents it to the user's visual display device.

[0703] When a user performs an imitative action, the camera and microphone on the visual display device record the action and sound. Once recording is complete, the data is sent to a server. The server then uses speech recognition and image analysis technologies to compare the imitative action with the instructional video. Specifically, Google Cloud Speech-to-Text is used for speech recognition, and OpenCV is applied for image analysis.

[0704] The server generates evaluations and areas for improvement based on these comparison results. These evaluations and areas for improvement are returned to the user's visual display device, allowing the user to visually confirm them. If certain criteria are met, the next step is for the server to provide an interactive training session using the generated AI model. This session allows the user to receive more detailed feedback and interactive training.

[0705] A concrete example is when a factory worker learns how to operate a new machine. The worker watches a training video, imitates the movements, and records them using a visual display device. Afterwards, they receive feedback based on how closely their movements match the original video, and if they perform well, they can receive detailed interactive training from AI.

[0706] An example of a prompt message might be: "I need to learn how to operate the new welding machine. I would like to practice with an AI avatar to find out what specific areas for improvement are needed."

[0707] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0708] Step 1:

[0709] The user wears a visual display device and selects training instructional videos from a server via a terminal. The server streams the selected instructional videos to the terminal. The input data is the user's selection information, and the output data is the instructional videos displayed on the user's terminal.

[0710] Step 2:

[0711] The user imitates the actions while watching instructional videos. The device records these imitations using a camera and microphone mounted on the visual display device. The input is the user's actions and vocalizations, and the recorded imitation data is the output.

[0712] Step 3:

[0713] The recorded imitation data is sent from the terminal to the server. The server converts the audio data into text data using speech recognition technology (e.g., Google Cloud Speech-to-Text) and extracts motion features from the video data using image analysis technology (e.g., OpenCV). As a result, the imitation data is output as numerically represented analysis data.

[0714] Step 4:

[0715] The server compares the analyzed data with the instructional video and evaluates their similarity. Based on the comparison results, it calculates specific evaluations and areas for improvement. The inputs are the analyzed data and the feature data of the instructional video, and the output is the evaluation and areas for improvement.

[0716] Step 5:

[0717] The server sends data to the terminal to present the calculated evaluation and areas for improvement to the user. The user reviews the feedback through a visual display device. The input is the evaluation and areas for improvement, and the output is their visual display.

[0718] Step 6:

[0719] If the evaluation meets certain criteria, the server starts an interactive conversational training session with an artificial construct using the generated AI model. The user and the AI ​​avatar interact through voice and video. The input is the user's progress, and the output is the experience gained from the conversational training.

[0720] 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.

[0721] This invention combines an imitation training system using instructional videos with an emotion engine that recognizes user emotions, enabling more personalized feedback and interaction. This system operates with a terminal and server working together via a network. Users log into the system using their terminal and select and view instructional videos.

[0722] The server streams the instructional video selected by the user to the device. The user watches it, learns the content, tone of voice, and facial expressions, and records themselves imitating the actions. The recorded data is then sent from the device to the server.

[0723] The server compares this data with instructional videos, using speech recognition and image analysis technologies to evaluate the accuracy of the imitated behavior. It then generates a scored evaluation of the imitated behavior and specific areas for improvement, which are presented to the user on their device. During this process, an emotion engine is used to analyze the user's emotional state, and the evaluation and improvement points are adjusted based on this analysis.

[0724] The emotion engine infers emotions from the user's facial expressions, tone of voice, and other factors. Based on the recognized emotion information, the server optimizes the feedback content for the user. For example, if it determines that the user is tense, it can include suggestions for relaxation techniques. In this way, the evaluation takes the user's emotional state into account, resulting in more flexible and personalized content.

[0725] Furthermore, if the user's mimicked behavior meets the evaluation criteria, the server provides interactive training using an AI avatar. In this training, the AI ​​avatar adjusts its interaction with the user based on emotional information. For example, if the user is feeling down, the avatar is configured to offer words of encouragement.

[0726] As a concrete example, consider a new sales employee practicing customer service. Using this system, the user can understand how their explanation skills, facial expressions, and voice are perceived by customers. Furthermore, the practice content is tailored to the user's emotions at the time, allowing them to steadily hone their skills. In this way, this system supports efficient and effective skill improvement by precisely addressing individual needs.

[0727] The following describes the processing flow.

[0728] Step 1:

[0729] The user logs into the system using their device and selects the video they want to watch from a list of instructional videos. The server then prepares to stream this video data to the device.

[0730] Step 2:

[0731] The device plays instructional videos received from the server. The user watches these videos and learns from their content, tone of voice, facial expressions, and other information.

[0732] Step 3:

[0733] The user uses their own camera and microphone to record themselves imitating the instructional video. The device then sends the recorded data to the server.

[0734] Step 4:

[0735] The server receives the recorded data and begins analysis to compare it with the instructional video. It uses speech recognition technology to evaluate voice tone and speaking speed, and image analysis technology to analyze similarities in facial expressions and movements.

[0736] Step 5:

[0737] By utilizing an emotion engine, the server recognizes emotions from the user's recorded data. This is estimated from the user's voice and facial expressions.

[0738] Step 6:

[0739] The server combines the evaluation of the imitated behavior with the user's emotional state to generate a scored evaluation and specific areas for improvement. It also adjusts the feedback based on the user's emotions.

[0740] Step 7:

[0741] The terminal visually displays the evaluation results and areas for improvement received from the server to the user. The user reviews this and understands their own performance.

[0742] Step 8:

[0743] Users will conduct imitation training again as needed and strive to further improve their skills based on the evaluation.

[0744] Step 9:

[0745] If the user's mimicked behavior meets the set criteria, the server will set up an interactive training session using the AI ​​avatar.

[0746] Step 10:

[0747] The device runs an AI avatar and begins interactive training with the user. Leveraging the recognition results of the emotion engine, the avatar's dialogue is adjusted to match the user's emotions.

[0748] (Example 2)

[0749] 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".

[0750] Traditional imitation training systems simply evaluate user behavior without providing feedback that takes into account the individual user's emotional state. As a result, users found it difficult to make effective improvements, and skill development tailored to individual needs was challenging.

[0751] 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.

[0752] In this invention, the server includes means for presenting educational videos selected by the user, means for recording the user's imitation behavior, means for analyzing the recorded imitation behavior by comparing it with the educational videos and generating evaluation and improvement guidelines, means for performing sentiment analysis to infer the user's emotional state, and means for adjusting feedback based on the emotional state. This makes it possible to provide feedback optimized for each user and realize effective skill improvement that takes into account individual emotional states.

[0753] "Educational videos" are video content that includes visual and auditory information and is provided for users to watch for the purpose of learning and skill imitation.

[0754] "Imitative behavior" refers to users recreating their own actions based on educational videos and recording these actions with a camera or microphone.

[0755] "Evaluation and Improvement Guidelines" refer to evaluations of the accuracy of user imitation behavior, generated by comparing it with educational videos, as well as specific advice and suggestions for further technical improvement.

[0756] "Emotional analysis" is a technology that infers a user's emotional state from their facial expressions, tone of voice, and other factors, and analyzes the user's inner state.

[0757] "Feedback adjustment" refers to the process of adapting the evaluations and improvement guidelines provided to users, based on the results of sentiment analysis, to their individual emotional states.

[0758] This invention supports efficient skill improvement in an imitation training system that utilizes educational videos by recognizing the user's emotions and providing personalized feedback. The system consists of terminals and servers, which operate in cooperation with each other via a network.

[0759] First, the user logs into the system using their device. During login, the user's authentication information is sent to the server for access authentication. Next, the user selects an educational video from the available options. The selection information is sent from the device to the server, and the server streams the selected educational video to the device.

[0760] Users watch streamed video and learn the content, tone of voice, and actions. After watching, users begin to imitate the actions, and the device records the user's actions using its camera and microphone. The recorded data is sent from the device to a server, which analyzes the data using high-precision speech recognition and image analysis technologies. The analysis evaluates how well the user's imitated actions match the educational video, and generates a scored evaluation and specific improvement guidelines.

[0761] Before the generated feedback is presented to the user, an emotion analysis engine infers the user's emotional state from their facial expressions and tone of voice. Based on this, the content of the feedback is adjusted to suit the individual user's emotional state. This adapted feedback is then presented to the user on the device.

[0762] For example, if a new sales employee uses this system to practice customer service, they can objectively understand how their explanation skills, facial expressions, and language are perceived by customers. As a result, users can effectively improve their skills.

[0763] An example of a prompt might be, "Describe a system used in customer service training for new sales staff that evaluates user mimicry and provides feedback based on their emotional state." This helps the generative AI model create user-optimized feedback.

[0764] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0765] Step 1:

[0766] The user logs into the system using their terminal. During this process, the user enters their user ID and password. The terminal sends this information to the server, which then performs authentication by referencing its authentication database. Upon receiving the authentication result, the terminal verifies whether the user was successfully authenticated. The user then receives a message indicating whether the login was successful or unsuccessful.

[0767] Step 2:

[0768] The user selects an educational video on their device. The device sends the ID of the selected video to the server. The server retrieves the corresponding educational video from its database and begins streaming it to the device. The input is the video ID, and the output is the streaming video.

[0769] Step 3:

[0770] The user watches an educational video on their device and begins to imitate the behavior. The device uses its camera and microphone to record the user's actions and voice. The recorded data is generated as video and audio files, which serve as input for transmission to the server.

[0771] Step 4:

[0772] The server receives recorded data sent from the terminal. It analyzes the audio file using speech recognition technology and converts the content into text. It analyzes the video data using image analysis technology and classifies user actions as specific actions. The output is the analysis results of actions and audio.

[0773] Step 5:

[0774] The server compares the user's imitation behavior with educational videos based on the analysis results. Using a generative AI model, it evaluates the analyzed data and generates a scored evaluation and specific improvement guidelines. This is the output of the step.

[0775] Step 6:

[0776] The server uses an emotion analysis engine to analyze the user's facial expressions and voice tone during recording to infer the user's emotional state. The input is the recorded data, and the output is the inferred emotion information. Based on this information, feedback is adjusted.

[0777] Step 7:

[0778] The device receives and displays tailored feedback to the user. This feedback includes evaluation results, specific improvement guidelines, and advice based on emotional state. Through this output, users can improve their behavior and enhance their skills.

[0779] (Application Example 2)

[0780] 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".

[0781] Traditional imitation training systems lack feedback that takes into account the user's emotional state, making it difficult for users to obtain appropriate improvement strategies that align with their own feelings. Furthermore, the difficulty in real-time evaluation in real-world environments results in a lack of responsiveness in on-site situations.

[0782] 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.

[0783] In this invention, the server includes means for analyzing the user's emotional state and optimizing feedback, means for comparing and evaluating imitation behavior with instructional videos, and means for evaluating behavior in a real environment in real time. This enables the user to receive appropriate feedback according to their emotional state while performing effective training in a real environment.

[0784] "Means for displaying user-selected instructional videos" refers to means for displaying training videos arbitrarily selected by the user on the device.

[0785] "Means for recording user imitation behavior" refers to methods of recording a user's actions when they imitate a video using devices such as cameras and microphones.

[0786] "Means for comparing recorded imitation behavior with instructional videos and generating evaluations and areas for improvement" refers to a means of comparing recorded user behavior with pre-prepared standard videos, evaluating the accuracy of that behavior, and identifying areas for improvement.

[0787] "Means of presenting evaluations and areas for improvement to users" refers to means of presenting the results of behavioral evaluations and improvement suggestions to users through visual and auditory means.

[0788] "Means for analyzing the user's emotional state and optimizing feedback" refers to methods for analyzing the user's emotions from their facial expressions and tone of voice, and providing appropriate feedback that corresponds to those emotions.

[0789] "Means of providing interactive training when imitative behavior meets evaluation criteria" refers to a means of providing interactive exercises as a higher-level training when the user's performance meets the criteria.

[0790] "Means for evaluating behavior in a real-world environment in real time" refers to means for sequentially evaluating user behavior in a real environment and providing immediate feedback on the results.

[0791] As part of this invention, the server streams instructional videos for imitation training selected by the user to the terminal. The user records their actions using smart glasses or other video recording devices. The recorded video data is analyzed using speech recognition and image analysis technologies and sent to the server.

[0792] The server compares recorded imitated behavior with instructional videos, evaluates the accuracy of the behavior, and generates areas for improvement. For further analysis, the server analyzes the user's emotional state using an emotion engine and tailors the feedback to individual needs. The feedback is transmitted to the terminal and presented to the user. For example, if an emotional state indicating tension is detected, relaxation methods are suggested.

[0793] If the imitation behavior meets the evaluation criteria, the server provides AI-driven interactive training, which can be adjusted based on user interaction. Users can receive evaluation results and training in real time via smart glasses or mobile devices. For this purpose, analysis software such as OpenCV and TensorFlow, as well as the Emotion Recognition API, are used.

[0794] As a concrete example, consider a scenario where sales staff are being trained on how to explain a new product. Using this system, users can evaluate their own explanation skills and nonverbal communication in a simulated environment and identify areas for improvement. Based on a prompt such as, "You are undergoing product introduction training. If you feel nervous, how would you suggest ways to relax?", the user is presented with feedback provided by a generative AI model.

[0795] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0796] Step 1:

[0797] The user logs into the system using a terminal and selects a training instruction video. The terminal sends the selected video as a request to the server. The input is the identification information of the selected instruction video, and the output is the streaming request to the server.

[0798] Step 2:

[0799] The server streams instructional videos to the terminal. The terminal receives the video and displays it to the user. The input here is the data stream of the instructional video, and the output is the video that the user can visually confirm. The server uses streaming technology to convert the video into an appropriate format.

[0800] Step 3:

[0801] The user records their mimicked actions using smart glasses or another capture device. The input is the user's behavior data, and the output is the recorded video and audio data. The user saves this to the device's memory.

[0802] Step 4:

[0803] The terminal sends recorded mimicry data to the server. The server receives this data and prepares it for analysis. The input is recorded behavior data, and the output is a dataset for analysis.

[0804] Step 5:

[0805] The server uses speech recognition and image analysis to compare recorded imitation behavior with instructional videos and generate an evaluation. Necessary data processing includes motion position detection and speech intonation analysis. Inputs are user behavior data and instructional videos, and output is an evaluation result regarding the accuracy of the behavior.

[0806] Step 6:

[0807] The server uses an emotion engine to analyze the user's emotional state and optimize the feedback. Input is the user's facial expressions and voice tone data, and output is the emotion evaluation result. Based on the emotion evaluation, a generative AI model is used to adjust the feedback.

[0808] Step 7:

[0809] The server generates suggestions for improvement based on the final evaluation and emotional state, and presents them to the user via the terminal. The input here is evaluation and emotional data, and the output is a personalized feedback message. The feedback is displayed as audio or visual.

[0810] Step 8:

[0811] If the imitation behavior meets the criteria, the server provides interactive training using an AI avatar. The user experiences this training on their device. The input is the evaluation result, and the output is the interactive training session. The AI ​​avatar interacts with the user using prompts, offering encouragement and guidance.

[0812] 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.

[0813] 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.

[0814] 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.

[0815] 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.

[0816] 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.

[0817] 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.

[0818] 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.

[0819] 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.

[0820] 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."

[0821] 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.

[0822] 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.

[0823] 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.

[0824] 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.

[0825] 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.

[0826] 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.

[0827] 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.

[0828] 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.

[0829] 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.

[0830] 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.

[0831] 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.

[0832] 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.

[0833] The following is further disclosed regarding the embodiments described above.

[0834] (Claim 1)

[0835] A means of presenting instructional videos selected by the user,

[0836] Means for recording user imitation behavior,

[0837] A means for comparing recorded imitative behavior with instructional videos to generate evaluations and areas for improvement,

[0838] A means of presenting evaluations and areas for improvement to users,

[0839] When imitative behavior meets the evaluation criteria, a means of providing a setting for interactive training is provided.

[0840] A system that includes this.

[0841] (Claim 2)

[0842] The system according to claim 1, further comprising means for generating a scored evaluation of a user's imitative behavior.

[0843] (Claim 3)

[0844] The system according to claim 1, further comprising means for analyzing mimicry behavior using speech recognition and image analysis technologies.

[0845] "Example 1"

[0846] (Claim 1)

[0847] A means of providing educational videos selected by the user,

[0848] Means for recording users' imitation behavior,

[0849] A means for comparing recorded imitation behavior with educational videos and generating evaluations and areas for improvement,

[0850] Means for providing evaluations and suggestions for improvement to users,

[0851] When imitative behavior meets the evaluation criteria, a means of providing opportunities for interactive learning,

[0852] A means of generating specific feedback using generative AI technology,

[0853] A system that includes this.

[0854] (Claim 2)

[0855] The system according to claim 1, further comprising means for generating a scored evaluation of a user's imitative behavior.

[0856] (Claim 3)

[0857] The system according to claim 1, further comprising means for analyzing mimicry behavior using speech recognition and image analysis techniques.

[0858] "Application Example 1"

[0859] (Claim 1)

[0860] A device that displays instructional videos selected by the user,

[0861] A device that records the user's mimicked behavior,

[0862] A device that compares recorded imitative behavior with instructional videos, generates evaluations and points for improvement,

[0863] A device that presents evaluations and areas for improvement to the user,

[0864] A device that provides interactive training with an artificial construct when the imitative behavior meets the evaluation criteria,

[0865] A device installed on a visual display device that performs motion imitation and evaluation in real time,

[0866] A system that includes this.

[0867] (Claim 2)

[0868] The system according to claim 1, further comprising means for generating a quantified evaluation of a user's imitative behavior.

[0869] (Claim 3)

[0870] The system according to claim 1, further comprising means for analyzing mimicry behavior using speech recognition and image analysis technologies.

[0871] "Example 2 of combining an emotion engine"

[0872] (Claim 1)

[0873] A means of presenting educational videos selected by the user,

[0874] Means for recording user imitation behavior,

[0875] A means for analyzing recorded imitative behavior by comparing it with educational videos, and for generating evaluation and improvement guidelines,

[0876] A means of presenting the generated evaluation and improvement guidelines to the user,

[0877] A means of providing opportunities for interactive training when imitative behavior meets the evaluation criteria,

[0878] A means of performing sentiment analysis to infer the emotional state of a user,

[0879] A means of adjusting feedback based on the user's emotional state,

[0880] A system that includes this.

[0881] (Claim 2)

[0882] The system according to claim 1, further comprising means for generating a quantitative evaluation of the user's imitative behavior.

[0883] (Claim 3)

[0884] The system according to claim 1, further comprising means for analyzing mimicry behavior using speech recognition and image analysis techniques.

[0885] "Application example 2 when combining with an emotional engine"

[0886] (Claim 1)

[0887] A means of presenting instructional videos selected by the user,

[0888] Means for recording user imitation behavior,

[0889] A means for comparing recorded imitative behavior with instructional videos to generate evaluations and areas for improvement,

[0890] A means of presenting evaluations and areas for improvement to users,

[0891] A means of analyzing the user's emotional state and optimizing feedback,

[0892] When the imitative behavior meets the evaluation criteria, a means of providing interactive training,

[0893] A means of evaluating behavior in a real-world environment in real time,

[0894] A system that includes this.

[0895] (Claim 2)

[0896] The system according to claim 1, further comprising means for generating a scored evaluation of a user's imitative behavior.

[0897] (Claim 3)

[0898] The system according to claim 1, further comprising means for analyzing mimicry behavior using speech recognition and image analysis technologies. [Explanation of Symbols]

[0899] 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 of presenting instructional videos selected by the user, Means for recording user imitation behavior, A means for comparing recorded imitative behavior with instructional videos to generate evaluations and areas for improvement, A means of presenting evaluations and areas for improvement to users, When imitative behavior meets the evaluation criteria, a means of providing a setting for interactive training is provided. A system that includes this.

2. The system according to claim 1, further comprising means for generating a scored evaluation of a user's imitative behavior.

3. The system according to claim 1, further comprising means for analyzing mimicry behavior using speech recognition and image analysis technologies.

Images