system

Abstract

We provide the system. [Solution] To take in existing information, analyze it, and convert specific data into three-dimensional images. Means for incorporating existing education or training information, A method for analyzing captured information using AI to identify objects to be visualized in three dimensions, A means for generating a three-dimensional image of a specified object and reproducing its movements, A means for transmitting the generated three-dimensional image to a terminal, A means of displaying and enabling operation of three-dimensional images on a terminal, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance 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 conventional education and training methods, there is a problem that it is difficult for viewers to effectively convey physical actions and skills that are visually difficult to understand, especially three-dimensional actions. In particular, in order to inherit craftsmanship and deepen the understanding of physical phenomena, it is necessary to convey information from multiple angles, but there are limitations in ordinary planar videos.

Means for Solving the Problems

[0005] To address this challenge, the present invention provides a system that uses AI to analyze existing educational and training information and convert it into three-dimensional images. Specifically, it takes in information, identifies objects to be visualized in three dimensions through AI analysis, and generates three-dimensional images of those objects. The generated three-dimensional images are transmitted to a terminal via a network, allowing users to gain an interactive learning experience that can be visually manipulated from multiple angles. This system makes it possible to effectively transmit craftsmanship and physical phenomena, thereby improving educational effectiveness.

[0006] "Existing educational or training information" refers to all information provided for educational or training purposes in formats such as text, images, audio, and video.

[0007] "Ingestion" refers to the process of receiving information from an external source and storing it in an internal system.

[0008] "Analysis" refers to the process of thoroughly examining information to understand its content and structure, and then evaluating it according to a specific purpose.

[0009] "AI" is an abbreviation for artificial intelligence, and refers to technology that imitates human intellectual behavior and analyzes and processes data.

[0010] "Three-dimensional imagery" refers to a three-dimensional visual representation with depth, allowing viewers to obtain information from multiple angles.

[0011] "Generation" refers to the process of transforming existing information to create data in a new format.

[0012] "Transmitting" refers to moving data from its original location to its destination location.

[0013] A "terminal" refers to a computer or digital device used as part of a system.

[0014] "Operation" refers to the input given in order to operate a machine or system.

[0015] "Interactive learning experience" refers to an experience where learners actively participate and deepen their learning content through two-way interaction with the system.

Brief Explanation of Drawings

[0016] [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 multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple 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 the emotion engine is combined. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when combined with an emotion engine.

Embodiments for Carrying Out the Invention

[0017] 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.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0020] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0022] 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).

[0023] 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."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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".

[0037] One embodiment of the present invention is a system that uses AI to convert existing educational or training information into three-dimensional images, thereby providing viewers with an effective learning experience.

[0038] This system's program incorporates diverse information provided by educational institutions and companies, analyzes it, and determines what to visualize in 3D. AI technology forms the core of the system, extracting necessary data from the collected information and generating a three-dimensional model. The generated 3D image data is delivered to the user's terminal via the network. The user can manipulate this 3D image from various angles using their terminal, allowing students and trainees to deepen their understanding through interactive manipulation.

[0039] For example, in welding technique training, the server captures video footage demonstrating welding procedures, and AI analyzes key actions related to those procedures. Next, the server converts this into a three-dimensional model and generates video footage that recreates specific hand movements and welding rod manipulation. The generated video is sent to the user's terminal, enabling the user to gain a visual and three-dimensional understanding of actual welding techniques.

[0040] This system aims to improve the efficiency of information transmission in education and training, particularly enhancing the learning effectiveness of complex physical movements. In the transmission of craftsmanship and procedures, information that is difficult to convey with existing two-dimensional materials can be learned more intuitively through three-dimensional visualization. The use of this system is expected to simultaneously increase learners' understanding and interest in educational settings and corporate training facilities.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The server retrieves information related to education and training from the database and imports it into the system. During the import process, it checks the file format and classifies each data item into categories such as text, images, audio, and video.

[0044] Step 2:

[0045] The server analyzes the collected information using AI. This analysis process involves extracting key points from text information using natural language processing and recognizing important movements from images and videos using image recognition technology. Based on the analysis results, it identifies objects that should be converted into 3D images.

[0046] Step 3:

[0047] The server generates three-dimensional video data based on the object identified by the AI. It creates a three-dimensional model from image and video data, and uses motion capture technology to reproduce actual movements, performing realistic simulations.

[0048] Step 4:

[0049] The server compresses the generated 3D video data to efficiently distribute it. The compressed data is then configured to be sent to the user's terminal via the network.

[0050] Step 5:

[0051] Users receive 3D video transmitted from a server using their terminals and view it through a dedicated display application. The application provides an interface that allows users to observe the 3D video from various angles and manipulate the video from any viewpoint.

[0052] Step 6:

[0053] The terminal records user activity logs and sends the collected information to the server as feedback. Based on this feedback data, the server analyzes the user's learning effectiveness and provides the user with necessary improvements or additional resources.

[0054] (Example 1)

[0055] 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."

[0056] In education and training, it is difficult to convey complex technologies and procedures to learners in an easily understandable way. Furthermore, two-dimensional materials and videos make it difficult to fully understand the details of physical actions, limiting learning effectiveness. Additionally, there is insufficient evaluation of learning effectiveness using learners' operation history.

[0057] 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.

[0058] In this invention, the server includes means for collecting and importing information from educational institutions and companies, means for analyzing the imported information using machine learning algorithms and visual information analysis techniques to extract important features, and means for generating a three-dimensional model using computer graphics software based on the extracted features. This enables learners to effectively learn techniques and procedures through interactive and visual three-dimensional images, and also enables the evaluation of learning effectiveness using operation history.

[0059] "Educational institutions and companies" refers to schools that conduct educational activities and companies that provide training.

[0060] "Collecting information" means the act of selecting and acquiring necessary data.

[0061] A "machine learning algorithm" refers to a computational method that analyzes data and extracts patterns and features.

[0062] "Visual information analysis technology" refers to techniques for extracting useful information from images and videos.

[0063] "Key features" refer to data characteristics that should be given particular attention for a specific purpose.

[0064] "Computer graphics software" refers to programs used to generate and display three-dimensional models.

[0065] A "three-dimensional model" is a digital representation that can be displayed in three dimensions, and its purpose is to visually reproduce actual physical objects and actions.

[0066] "Optimization" refers to the act of adjusting data and processing methods to operate efficiently.

[0067] "User terminal" refers to electronic devices such as computers and tablets used by learners.

[0068] An "interactive learning experience" refers to a learning method in which users can understand the content while directly interacting with it.

[0069] "Operation history" refers to data that records a series of operations performed by a user, and is used to understand the learning progress.

[0070] This invention is a system for providing effective learning experiences using educational information in educational institutions and training facilities. The technologies and processes used in implementing this system are described below.

[0071] The server collects diverse information provided by educational institutions and companies and stores it in a database. This information includes text, images, and videos. The server then utilizes machine learning algorithms and visual information analysis techniques to analyze the collected information. Specifically, it uses natural language processing (NLP) techniques to analyze text information and computer vision techniques to analyze visual information. During this analysis process, important features relevant to the learning objectives are extracted.

[0072] Based on the extracted features, the server uses computer graphics software (e.g., Blender or Unity) to generate a 3D model. The generated 3D model is then optimized to run efficiently on the user's device. This optimization utilizes rendering and data compression techniques.

[0073] The optimized 3D model is delivered to the user's device via the network. The user can view and interactively manipulate the received 3D model on their device. This allows users to deepen their understanding of educational information through a visually engaging experience.

[0074] To give a specific example, in welding technique training, the server captures video footage demonstrating the welding procedure, uses AI to analyze key movements, generates a three-dimensional model, and reproduces hand movements and welding rod manipulation. Users can visually confirm this on their devices and learn while experiencing realistic welding techniques.

[0075] An example of a prompt might be, "Convert the welding procedure into a 3D video. Emphasize important hand movements and tool handling, and visualize it clearly for educational purposes." The system would then take the appropriate action and create the visualization.

[0076] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0077] Step 1:

[0078] The server collects diverse information provided by educational institutions and companies and stores it in a database. Specifically, the server retrieves text, images, videos, etc., from data sources accessible via the network and stores them in the database according to their data format. This process takes educational materials and training videos as input and produces an integrated dataset as output.

[0079] Step 2:

[0080] The server executes machine learning algorithms to analyze the collected information. This process uses natural language processing techniques to analyze text information and extract key terms and procedures. It also uses computer vision techniques to analyze image and video information and extract relevant visual elements. The input here is the dataset obtained in step 1, and the output is the data with key features extracted.

[0081] Step 3:

[0082] The server uses computer graphics software to generate a three-dimensional model based on the extracted features. At this stage, the features extracted in step 2 are provided as input, and the software is used to construct a three-dimensional model. The output is a three-dimensional model suitable for educational purposes. This model is intended to visualize actual physical actions and procedures.

[0083] Step 4:

[0084] The server optimizes the generated 3D model and prepares it for distribution to the user's terminal. Optimization uses data compression techniques to reduce communication bandwidth and device load. The input is the model generated in step 3, and the output is the transferable optimized model.

[0085] Step 5:

[0086] The terminal receives a three-dimensional model distributed from the server and provides an interface to the learner. Through this interface, the user can observe and manipulate the three-dimensional model. The specific input is the model data from the server, and the output is a visual experience through the interface. Through this interaction, the user can gain a deeper understanding of the educational content.

[0087] Step 6:

[0088] Users interact with a three-dimensional model on their device to deepen their understanding. During this process, users can rotate and zoom the model. The input is the three-dimensional model displayed on the device, and the output is the user's own understanding and learning experience.

[0089] (Application Example 1)

[0090] 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."

[0091] Current education and training systems have a two-dimensional nature to information transmission, limiting learners' understanding and skill development. Furthermore, learning standard procedures for machine control units in factories is difficult, and improved assembly accuracy is particularly required for newly introduced machine control units.

[0092] 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.

[0093] In this invention, the server includes means for acquiring existing educational or training information, means for analyzing the acquired information using AI to identify objects to be visualized in three dimensions, and means for using the three-dimensional images for knowledge learning in machine control units to improve assembly accuracy. This enables the provision of an interactive three-dimensional learning experience and improves assembly accuracy in factories.

[0094] "Means of incorporating existing educational or training information" refers to technologies for acquiring educational information and training materials provided by educational institutions and companies in digital format.

[0095] "A method for identifying objects to be analyzed and converted into 3D images using AI" refers to a process that utilizes artificial intelligence technology to analyze the collected information and determine which content should be converted into 3D images.

[0096] "Means for generating three-dimensional images and reproducing actions" refers to a function that creates a three-dimensional image model based on identified information and visually reproduces the associated actions.

[0097] "Means for transmitting generated three-dimensional images to a terminal" refers to technology that delivers the created three-dimensional image data to an appropriate receiving device via a network.

[0098] "Means for displaying and enabling manipulation of three-dimensional images on a terminal" refers to an interface function that displays the received three-dimensional image on the terminal's display, allowing the viewer to freely manipulate the image.

[0099] "A means of improving assembly accuracy by utilizing three-dimensional images for knowledge learning in machine control units" refers to a technology that uses three-dimensional image data to enable machine control units to effectively learn and improve accuracy and efficiency in actual work.

[0100] The system for realizing this invention includes a process for converting educational and training information into 3D images. Specifically, a server digitally acquires information provided by educational institutions and companies. Next, the server uses AI software (e.g., TENSORFLOW® or PyTorch) to analyze the acquired information and identify which content should be converted into 3D images. In this process, important actions and procedures are extracted. Then, a 3D modeling tool (e.g., Unity or Blender) is used to generate a 3D model based on the identified information.

[0101] The generated three-dimensional video data is transmitted to the user's terminal via the network. The terminal displays the received video on its screen and allows the user to manipulate the video. This manipulation includes changing the viewpoint and zooming in and out. This allows the user to understand the educational content visually and intuitively. Furthermore, in factory machine control units, three-dimensional video can be used for learning and to improve assembly accuracy.

[0102] As a concrete example, this system is used in factories when new employees learn the work procedures of experienced workers. Through the generated three-dimensional images, new employees can intuitively grasp the procedures and techniques. An example of a prompt message would be, "Convert the assembly procedure for automobile parts into a three-dimensional image and analyze the specific procedures and actions to teach to new robot A." In this way, it is expected that the effectiveness of education and training will be maximized.

[0103] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0104] Step 1:

[0105] The server digitally retrieves educational information and training materials provided by educational institutions and companies. Input is digital data such as PDFs and video files, which are stored in an internal database. Output is the stored digital educational information.

[0106] Step 2:

[0107] The server uses AI software to analyze the captured digital information and identify the objects to be visualized in 3D. The input is the digital information captured in step 1, and a specific algorithm is used to extract important actions and procedures from this information. The output is a set of information to be visualized in 3D.

[0108] Step 3:

[0109] The server uses a 3D modeling tool to convert the identified information into a 3D model. The input is the information extracted in step 2, and a model generation algorithm is applied to create a 3D image. The output is the completed 3D model data. Specifically, the 3D shape and color information are arranged.

[0110] Step 4:

[0111] The server transmits the generated 3D model data to the user's terminal via the network. The input is the 3D model data obtained in step 3, which is compressed into an appropriate format before transmission. The output is the 3D model data displayed on the user's terminal.

[0112] Step 5:

[0113] The terminal displays the received 3D image on its screen, allowing the user to freely manipulate it. The input is the 3D data received in step 4, and interactive features such as viewpoint change and zoom in / zoom out are provided to give the user an interactive experience. The output is the screen display of the 3D image that the user is manipulating.

[0114] Step 6:

[0115] The system records the user's operation history on the terminal, and the server uses this data to evaluate the user's learning effectiveness. The input is user operation history data, and data analysis techniques are used to quantify learning progress and comprehension. The output is an evaluation result showing the learning effectiveness.

[0116] 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.

[0117] One embodiment of the present invention is a system that uses AI to convert existing educational or training information into three-dimensional images and combines this with a user's emotion engine to further enhance the learning experience. This system analyzes the acquired information, generates specific data as three-dimensional images, and in addition recognizes the user's emotions, dynamically adjusting the images and presentation content based on the user's reactions.

[0118] The program's processing involves the server first ingesting education and training-related data and analyzing it using AI. This analysis determines which information should be visualized as a hologram. Next, the server generates a three-dimensional image based on the identified information and sends it to the user's terminal.

[0119] Users can receive this 3D video using their devices and interact with it interactively through the interface. Furthermore, the devices are equipped with an emotion engine that uses sensors such as cameras and microphones to analyze the user's facial expressions and voice information in real time and send the results to the server. Based on this emotion data, the server evaluates the user's level of understanding and concentration, and customizes and redistributes the video and presented content. This process allows users to learn at their own pace, improving the effectiveness of the education.

[0120] A concrete example is in history education, where users experience historical events and culture through 3D video, and an emotion engine detects the user's interest and surprise. The next content presented then becomes more detailed or new perspectives are added in response to that reaction. This feature allows users to engage in more immersive learning, improving the quality of their learning.

[0121] This system aims to provide a deep and effective learning process tailored to individual learners, rather than simply transmitting knowledge in educational and training settings.

[0122] The following describes the processing flow.

[0123] Step 1:

[0124] The server retrieves information related to education and training from the database and imports it into the system. The imported information is classified into text, images, audio, video, etc., and prepared for analysis.

[0125] Step 2:

[0126] The server analyzes the collected information using AI. The analysis extracts key points from text information using natural language processing and detects important actions from images and videos using image recognition. Based on these analysis results, it determines which content should be represented as a three-dimensional image.

[0127] Step 3:

[0128] The server generates three-dimensional video data based on the identified information. The generation process converts image data into a three-dimensional model and adds motion simulations to create realistic images. This three-dimensional video is then prepared for later access by the user.

[0129] Step 4:

[0130] The server compresses the generated 3D video and prepares it for distribution to the terminal. The data is optimized and transmitted for efficient distribution over the network.

[0131] Step 5:

[0132] The user receives three-dimensional video using a device and displays it through a dedicated application. The application provides an interface that allows the user to view the video from multiple angles and freely change their viewpoint.

[0133] Step 6:

[0134] The device uses its camera and microphone to monitor the user's facial expressions and voice in real time, and analyzes them through an emotion engine. Once the user's emotional state is identified, the analysis results are sent to the server.

[0135] Step 7:

[0136] The server evaluates the user's learning progress and level of interest based on data obtained from the emotion engine. It dynamically adjusts 3D images and presentation content as needed, generating and redistributing optimal learning content to the user. This process allows users to continue learning while maintaining their interest.

[0137] (Example 2)

[0138] 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".

[0139] In traditional education and training settings, standardized information delivery was prevalent, making it difficult to customize content to suit the individual learner's level of understanding and interests. In particular, there was a lack of means to dynamically adjust content based on learners' emotional states using visually intuitive three-dimensional displays. As a result, while there was a demand for improved learning quality and immersion, concrete solutions had not been presented.

[0140] 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.

[0141] In this invention, the server includes means for taking in and analyzing existing training information and converting specific information into a three-dimensional image; means for identifying an object to be visualized using a general-purpose data analysis device; means for generating the identified object as a three-dimensional display and reproducing its actions; and means for collecting the user's emotional data using an emotion analysis unit, adjusting the video display, and retransmitting it. This makes it possible to provide a highly customized learning experience that corresponds to the learner's emotional state and improve educational effectiveness.

[0142] "Existing training information" refers to data that has been accumulated and organized for commonly used educational and training purposes, and includes various formats such as text, images, audio, and video.

[0143] A "general-purpose data analysis device" refers to a computer program or hardware capable of processing and analyzing various types of data through self-learning or pattern recognition.

[0144] A "three-dimensional display" refers to a digital content that adds visual depth to a two-dimensional plane, thereby visually representing a three-dimensional space.

[0145] An "emotion analysis unit" refers to software and hardware that analyzes data such as video and audio to estimate the user's emotional state based on changes in their facial expressions and voice.

[0146] A "central control unit" refers to a server or computer system that integrates multiple system components and has control functions to manage the progress of the overall work and operate the system efficiently.

[0147] A "display device" is hardware used to provide visual information to a user, and includes monitors, head-mounted displays, and projectors.

[0148] This system aims to provide a more effective learning experience in the fields of education and training by utilizing three-dimensional displays. The server first retrieves existing training information from databases and analyzes it using general-purpose data analysis tools. This analysis process utilizes artificial intelligence technology to identify which information is suitable for visualization. The software used includes generative AI models such as TensorFlow.

[0149] Next, the server uses Unity or Unreal Engine to generate a 3D representation from the analysis results. This 3D representation is a real-time virtual content designed to make it easier for users to visually understand the information.

[0150] The generated three-dimensional display is transmitted to a display device via a network. This display device, or terminal, includes hardware such as a monitor or head-mounted display to enable three-dimensional display. Users can manipulate the three-dimensional display shown on the terminal, enabling two-way interaction through the interface.

[0151] The device is equipped with an emotion analysis unit that analyzes the user's facial expressions and voice data in real time to estimate their emotional state. The emotion data obtained from this analysis is sent back to the server, which evaluates the user's level of concentration and comprehension. Based on this, the video content is dynamically adjusted and learning content optimized for the user is resent.

[0152] As a concrete example, during history learning, users visually experience historical events through three-dimensional displays. For instance, if a user shows interest in a particular historical scene, an emotion analysis unit detects this interest, and the server adds and presents relevant detailed information. This functionality allows users to gain a deeper understanding of history.

[0153] An example of a prompt is, "Please suggest a method for presenting the social structure of the Edo period as a three-dimensional image in user-generated history education content." Using such prompts helps the generative AI model generate specific and effective content.

[0154] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0155] Step 1:

[0156] The server retrieves existing training information from databases and external sources. Inputs include multimedia data such as text files, images, and videos. The server analyzes this data using AI technology to determine which information should be visualized as a three-dimensional representation. Specifically, it applies natural language processing and image recognition algorithms to extract key items to be understood. The output is a list of data to be visualized.

[0157] Step 2:

[0158] The server generates a 3D representation using Unity or Unreal Engine based on the data identified in Step 1. The input here is the data to be visualized and its associated resource files. The server utilizes 3D modeling and shader programming techniques to create a real-time, interactive 3D image. During this process, the spatial layout and object interactions are configured. The output is visualized 3D image data.

[0159] Step 3:

[0160] The server transmits the generated 3D display to the terminal via the network. Input includes encoded video data and distribution protocol information. The server uses data compression technology to efficiently transfer the data and enable real-time visualization. Output is streaming data viewable on the terminal.

[0161] Step 4:

[0162] The user visually experiences a three-dimensional display received through the device. The input here is streaming data received from a server. The device displays the three-dimensional image on its screen, enabling user interaction. Users explore the content at their own pace using touchscreens or controllers. The output includes user interaction information and emotional data.

[0163] Step 5:

[0164] The device uses an emotion analysis unit to collect and analyze the user's facial expressions and voice information in real time. Inputs include sensor data acquired from the camera and microphone. The device uses an emotion estimation algorithm to evaluate the user's level of interest and concentration. The output is the generated emotion analysis results, which are sent to the server.

[0165] Step 6:

[0166] The server dynamically adjusts video content to improve the user's learning experience based on the sentiment data it receives. Input includes sentiment data and user interaction information. The server applies machine learning models to reconstruct content that meets user needs and redistributes the adjusted video. The output is adaptively adjusted visualization content.

[0167] (Application Example 2)

[0168] 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 device 14 will be referred to as the "terminal."

[0169] Traditional technologies for improving the customer experience in physical stores have struggled to grasp customer emotions and interests in real time and provide products and services that respond accordingly. As a result, it has been difficult to provide information and promotions optimized for each individual customer, and the improvement of the customer experience has not been fully achieved.

[0170] 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.

[0171] In this invention, the server includes means for acquiring existing information, means for analyzing the acquired information using artificial intelligence to identify a target for three-dimensional visualization, and means for acquiring user facial expression and voice data and analyzing emotions. This enables dynamic adjustment of three-dimensional video content and personalized information provision in response to customer emotions in physical stores.

[0172] "Existing information" refers to all types of data related to education, training, and commercial activities, and in particular includes content that should be subject to 3D visualization.

[0173] "Artificial intelligence" refers to the technology that enables computer systems to perform processing that mimics human intelligence, and in this context, it is used for information analysis and target identification.

[0174] "Three-dimensional video" refers to video content that is rendered in three dimensions, enabling a more immersive information experience for the viewer.

[0175] "Communication equipment" refers to devices used to send and receive images and information, and primarily includes terminals that enable user operation.

[0176] "Emotional analysis" is the process of inferring a person's emotional state from data obtained from their facial expressions and voice.

[0177] "Dynamic adjustment" means changing the displayed content and interface in real time based on pre-set conditions.

[0178] In an embodiment of this invention, the server is configured as a system that takes in existing information, analyzes it using artificial intelligence functions, and identifies objects that should be converted into three-dimensional images. Based on the identified information, the server generates a three-dimensional image and transmits this image data to a communication device.

[0179] The communication device displays the received three-dimensional image, allowing the user to operate it through an interface. This utilizes visual output devices such as smart glasses. The communication device is equipped with sensors to capture the user's facial expressions and voice, providing a means to analyze emotions in real time.

[0180] The results of the user's sentiment analysis are sent to the server, which dynamically adjusts the content of the displayed 3D image based on this analysis. This provides personalized content that responds to the user's state and reactions.

[0181] A concrete example of this application is a scenario where, in a physical store, a customer wearing smart glasses shows interest or surprise at a product within the store, and then detailed information about that product and related promotions are instantly presented as a 3D image. The emotion analysis used in this scenario could utilize technologies such as OpenCV or Dlib.

[0182] An example of a prompt using a generative AI model is: "Please describe the details of a system that analyzes customer emotions in real time through smart glasses in a physical store and provides personalized promotions."

[0183] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0184] Step 1:

[0185] The server retrieves existing information from the database. The input consists of text and image data related to educational and commercial activities. Based on this data, it performs preparatory processing to determine which information should be used as a 3D image.

[0186] Step 2:

[0187] The server analyzes the acquired information using an artificial intelligence algorithm. The input is the data prepared in step 1, and the output is a list of specific target information that can be visualized in three dimensions. This analysis is performed using a machine learning model, which extracts the necessary information based on the content of the data.

[0188] Step 3:

[0189] The server generates a three-dimensional image based on the identified target information. The input is the result list from step 2, and the output is the generated three-dimensional image data. The generated image is designed to closely resemble physical reality using computer graphics technology.

[0190] Step 4:

[0191] The server transmits the generated 3D video data to the communication device. The input is the 3D video data generated in step 3, and the output is the data transferred to the user's communication device. The data is transmitted efficiently using network protocols.

[0192] Step 5:

[0193] The terminal displays the received three-dimensional image. The input is the three-dimensional image data from step 4, and the user can manipulate this image through the interface. Display is performed using smart glasses or augmented reality devices.

[0194] Step 6:

[0195] The device uses sensors to acquire data on the user's facial expressions and voice, and then analyzes their emotions based on that data. The input is biometric data from the user, and the output is analyzed emotional data. The data is processed in real time, and tools such as OpenCV and Dlib are used for analysis.

[0196] Step 7:

[0197] The server dynamically adjusts the content of the 3D video based on the emotion analysis results and sends it back to the communication device. The input is the emotion data from step 6, and the output is the new, adjusted 3D video data. This process provides personalized video that is tailored to the user's state.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] [Second Embodiment]

[0202] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0203] 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.

[0204] 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).

[0205] 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.

[0206] 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.

[0207] 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).

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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".

[0214] One embodiment of the present invention is a system that uses AI to convert existing educational or training information into three-dimensional images, thereby providing viewers with an effective learning experience.

[0215] This system's program incorporates diverse information provided by educational institutions and companies, analyzes it, and determines what to visualize in 3D. AI technology forms the core of the system, extracting necessary data from the collected information and generating a three-dimensional model. The generated 3D image data is delivered to the user's terminal via the network. The user can manipulate this 3D image from various angles using their terminal, allowing students and trainees to deepen their understanding through interactive manipulation.

[0216] For example, in welding technique training, the server captures video footage demonstrating welding procedures, and AI analyzes key actions related to those procedures. Next, the server converts this into a three-dimensional model and generates video footage that recreates specific hand movements and welding rod manipulation. The generated video is sent to the user's terminal, enabling the user to gain a visual and three-dimensional understanding of actual welding techniques.

[0217] This system aims to improve the efficiency of information transmission in education and training, particularly enhancing the learning effectiveness of complex physical movements. In the transmission of craftsmanship and procedures, information that is difficult to convey with existing two-dimensional materials can be learned more intuitively through three-dimensional visualization. The use of this system is expected to simultaneously increase learners' understanding and interest in educational settings and corporate training facilities.

[0218] The following describes the processing flow.

[0219] Step 1:

[0220] The server retrieves information related to education and training from the database and imports it into the system. During the import process, it checks the file format and classifies each data item into categories such as text, images, audio, and video.

[0221] Step 2:

[0222] The server analyzes the collected information using AI. This analysis process involves extracting key points from text information using natural language processing and recognizing important movements from images and videos using image recognition technology. Based on the analysis results, it identifies objects that should be converted into 3D images.

[0223] Step 3:

[0224] The server generates three-dimensional video data based on the object identified by the AI. It creates a three-dimensional model from image and video data, and uses motion capture technology to reproduce actual movements, performing realistic simulations.

[0225] Step 4:

[0226] The server compresses the generated 3D video data to efficiently distribute it. The compressed data is then configured to be sent to the user's terminal via the network.

[0227] Step 5:

[0228] Users receive 3D video transmitted from a server using their terminals and view it through a dedicated display application. The application provides an interface that allows users to observe the 3D video from various angles and manipulate the video from any viewpoint.

[0229] Step 6:

[0230] The terminal records user activity logs and sends the collected information to the server as feedback. Based on this feedback data, the server analyzes the user's learning effectiveness and provides the user with necessary improvements or additional resources.

[0231] (Example 1)

[0232] 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."

[0233] In education and training, it is difficult to convey complex technologies and procedures to learners in an easily understandable way. Furthermore, two-dimensional materials and videos make it difficult to fully understand the details of physical actions, limiting learning effectiveness. Additionally, there is insufficient evaluation of learning effectiveness using learners' operation history.

[0234] 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.

[0235] In this invention, the server includes means for collecting and importing information from educational institutions and companies, means for analyzing the imported information using machine learning algorithms and visual information analysis techniques to extract important features, and means for generating a three-dimensional model using computer graphics software based on the extracted features. This enables learners to effectively learn techniques and procedures through interactive and visual three-dimensional images, and also enables the evaluation of learning effectiveness using operation history.

[0236] "Educational institutions and companies" refers to schools that conduct educational activities and companies that provide training.

[0237] "Collecting information" means the act of selecting and acquiring necessary data.

[0238] A "machine learning algorithm" refers to a computational method that analyzes data and extracts patterns and features.

[0239] "Visual information analysis technology" refers to techniques for extracting useful information from images and videos.

[0240] "Key features" refer to data characteristics that should be given particular attention for a specific purpose.

[0241] "Computer graphics software" refers to programs used to generate and display three-dimensional models.

[0242] A "three-dimensional model" is a digital representation that can be displayed in three dimensions, and its purpose is to visually reproduce actual physical objects and actions.

[0243] "Optimization" refers to the act of adjusting data and processing methods to operate efficiently.

[0244] "User terminal" refers to electronic devices such as computers and tablets used by learners.

[0245] An "interactive learning experience" refers to a learning method in which users can understand the content while directly interacting with it.

[0246] "Operation history" refers to data that records a series of operations performed by a user, and is used to understand the learning progress.

[0247] This invention is a system for providing effective learning experiences using educational information in educational institutions and training facilities. The technologies and processes used in implementing this system are described below.

[0248] The server collects diverse information provided by educational institutions and companies and stores it in a database. This information includes text, images, and videos. The server then utilizes machine learning algorithms and visual information analysis techniques to analyze the collected information. Specifically, it uses natural language processing (NLP) techniques to analyze text information and computer vision techniques to analyze visual information. During this analysis process, important features relevant to the learning objectives are extracted.

[0249] Based on the extracted features, the server uses computer graphics software (e.g., Blender or Unity) to generate a 3D model. The generated 3D model is then optimized to run efficiently on the user's device. This optimization utilizes rendering and data compression techniques.

[0250] The optimized 3D model is delivered to the user's device via the network. The user can view and interactively manipulate the received 3D model on their device. This allows users to deepen their understanding of educational information through a visually engaging experience.

[0251] To give a specific example, in welding technique training, the server captures video footage demonstrating the welding procedure, uses AI to analyze key movements, generates a three-dimensional model, and reproduces hand movements and welding rod manipulation. Users can visually confirm this on their devices and learn while experiencing realistic welding techniques.

[0252] An example of a prompt might be, "Convert the welding procedure into a 3D video. Emphasize important hand movements and tool handling, and visualize it clearly for educational purposes." The system would then take the appropriate action and create the visualization.

[0253] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0254] Step 1:

[0255] The server collects diverse information provided by educational institutions and companies and stores it in a database. Specifically, the server retrieves text, images, videos, etc., from data sources accessible via the network and stores them in the database according to their data format. This process takes educational materials and training videos as input and produces an integrated dataset as output.

[0256] Step 2:

[0257] The server executes machine learning algorithms to analyze the collected information. This process uses natural language processing techniques to analyze text information and extract key terms and procedures. It also uses computer vision techniques to analyze image and video information and extract relevant visual elements. The input here is the dataset obtained in step 1, and the output is the data with key features extracted.

[0258] Step 3:

[0259] The server uses computer graphics software to generate a three-dimensional model based on the extracted features. At this stage, the features extracted in step 2 are provided as input, and the software is used to construct a three-dimensional model. The output is a three-dimensional model suitable for educational purposes. This model is intended to visualize actual physical actions and procedures.

[0260] Step 4:

[0261] The server optimizes the generated 3D model and prepares it for distribution to the user's terminal. Optimization uses data compression techniques to reduce communication bandwidth and device load. The input is the model generated in step 3, and the output is the transferable optimized model.

[0262] Step 5:

[0263] The terminal receives a three-dimensional model distributed from the server and provides an interface to the learner. Through this interface, the user can observe and manipulate the three-dimensional model. The specific input is the model data from the server, and the output is a visual experience through the interface. Through this interaction, the user can gain a deeper understanding of the educational content.

[0264] Step 6:

[0265] Users interact with a three-dimensional model on their device to deepen their understanding. During this process, users can rotate and zoom the model. The input is the three-dimensional model displayed on the device, and the output is the user's own understanding and learning experience.

[0266] (Application Example 1)

[0267] 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."

[0268] Current education and training systems have a two-dimensional nature to information transmission, limiting learners' understanding and skill development. Furthermore, learning standard procedures for machine control units in factories is difficult, and improved assembly accuracy is particularly required for newly introduced machine control units.

[0269] 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.

[0270] In this invention, the server includes means for acquiring existing educational or training information, means for analyzing the acquired information using AI to identify objects to be visualized in three dimensions, and means for using the three-dimensional images for knowledge learning in machine control units to improve assembly accuracy. This enables the provision of an interactive three-dimensional learning experience and improves assembly accuracy in factories.

[0271] "Means of incorporating existing educational or training information" refers to technologies for acquiring educational information and training materials provided by educational institutions and companies in digital format.

[0272] "A method for identifying objects to be analyzed and converted into 3D images using AI" refers to a process that utilizes artificial intelligence technology to analyze the collected information and determine which content should be converted into 3D images.

[0273] "Means for generating three-dimensional images and reproducing actions" refers to a function that creates a three-dimensional image model based on identified information and visually reproduces the associated actions.

[0274] "Means for transmitting generated three-dimensional images to a terminal" refers to technology that delivers the created three-dimensional image data to an appropriate receiving device via a network.

[0275] "Means for displaying and enabling manipulation of three-dimensional images on a terminal" refers to an interface function that displays the received three-dimensional image on the terminal's display, allowing the viewer to freely manipulate the image.

[0276] "A means of improving assembly accuracy by utilizing three-dimensional images for knowledge learning in machine control units" refers to a technology that uses three-dimensional image data to enable machine control units to effectively learn and improve accuracy and efficiency in actual work.

[0277] The system for realizing this invention includes a process for converting educational and training information into 3D images. Specifically, a server digitally acquires information provided by educational institutions and companies. Next, the server uses AI software (e.g., TensorFlow or PyTorch) to analyze the acquired information and identify which content should be converted into 3D images. In this process, important actions and procedures are extracted. Then, a 3D modeling tool (e.g., Unity or Blender) is used to generate a 3D model based on the identified information.

[0278] The generated three-dimensional video data is transmitted to the user's terminal via the network. The terminal displays the received video on its screen and allows the user to manipulate the video. This manipulation includes changing the viewpoint and zooming in and out. This allows the user to understand the educational content visually and intuitively. Furthermore, in factory machine control units, three-dimensional video can be used for learning and to improve assembly accuracy.

[0279] As a specific example, this system is used when a novice learns the work procedures of a veteran in a factory. Through the generated three-dimensional video, the novice can intuitively understand the procedures and tips. An example of a prompt sentence is: "Please analyze the specific procedures and operations for converting the assembly procedures of automotive parts into a three-dimensional video and teaching them to novice robot A." In this way, it is expected to maximize the effectiveness of education and training.

[0280] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0281] Step 1:

[0282] The server imports educational information and training materials provided by educational institutions or companies in digital form. The input is digital data such as PDF or video files, and this is stored in an internal database. The output is the stored educational information in digital form.

[0283] Step 2:

[0284] The server analyzes the digital information imported using AI software and identifies the target for three-dimensional visualization. The input is the digital information imported in Step 1, and important actions and procedures are extracted from this information using a specific algorithm. The output is a set of information that is the target for three-dimensional conversion.

[0285] Step 3:

[0286] The server uses a three-dimensional modeling tool to convert the identified information into a three-dimensional model. The input is the information extracted in Step 2, and a model generation algorithm is applied to create a stereoscopic video. The output is the completed three-dimensional model data. Specific operations include the arrangement of three-dimensional shapes and color information.

[0287] Step 4:

[0288] The server transmits the generated 3D model data to the user's terminal via the network. The input is the 3D model data obtained in step 3, which is compressed into an appropriate format before transmission. The output is the 3D model data displayed on the user's terminal.

[0289] Step 5:

[0290] The terminal displays the received 3D image on its screen, allowing the user to freely manipulate it. The input is the 3D data received in step 4, and interactive features such as viewpoint change and zoom in / zoom out are provided to give the user an interactive experience. The output is the screen display of the 3D image that the user is manipulating.

[0291] Step 6:

[0292] The system records the user's operation history on the terminal, and the server uses this data to evaluate the user's learning effectiveness. The input is user operation history data, and data analysis techniques are used to quantify learning progress and comprehension. The output is an evaluation result showing the learning effectiveness.

[0293] 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.

[0294] One embodiment of the present invention is a system that uses AI to convert existing educational or training information into three-dimensional images and combines this with a user's emotion engine to further enhance the learning experience. This system analyzes the acquired information, generates specific data as three-dimensional images, and in addition recognizes the user's emotions, dynamically adjusting the images and presentation content based on the user's reactions.

[0295] The program's processing involves the server first ingesting education and training-related data and analyzing it using AI. This analysis determines which information should be visualized as a hologram. Next, the server generates a three-dimensional image based on the identified information and sends it to the user's terminal.

[0296] Users can receive this 3D video using their devices and interact with it interactively through the interface. Furthermore, the devices are equipped with an emotion engine that uses sensors such as cameras and microphones to analyze the user's facial expressions and voice information in real time and send the results to the server. Based on this emotion data, the server evaluates the user's level of understanding and concentration, and customizes and redistributes the video and presented content. This process allows users to learn at their own pace, improving the effectiveness of the education.

[0297] A concrete example is in history education, where users experience historical events and culture through 3D video, and an emotion engine detects the user's interest and surprise. The next content presented then becomes more detailed or new perspectives are added in response to that reaction. This feature allows users to engage in more immersive learning, improving the quality of their learning.

[0298] This system aims to provide a deep and effective learning process tailored to individual learners, rather than simply transmitting knowledge in educational and training settings.

[0299] The following describes the processing flow.

[0300] Step 1:

[0301] The server retrieves information related to education and training from the database and imports it into the system. The imported information is classified into text, images, audio, video, etc., and prepared for analysis.

[0302] Step 2:

[0303] The server analyzes the captured information using AI. In the analysis, the key points of the text information are extracted by natural language processing, and important actions are detected from images and videos by making full use of image recognition. Based on the analysis results, it is determined which content should be expressed as a three-dimensional video.

[0304] Step 3:

[0305] The server generates three-dimensional video data based on the identified content. In the generation process, the image data is converted into a three-dimensional model, and a real video is created by adding motion simulation. This three-dimensional video is prepared so that the user can access it later. <>

[0306] Step 4:

[0307] The server compresses the generated three-dimensional video and prepares it for distribution to the terminal. The data is optimized and transmitted for efficient distribution via the network.

[0308] Step 5:

[0309] The user receives the three-dimensional video using the terminal and displays it via a dedicated application. The application provides an interface that allows the video to be viewed from multiple angles, enabling the user to freely change the viewing perspective.

[0310] Step 6:

[0311] The terminal uses the camera and microphone to monitor the user's expression and voice in real time and analyzes them through an emotion engine. As soon as the user's emotional state is identified, the analysis results are sent to the server.

[0312] Step 7:

[0313] The server evaluates the user's learning progress and level of interest based on data obtained from the emotion engine. It dynamically adjusts 3D images and presentation content as needed, generating and redistributing optimal learning content to the user. This process allows users to continue learning while maintaining their interest.

[0314] (Example 2)

[0315] 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".

[0316] In traditional education and training settings, standardized information delivery was prevalent, making it difficult to customize content to suit the individual learner's level of understanding and interests. In particular, there was a lack of means to dynamically adjust content based on learners' emotional states using visually intuitive three-dimensional displays. As a result, while there was a demand for improved learning quality and immersion, concrete solutions had not been presented.

[0317] 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.

[0318] In this invention, the server includes means for taking in and analyzing existing training information and converting specific information into a three-dimensional image; means for identifying an object to be visualized using a general-purpose data analysis device; means for generating the identified object as a three-dimensional display and reproducing its actions; and means for collecting the user's emotional data using an emotion analysis unit, adjusting the video display, and retransmitting it. This makes it possible to provide a highly customized learning experience that corresponds to the learner's emotional state and improve educational effectiveness.

[0319] "Existing training information" refers to data that has been accumulated and organized for commonly used educational and training purposes, and includes various formats such as text, images, audio, and video.

[0320] A "general-purpose data analysis device" refers to a computer program or hardware capable of processing and analyzing various types of data through self-learning or pattern recognition.

[0321] A "three-dimensional display" refers to a digital content that adds visual depth to a two-dimensional plane, thereby visually representing a three-dimensional space.

[0322] An "emotion analysis unit" refers to software and hardware that analyzes data such as video and audio to estimate the user's emotional state based on changes in their facial expressions and voice.

[0323] A "central control unit" refers to a server or computer system that integrates multiple system components and has control functions to manage the progress of the overall work and operate the system efficiently.

[0324] A "display device" is hardware used to provide visual information to a user, and includes monitors, head-mounted displays, and projectors.

[0325] This system aims to provide a more effective learning experience in the fields of education and training by utilizing three-dimensional displays. The server first retrieves existing training information from databases and analyzes it using general-purpose data analysis tools. This analysis process utilizes artificial intelligence technology to identify which information is suitable for visualization. The software used includes generative AI models such as TensorFlow.

[0326] Next, the server uses Unity or Unreal Engine to generate a 3D representation from the analysis results. This 3D representation is a real-time virtual content designed to make it easier for users to visually understand the information.

[0327] The generated three-dimensional display is transmitted to a display device via a network. This display device, or terminal, includes hardware such as a monitor or head-mounted display to enable three-dimensional display. Users can manipulate the three-dimensional display shown on the terminal, enabling two-way interaction through the interface.

[0328] The device is equipped with an emotion analysis unit that analyzes the user's facial expressions and voice data in real time to estimate their emotional state. The emotion data obtained from this analysis is sent back to the server, which evaluates the user's level of concentration and comprehension. Based on this, the video content is dynamically adjusted and learning content optimized for the user is resent.

[0329] As a concrete example, during history learning, users visually experience historical events through three-dimensional displays. For instance, if a user shows interest in a particular historical scene, an emotion analysis unit detects this interest, and the server adds and presents relevant detailed information. This functionality allows users to gain a deeper understanding of history.

[0330] An example of a prompt is, "Please suggest a method for presenting the social structure of the Edo period as a three-dimensional image in user-generated history education content." Using such prompts helps the generative AI model generate specific and effective content.

[0331] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0332] Step 1:

[0333] The server retrieves existing training information from databases and external sources. Inputs include multimedia data such as text files, images, and videos. The server analyzes this data using AI technology to determine which information should be visualized as a three-dimensional representation. Specifically, it applies natural language processing and image recognition algorithms to extract key items to be understood. The output is a list of data to be visualized.

[0334] Step 2:

[0335] The server generates a 3D representation using Unity or Unreal Engine based on the data identified in Step 1. The input here is the data to be visualized and its associated resource files. The server utilizes 3D modeling and shader programming techniques to create a real-time, interactive 3D image. During this process, the spatial layout and object interactions are configured. The output is visualized 3D image data.

[0336] Step 3:

[0337] The server transmits the generated 3D display to the terminal via the network. Input includes encoded video data and distribution protocol information. The server uses data compression technology to efficiently transfer the data and enable real-time visualization. Output is streaming data viewable on the terminal.

[0338] Step 4:

[0339] The user visually experiences a three-dimensional display received through the device. The input here is streaming data received from a server. The device displays the three-dimensional image on its screen, enabling user interaction. Users explore the content at their own pace using touchscreens or controllers. The output includes user interaction information and emotional data.

[0340] Step 5:

[0341] The device uses an emotion analysis unit to collect and analyze the user's facial expressions and voice information in real time. Inputs include sensor data acquired from the camera and microphone. The device uses an emotion estimation algorithm to evaluate the user's level of interest and concentration. The output is the generated emotion analysis results, which are sent to the server.

[0342] Step 6:

[0343] The server dynamically adjusts video content to improve the user's learning experience based on the sentiment data it receives. Input includes sentiment data and user interaction information. The server applies machine learning models to reconstruct content that meets user needs and redistributes the adjusted video. The output is adaptively adjusted visualization content.

[0344] (Application Example 2)

[0345] 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."

[0346] Traditional technologies for improving the customer experience in physical stores have struggled to grasp customer emotions and interests in real time and provide products and services that respond accordingly. As a result, it has been difficult to provide information and promotions optimized for each individual customer, and the improvement of the customer experience has not been fully achieved.

[0347] 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.

[0348] In this invention, the server includes means for acquiring existing information, means for analyzing the acquired information using artificial intelligence to identify a target for three-dimensional visualization, and means for acquiring user facial expression and voice data and analyzing emotions. This enables dynamic adjustment of three-dimensional video content and personalized information provision in response to customer emotions in physical stores.

[0349] "Existing information" refers to all types of data related to education, training, and commercial activities, and in particular includes content that should be subject to 3D visualization.

[0350] "Artificial intelligence" refers to the technology that enables computer systems to perform processing that mimics human intelligence, and in this context, it is used for information analysis and target identification.

[0351] "Three-dimensional video" refers to video content that is rendered in three dimensions, enabling a more immersive information experience for the viewer.

[0352] "Communication equipment" refers to devices used to send and receive images and information, and primarily includes terminals that enable user operation.

[0353] "Emotional analysis" is the process of inferring a person's emotional state from data obtained from their facial expressions and voice.

[0354] "Dynamic adjustment" means changing the displayed content and interface in real time based on pre-set conditions.

[0355] In an embodiment of this invention, the server is configured as a system that takes in existing information, analyzes it using artificial intelligence functions, and identifies objects that should be converted into three-dimensional images. Based on the identified information, the server generates a three-dimensional image and transmits this image data to a communication device.

[0356] The communication device displays the received three-dimensional image, allowing the user to operate it through an interface. This utilizes visual output devices such as smart glasses. The communication device is equipped with sensors to capture the user's facial expressions and voice, providing a means to analyze emotions in real time.

[0357] The results of the user's sentiment analysis are sent to the server, which dynamically adjusts the content of the displayed 3D image based on this analysis. This provides personalized content that responds to the user's state and reactions.

[0358] A concrete example of this application is a scenario where, in a physical store, a customer wearing smart glasses shows interest or surprise at a product within the store, and then detailed information about that product and related promotions are instantly presented as a 3D image. The emotion analysis used in this scenario could utilize technologies such as OpenCV or Dlib.

[0359] An example of a prompt using a generative AI model is: "Please describe the details of a system that analyzes customer emotions in real time through smart glasses in a physical store and provides personalized promotions."

[0360] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0361] Step 1:

[0362] The server retrieves existing information from the database. The input consists of text and image data related to educational and commercial activities. Based on this data, it performs preparatory processing to determine which information should be used as a 3D image.

[0363] Step 2:

[0364] The server analyzes the acquired information using an artificial intelligence algorithm. The input is the data prepared in step 1, and the output is a list of specific target information that can be visualized in three dimensions. This analysis is performed using a machine learning model, which extracts the necessary information based on the content of the data.

[0365] Step 3:

[0366] The server generates a three-dimensional image based on the identified target information. The input is the result list from step 2, and the output is the generated three-dimensional image data. The generated image is designed to closely resemble physical reality using computer graphics technology.

[0367] Step 4:

[0368] The server transmits the generated 3D video data to the communication device. The input is the 3D video data generated in step 3, and the output is the data transferred to the user's communication device. The data is transmitted efficiently using network protocols.

[0369] Step 5:

[0370] The terminal displays the received three-dimensional image. The input is the three-dimensional image data from step 4, and the user can manipulate this image through the interface. Display is performed using smart glasses or augmented reality devices.

[0371] Step 6:

[0372] The device uses sensors to acquire data on the user's facial expressions and voice, and then analyzes their emotions based on that data. The input is biometric data from the user, and the output is analyzed emotional data. The data is processed in real time, and tools such as OpenCV and Dlib are used for analysis.

[0373] Step 7:

[0374] The server dynamically adjusts the content of the 3D video based on the emotion analysis results and sends it back to the communication device. The input is the emotion data from step 6, and the output is the new, adjusted 3D video data. This process provides personalized video that is tailored to the user's state.

[0375] 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.

[0376] 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.

[0377] 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.

[0378] [Third Embodiment]

[0379] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0380] 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.

[0381] 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).

[0382] 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.

[0383] 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.

[0384] 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).

[0385] 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.

[0386] 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.

[0387] 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.

[0388] 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.

[0389] 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.

[0390] 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".

[0391] One embodiment of the present invention is a system that uses AI to convert existing educational or training information into three-dimensional images, thereby providing viewers with an effective learning experience.

[0392] This system's program incorporates diverse information provided by educational institutions and companies, analyzes it, and determines what to visualize in 3D. AI technology forms the core of the system, extracting necessary data from the collected information and generating a three-dimensional model. The generated 3D image data is delivered to the user's terminal via the network. The user can manipulate this 3D image from various angles using their terminal, allowing students and trainees to deepen their understanding through interactive manipulation.

[0393] For example, in welding technique training, the server captures video footage demonstrating welding procedures, and AI analyzes key actions related to those procedures. Next, the server converts this into a three-dimensional model and generates video footage that recreates specific hand movements and welding rod manipulation. The generated video is sent to the user's terminal, enabling the user to gain a visual and three-dimensional understanding of actual welding techniques.

[0394] This system aims to improve the efficiency of information transmission in education and training, particularly enhancing the learning effectiveness of complex physical movements. In the transmission of craftsmanship and procedures, information that is difficult to convey with existing two-dimensional materials can be learned more intuitively through three-dimensional visualization. The use of this system is expected to simultaneously increase learners' understanding and interest in educational settings and corporate training facilities.

[0395] The following describes the processing flow.

[0396] Step 1:

[0397] The server retrieves information related to education and training from the database and imports it into the system. During the import process, it checks the file format and classifies each data item into categories such as text, images, audio, and video.

[0398] Step 2:

[0399] The server analyzes the collected information using AI. This analysis process involves extracting key points from text information using natural language processing and recognizing important movements from images and videos using image recognition technology. Based on the analysis results, it identifies objects that should be converted into 3D images.

[0400] Step 3:

[0401] The server generates three-dimensional video data based on the object identified by the AI. It creates a three-dimensional model from image and video data, and uses motion capture technology to reproduce actual movements, performing realistic simulations.

[0402] Step 4:

[0403] The server compresses the generated 3D video data to efficiently distribute it. The compressed data is then configured to be sent to the user's terminal via the network.

[0404] Step 5:

[0405] Users receive 3D video transmitted from a server using their terminals and view it through a dedicated display application. The application provides an interface that allows users to observe the 3D video from various angles and manipulate the video from any viewpoint.

[0406] Step 6:

[0407] The terminal records user activity logs and sends the collected information to the server as feedback. Based on this feedback data, the server analyzes the user's learning effectiveness and provides the user with necessary improvements or additional resources.

[0408] (Example 1)

[0409] 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."

[0410] In education and training, it is difficult to convey complex technologies and procedures to learners in an easily understandable way. Furthermore, two-dimensional materials and videos make it difficult to fully understand the details of physical actions, limiting learning effectiveness. Additionally, there is insufficient evaluation of learning effectiveness using learners' operation history.

[0411] 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.

[0412] In this invention, the server includes means for collecting and importing information from educational institutions and companies, means for analyzing the imported information using machine learning algorithms and visual information analysis techniques to extract important features, and means for generating a three-dimensional model using computer graphics software based on the extracted features. This enables learners to effectively learn techniques and procedures through interactive and visual three-dimensional images, and also enables the evaluation of learning effectiveness using operation history.

[0413] "Educational institutions and companies" refers to schools that conduct educational activities and companies that provide training.

[0414] "Collecting information" means the act of selecting and acquiring necessary data.

[0415] A "machine learning algorithm" refers to a computational method that analyzes data and extracts patterns and features.

[0416] "Visual information analysis technology" refers to techniques for extracting useful information from images and videos.

[0417] "Key features" refer to data characteristics that should be given particular attention for a specific purpose.

[0418] "Computer graphics software" refers to programs used to generate and display three-dimensional models.

[0419] A "three-dimensional model" is a digital representation that can be displayed in three dimensions, and its purpose is to visually reproduce actual physical objects and actions.

[0420] "Optimization" refers to the act of adjusting data and processing methods to operate efficiently.

[0421] "User terminal" refers to electronic devices such as computers and tablets used by learners.

[0422] An "interactive learning experience" refers to a learning method in which users can understand the content while directly interacting with it.

[0423] "Operation history" refers to data that records a series of operations performed by a user, and is used to understand the learning progress.

[0424] This invention is a system for providing effective learning experiences using educational information in educational institutions and training facilities. The technologies and processes used in implementing this system are described below.

[0425] The server collects diverse information provided by educational institutions and companies and stores it in a database. This information includes text, images, and videos. The server then utilizes machine learning algorithms and visual information analysis techniques to analyze the collected information. Specifically, it uses natural language processing (NLP) techniques to analyze text information and computer vision techniques to analyze visual information. During this analysis process, important features relevant to the learning objectives are extracted.

[0426] Based on the extracted features, the server uses computer graphics software (e.g., Blender or Unity) to generate a 3D model. The generated 3D model is then optimized to run efficiently on the user's device. This optimization utilizes rendering and data compression techniques.

[0427] The optimized 3D model is delivered to the user's device via the network. The user can view and interactively manipulate the received 3D model on their device. This allows users to deepen their understanding of educational information through a visually engaging experience.

[0428] To give a specific example, in welding technique training, the server captures video footage demonstrating the welding procedure, uses AI to analyze key movements, generates a three-dimensional model, and reproduces hand movements and welding rod manipulation. Users can visually confirm this on their devices and learn while experiencing realistic welding techniques.

[0429] An example of a prompt might be, "Convert the welding procedure into a 3D video. Emphasize important hand movements and tool handling, and visualize it clearly for educational purposes." The system would then take the appropriate action and create the visualization.

[0430] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0431] Step 1:

[0432] The server collects diverse information provided by educational institutions and companies and stores it in a database. Specifically, the server retrieves text, images, videos, etc., from data sources accessible via the network and stores them in the database according to their data format. This process takes educational materials and training videos as input and produces an integrated dataset as output.

[0433] Step 2:

[0434] The server executes machine learning algorithms to analyze the collected information. This process uses natural language processing techniques to analyze text information and extract key terms and procedures. It also uses computer vision techniques to analyze image and video information and extract relevant visual elements. The input here is the dataset obtained in step 1, and the output is the data with key features extracted.

[0435] Step 3:

[0436] The server uses computer graphics software to generate a three-dimensional model based on the extracted features. At this stage, the features extracted in step 2 are provided as input, and the software is used to construct a three-dimensional model. The output is a three-dimensional model suitable for educational purposes. This model is intended to visualize actual physical actions and procedures.

[0437] Step 4:

[0438] The server optimizes the generated 3D model and prepares it for distribution to the user's terminal. Optimization uses data compression techniques to reduce communication bandwidth and device load. The input is the model generated in step 3, and the output is the transferable optimized model.

[0439] Step 5:

[0440] The terminal receives a three-dimensional model distributed from the server and provides an interface to the learner. Through this interface, the user can observe and manipulate the three-dimensional model. The specific input is the model data from the server, and the output is a visual experience through the interface. Through this interaction, the user can gain a deeper understanding of the educational content.

[0441] Step 6:

[0442] Users interact with a three-dimensional model on their device to deepen their understanding. During this process, users can rotate and zoom the model. The input is the three-dimensional model displayed on the device, and the output is the user's own understanding and learning experience.

[0443] (Application Example 1)

[0444] 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."

[0445] Current education and training systems have a two-dimensional nature to information transmission, limiting learners' understanding and skill development. Furthermore, learning standard procedures for machine control units in factories is difficult, and improved assembly accuracy is particularly required for newly introduced machine control units.

[0446] 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.

[0447] In this invention, the server includes means for acquiring existing educational or training information, means for analyzing the acquired information using AI to identify objects to be visualized in three dimensions, and means for using the three-dimensional images for knowledge learning in machine control units to improve assembly accuracy. This enables the provision of an interactive three-dimensional learning experience and improves assembly accuracy in factories.

[0448] "Means of incorporating existing educational or training information" refers to technologies for acquiring educational information and training materials provided by educational institutions and companies in digital format.

[0449] "A method for identifying objects to be analyzed and converted into 3D images using AI" refers to a process that utilizes artificial intelligence technology to analyze the collected information and determine which content should be converted into 3D images.

[0450] "Means for generating three-dimensional images and reproducing actions" refers to a function that creates a three-dimensional image model based on identified information and visually reproduces the associated actions.

[0451] "Means for transmitting generated three-dimensional images to a terminal" refers to technology that delivers the created three-dimensional image data to an appropriate receiving device via a network.

[0452] "Means for displaying and enabling manipulation of three-dimensional images on a terminal" refers to an interface function that displays the received three-dimensional image on the terminal's display, allowing the viewer to freely manipulate the image.

[0453] "A means of improving assembly accuracy by utilizing three-dimensional images for knowledge learning in machine control units" refers to a technology that uses three-dimensional image data to enable machine control units to effectively learn and improve accuracy and efficiency in actual work.

[0454] The system for realizing this invention includes a process for converting educational and training information into 3D images. Specifically, a server digitally acquires information provided by educational institutions and companies. Next, the server uses AI software (e.g., TensorFlow or PyTorch) to analyze the acquired information and identify which content should be converted into 3D images. In this process, important actions and procedures are extracted. Then, a 3D modeling tool (e.g., Unity or Blender) is used to generate a 3D model based on the identified information.

[0455] The generated three-dimensional video data is transmitted to the user's terminal via the network. The terminal displays the received video on its screen and allows the user to manipulate the video. This manipulation includes changing the viewpoint and zooming in and out. This allows the user to understand the educational content visually and intuitively. Furthermore, in factory machine control units, three-dimensional video can be used for learning and to improve assembly accuracy.

[0456] As a concrete example, this system is used in factories when new employees learn the work procedures of experienced workers. Through the generated three-dimensional images, new employees can intuitively grasp the procedures and techniques. An example of a prompt message would be, "Convert the assembly procedure for automobile parts into a three-dimensional image and analyze the specific procedures and actions to teach to new robot A." In this way, it is expected that the effectiveness of education and training will be maximized.

[0457] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0458] Step 1:

[0459] The server digitally retrieves educational information and training materials provided by educational institutions and companies. Input is digital data such as PDFs and video files, which are stored in an internal database. Output is the stored digital educational information.

[0460] Step 2:

[0461] The server uses AI software to analyze the captured digital information and identify the objects to be visualized in 3D. The input is the digital information captured in step 1, and a specific algorithm is used to extract important actions and procedures from this information. The output is a set of information to be visualized in 3D.

[0462] Step 3:

[0463] The server uses a 3D modeling tool to convert the identified information into a 3D model. The input is the information extracted in step 2, and a model generation algorithm is applied to create a 3D image. The output is the completed 3D model data. Specifically, the 3D shape and color information are arranged.

[0464] Step 4:

[0465] The server transmits the generated 3D model data to the user's terminal via the network. The input is the 3D model data obtained in step 3, which is compressed into an appropriate format before transmission. The output is the 3D model data displayed on the user's terminal.

[0466] Step 5:

[0467] The terminal displays the received 3D image on its screen, allowing the user to freely manipulate it. The input is the 3D data received in step 4, and interactive features such as viewpoint change and zoom in / zoom out are provided to give the user an interactive experience. The output is the screen display of the 3D image that the user is manipulating.

[0468] Step 6:

[0469] The system records the user's operation history on the terminal, and the server uses this data to evaluate the user's learning effectiveness. The input is user operation history data, and data analysis techniques are used to quantify learning progress and comprehension. The output is an evaluation result showing the learning effectiveness.

[0470] 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.

[0471] One embodiment of the present invention is a system that uses AI to convert existing educational or training information into three-dimensional images and combines this with a user's emotion engine to further enhance the learning experience. This system analyzes the acquired information, generates specific data as three-dimensional images, and in addition recognizes the user's emotions, dynamically adjusting the images and presentation content based on the user's reactions.

[0472] The program's processing involves the server first ingesting education and training-related data and analyzing it using AI. This analysis determines which information should be visualized as a hologram. Next, the server generates a three-dimensional image based on the identified information and sends it to the user's terminal.

[0473] Users can receive this 3D video using their devices and interact with it interactively through the interface. Furthermore, the devices are equipped with an emotion engine that uses sensors such as cameras and microphones to analyze the user's facial expressions and voice information in real time and send the results to the server. Based on this emotion data, the server evaluates the user's level of understanding and concentration, and customizes and redistributes the video and presented content. This process allows users to learn at their own pace, improving the effectiveness of the education.

[0474] A concrete example is in history education, where users experience historical events and culture through 3D video, and an emotion engine detects the user's interest and surprise. The next content presented then becomes more detailed or new perspectives are added in response to that reaction. This feature allows users to engage in more immersive learning, improving the quality of their learning.

[0475] This system aims to provide a deep and effective learning process tailored to individual learners, rather than simply transmitting knowledge in educational and training settings.

[0476] The following describes the processing flow.

[0477] Step 1:

[0478] The server retrieves information related to education and training from the database and imports it into the system. The imported information is classified into text, images, audio, video, etc., and prepared for analysis.

[0479] Step 2:

[0480] The server analyzes the collected information using AI. The analysis extracts key points from text information using natural language processing and detects important actions from images and videos using image recognition. Based on these analysis results, it determines which content should be represented as a three-dimensional image.

[0481] Step 3:

[0482] The server generates three-dimensional video data based on the identified information. The generation process converts image data into a three-dimensional model and adds motion simulations to create realistic images. This three-dimensional video is then prepared for later access by the user.

[0483] Step 4:

[0484] The server compresses the generated 3D video and prepares it for distribution to the terminal. The data is optimized and transmitted for efficient distribution over the network.

[0485] Step 5:

[0486] The user receives three-dimensional video using a device and displays it through a dedicated application. The application provides an interface that allows the user to view the video from multiple angles and freely change their viewpoint.

[0487] Step 6:

[0488] The device uses its camera and microphone to monitor the user's facial expressions and voice in real time, and analyzes them through an emotion engine. Once the user's emotional state is identified, the analysis results are sent to the server.

[0489] Step 7:

[0490] The server evaluates the user's learning progress and level of interest based on data obtained from the emotion engine. It dynamically adjusts 3D images and presentation content as needed, generating and redistributing optimal learning content to the user. This process allows users to continue learning while maintaining their interest.

[0491] (Example 2)

[0492] 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."

[0493] In traditional education and training settings, standardized information delivery was prevalent, making it difficult to customize content to suit the individual learner's level of understanding and interests. In particular, there was a lack of means to dynamically adjust content based on learners' emotional states using visually intuitive three-dimensional displays. As a result, while there was a demand for improved learning quality and immersion, concrete solutions had not been presented.

[0494] 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.

[0495] In this invention, the server includes means for taking in and analyzing existing training information and converting specific information into a three-dimensional image; means for identifying an object to be visualized using a general-purpose data analysis device; means for generating the identified object as a three-dimensional display and reproducing its actions; and means for collecting the user's emotional data using an emotion analysis unit, adjusting the video display, and retransmitting it. This makes it possible to provide a highly customized learning experience that corresponds to the learner's emotional state and improve educational effectiveness.

[0496] "Existing training information" refers to data that has been accumulated and organized for commonly used educational and training purposes, and includes various formats such as text, images, audio, and video.

[0497] A "general-purpose data analysis device" refers to a computer program or hardware capable of processing and analyzing various types of data through self-learning or pattern recognition.

[0498] A "three-dimensional display" refers to a digital content that adds visual depth to a two-dimensional plane, thereby visually representing a three-dimensional space.

[0499] An "emotion analysis unit" refers to software and hardware that analyzes data such as video and audio to estimate the user's emotional state based on changes in their facial expressions and voice.

[0500] A "central control unit" refers to a server or computer system that integrates multiple system components and has control functions to manage the progress of the overall work and operate the system efficiently.

[0501] A "display device" is hardware used to provide visual information to a user, and includes monitors, head-mounted displays, and projectors.

[0502] This system aims to provide a more effective learning experience in the fields of education and training by utilizing three-dimensional displays. The server first retrieves existing training information from databases and analyzes it using general-purpose data analysis tools. This analysis process utilizes artificial intelligence technology to identify which information is suitable for visualization. The software used includes generative AI models such as TensorFlow.

[0503] Next, the server uses Unity or Unreal Engine to generate a 3D representation from the analysis results. This 3D representation is a real-time virtual content designed to make it easier for users to visually understand the information.

[0504] The generated three-dimensional display is transmitted to a display device via a network. This display device, or terminal, includes hardware such as a monitor or head-mounted display to enable three-dimensional display. Users can manipulate the three-dimensional display shown on the terminal, enabling two-way interaction through the interface.

[0505] The device is equipped with an emotion analysis unit that analyzes the user's facial expressions and voice data in real time to estimate their emotional state. The emotion data obtained from this analysis is sent back to the server, which evaluates the user's level of concentration and comprehension. Based on this, the video content is dynamically adjusted and learning content optimized for the user is resent.

[0506] As a concrete example, during history learning, users visually experience historical events through three-dimensional displays. For instance, if a user shows interest in a particular historical scene, an emotion analysis unit detects this interest, and the server adds and presents relevant detailed information. This functionality allows users to gain a deeper understanding of history.

[0507] An example of a prompt is, "Please suggest a method for presenting the social structure of the Edo period as a three-dimensional image in user-generated history education content." Using such prompts helps the generative AI model generate specific and effective content.

[0508] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0509] Step 1:

[0510] The server retrieves existing training information from databases and external sources. Inputs include multimedia data such as text files, images, and videos. The server analyzes this data using AI technology to determine which information should be visualized as a three-dimensional representation. Specifically, it applies natural language processing and image recognition algorithms to extract key items to be understood. The output is a list of data to be visualized.

[0511] Step 2:

[0512] The server generates a 3D representation using Unity or Unreal Engine based on the data identified in Step 1. The input here is the data to be visualized and its associated resource files. The server utilizes 3D modeling and shader programming techniques to create a real-time, interactive 3D image. During this process, the spatial layout and object interactions are configured. The output is visualized 3D image data.

[0513] Step 3:

[0514] The server transmits the generated 3D display to the terminal via the network. Input includes encoded video data and distribution protocol information. The server uses data compression technology to efficiently transfer the data and enable real-time visualization. Output is streaming data viewable on the terminal.

[0515] Step 4:

[0516] The user visually experiences a three-dimensional display received through the device. The input here is streaming data received from a server. The device displays the three-dimensional image on its screen, enabling user interaction. Users explore the content at their own pace using touchscreens or controllers. The output includes user interaction information and emotional data.

[0517] Step 5:

[0518] The device uses an emotion analysis unit to collect and analyze the user's facial expressions and voice information in real time. Inputs include sensor data acquired from the camera and microphone. The device uses an emotion estimation algorithm to evaluate the user's level of interest and concentration. The output is the generated emotion analysis results, which are sent to the server.

[0519] Step 6:

[0520] The server dynamically adjusts video content to improve the user's learning experience based on the sentiment data it receives. Input includes sentiment data and user interaction information. The server applies machine learning models to reconstruct content that meets user needs and redistributes the adjusted video. The output is adaptively adjusted visualization content.

[0521] (Application Example 2)

[0522] 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."

[0523] Traditional technologies for improving the customer experience in physical stores have struggled to grasp customer emotions and interests in real time and provide products and services that respond accordingly. As a result, it has been difficult to provide information and promotions optimized for each individual customer, and the improvement of the customer experience has not been fully achieved.

[0524] 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.

[0525] In this invention, the server includes means for acquiring existing information, means for analyzing the acquired information using artificial intelligence to identify a target for three-dimensional visualization, and means for acquiring user facial expression and voice data and analyzing emotions. This enables dynamic adjustment of three-dimensional video content and personalized information provision in response to customer emotions in physical stores.

[0526] "Existing information" refers to all types of data related to education, training, and commercial activities, and in particular includes content that should be subject to 3D visualization.

[0527] "Artificial intelligence" refers to the technology that enables computer systems to perform processing that mimics human intelligence, and in this context, it is used for information analysis and target identification.

[0528] "Three-dimensional video" refers to video content that is rendered in three dimensions, enabling a more immersive information experience for the viewer.

[0529] "Communication equipment" refers to devices used to send and receive images and information, and primarily includes terminals that enable user operation.

[0530] "Emotional analysis" is the process of inferring a person's emotional state from data obtained from their facial expressions and voice.

[0531] "Dynamic adjustment" means changing the displayed content and interface in real time based on pre-set conditions.

[0532] In an embodiment of this invention, the server is configured as a system that takes in existing information, analyzes it using artificial intelligence functions, and identifies objects that should be converted into three-dimensional images. Based on the identified information, the server generates a three-dimensional image and transmits this image data to a communication device.

[0533] The communication device displays the received three-dimensional image, allowing the user to operate it through an interface. This utilizes visual output devices such as smart glasses. The communication device is equipped with sensors to capture the user's facial expressions and voice, providing a means to analyze emotions in real time.

[0534] The results of the user's sentiment analysis are sent to the server, which dynamically adjusts the content of the displayed 3D image based on this analysis. This provides personalized content that responds to the user's state and reactions.

[0535] A concrete example of this application is a scenario where, in a physical store, a customer wearing smart glasses shows interest or surprise at a product within the store, and then detailed information about that product and related promotions are instantly presented as a 3D image. The emotion analysis used in this scenario could utilize technologies such as OpenCV or Dlib.

[0536] An example of a prompt using a generative AI model is: "Please describe the details of a system that analyzes customer emotions in real time through smart glasses in a physical store and provides personalized promotions."

[0537] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0538] Step 1:

[0539] The server retrieves existing information from the database. The input consists of text and image data related to educational and commercial activities. Based on this data, it performs preparatory processing to determine which information should be used as a 3D image.

[0540] Step 2:

[0541] The server analyzes the acquired information using an artificial intelligence algorithm. The input is the data prepared in step 1, and the output is a list of specific target information that can be visualized in three dimensions. This analysis is performed using a machine learning model, which extracts the necessary information based on the content of the data.

[0542] Step 3:

[0543] The server generates a three-dimensional image based on the identified target information. The input is the result list from step 2, and the output is the generated three-dimensional image data. The generated image is designed to closely resemble physical reality using computer graphics technology.

[0544] Step 4:

[0545] The server transmits the generated 3D video data to the communication device. The input is the 3D video data generated in step 3, and the output is the data transferred to the user's communication device. The data is transmitted efficiently using network protocols.

[0546] Step 5:

[0547] The terminal displays the received three-dimensional image. The input is the three-dimensional image data from step 4, and the user can manipulate this image through the interface. Display is performed using smart glasses or augmented reality devices.

[0548] Step 6:

[0549] The device uses sensors to acquire data on the user's facial expressions and voice, and then analyzes their emotions based on that data. The input is biometric data from the user, and the output is analyzed emotional data. The data is processed in real time, and tools such as OpenCV and Dlib are used for analysis.

[0550] Step 7:

[0551] The server dynamically adjusts the content of the 3D video based on the emotion analysis results and sends it back to the communication device. The input is the emotion data from step 6, and the output is the new, adjusted 3D video data. This process provides personalized video that is tailored to the user's state.

[0552] 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.

[0553] 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.

[0554] 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.

[0555] [Fourth Embodiment]

[0556] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0557] 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.

[0558] 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).

[0559] 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.

[0560] 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.

[0561] 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).

[0562] 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.

[0563] 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.

[0564] 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.

[0565] 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.

[0566] 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.

[0567] 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.

[0568] 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".

[0569] One embodiment of the present invention is a system that uses AI to convert existing educational or training information into three-dimensional images, thereby providing viewers with an effective learning experience.

[0570] This system's program incorporates diverse information provided by educational institutions and companies, analyzes it, and determines what to visualize in 3D. AI technology forms the core of the system, extracting necessary data from the collected information and generating a three-dimensional model. The generated 3D image data is delivered to the user's terminal via the network. The user can manipulate this 3D image from various angles using their terminal, allowing students and trainees to deepen their understanding through interactive manipulation.

[0571] For example, in welding technique training, the server captures video footage demonstrating welding procedures, and AI analyzes key actions related to those procedures. Next, the server converts this into a three-dimensional model and generates video footage that recreates specific hand movements and welding rod manipulation. The generated video is sent to the user's terminal, enabling the user to gain a visual and three-dimensional understanding of actual welding techniques.

[0572] This system aims to improve the efficiency of information transmission in education and training, particularly enhancing the learning effectiveness of complex physical movements. In the transmission of craftsmanship and procedures, information that is difficult to convey with existing two-dimensional materials can be learned more intuitively through three-dimensional visualization. The use of this system is expected to simultaneously increase learners' understanding and interest in educational settings and corporate training facilities.

[0573] The following describes the processing flow.

[0574] Step 1:

[0575] The server retrieves information related to education and training from the database and imports it into the system. During the import process, it checks the file format and classifies each data item into categories such as text, images, audio, and video.

[0576] Step 2:

[0577] The server analyzes the collected information using AI. This analysis process involves extracting key points from text information using natural language processing and recognizing important movements from images and videos using image recognition technology. Based on the analysis results, it identifies objects that should be converted into 3D images.

[0578] Step 3:

[0579] The server generates three-dimensional video data based on the object identified by the AI. It creates a three-dimensional model from image and video data, and uses motion capture technology to reproduce actual movements, performing realistic simulations.

[0580] Step 4:

[0581] The server compresses the generated 3D video data to efficiently distribute it. The compressed data is then configured to be sent to the user's terminal via the network.

[0582] Step 5:

[0583] Users receive 3D video transmitted from a server using their terminals and view it through a dedicated display application. The application provides an interface that allows users to observe the 3D video from various angles and manipulate the video from any viewpoint.

[0584] Step 6:

[0585] The terminal records user activity logs and sends the collected information to the server as feedback. Based on this feedback data, the server analyzes the user's learning effectiveness and provides the user with necessary improvements or additional resources.

[0586] (Example 1)

[0587] 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".

[0588] In education and training, it is difficult to convey complex technologies and procedures to learners in an easily understandable way. Furthermore, two-dimensional materials and videos make it difficult to fully understand the details of physical actions, limiting learning effectiveness. Additionally, there is insufficient evaluation of learning effectiveness using learners' operation history.

[0589] 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.

[0590] In this invention, the server includes means for collecting and importing information from educational institutions and companies, means for analyzing the imported information using machine learning algorithms and visual information analysis techniques to extract important features, and means for generating a three-dimensional model using computer graphics software based on the extracted features. This enables learners to effectively learn techniques and procedures through interactive and visual three-dimensional images, and also enables the evaluation of learning effectiveness using operation history.

[0591] "Educational institutions and companies" refers to schools that conduct educational activities and companies that provide training.

[0592] "Collecting information" means the act of selecting and acquiring necessary data.

[0593] A "machine learning algorithm" refers to a computational method that analyzes data and extracts patterns and features.

[0594] "Visual information analysis technology" refers to techniques for extracting useful information from images and videos.

[0595] "Key features" refer to data characteristics that should be given particular attention for a specific purpose.

[0596] "Computer graphics software" refers to programs used to generate and display three-dimensional models.

[0597] A "three-dimensional model" is a digital representation that can be displayed in three dimensions, and its purpose is to visually reproduce actual physical objects and actions.

[0598] "Optimization" refers to the act of adjusting data and processing methods to operate efficiently.

[0599] "User terminal" refers to electronic devices such as computers and tablets used by learners.

[0600] An "interactive learning experience" refers to a learning method in which users can understand the content while directly interacting with it.

[0601] "Operation history" refers to data that records a series of operations performed by a user, and is used to understand the learning progress.

[0602] This invention is a system for providing effective learning experiences using educational information in educational institutions and training facilities. The technologies and processes used in implementing this system are described below.

[0603] The server collects diverse information provided by educational institutions and companies and stores it in a database. This information includes text, images, and videos. The server then utilizes machine learning algorithms and visual information analysis techniques to analyze the collected information. Specifically, it uses natural language processing (NLP) techniques to analyze text information and computer vision techniques to analyze visual information. During this analysis process, important features relevant to the learning objectives are extracted.

[0604] Based on the extracted features, the server uses computer graphics software (e.g., Blender or Unity) to generate a 3D model. The generated 3D model is then optimized to run efficiently on the user's device. This optimization utilizes rendering and data compression techniques.

[0605] The optimized 3D model is delivered to the user's device via the network. The user can view and interactively manipulate the received 3D model on their device. This allows users to deepen their understanding of educational information through a visually engaging experience.

[0606] To give a specific example, in welding technique training, the server captures video footage demonstrating the welding procedure, uses AI to analyze key movements, generates a three-dimensional model, and reproduces hand movements and welding rod manipulation. Users can visually confirm this on their devices and learn while experiencing realistic welding techniques.

[0607] An example of a prompt might be, "Convert the welding procedure into a 3D video. Emphasize important hand movements and tool handling, and visualize it clearly for educational purposes." The system would then take the appropriate action and create the visualization.

[0608] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0609] Step 1:

[0610] The server collects diverse information provided by educational institutions and companies and stores it in a database. Specifically, the server retrieves text, images, videos, etc., from data sources accessible via the network and stores them in the database according to their data format. This process takes educational materials and training videos as input and produces an integrated dataset as output.

[0611] Step 2:

[0612] The server executes machine learning algorithms to analyze the collected information. This process uses natural language processing techniques to analyze text information and extract key terms and procedures. It also uses computer vision techniques to analyze image and video information and extract relevant visual elements. The input here is the dataset obtained in step 1, and the output is the data with key features extracted.

[0613] Step 3:

[0614] The server uses computer graphics software to generate a three-dimensional model based on the extracted features. At this stage, the features extracted in step 2 are provided as input, and the software is used to construct a three-dimensional model. The output is a three-dimensional model suitable for educational purposes. This model is intended to visualize actual physical actions and procedures.

[0615] Step 4:

[0616] The server optimizes the generated 3D model and prepares it for distribution to the user's terminal. Optimization uses data compression techniques to reduce communication bandwidth and device load. The input is the model generated in step 3, and the output is the transferable optimized model.

[0617] Step 5:

[0618] The terminal receives a three-dimensional model distributed from the server and provides an interface to the learner. Through this interface, the user can observe and manipulate the three-dimensional model. The specific input is the model data from the server, and the output is a visual experience through the interface. Through this interaction, the user can gain a deeper understanding of the educational content.

[0619] Step 6:

[0620] Users interact with a three-dimensional model on their device to deepen their understanding. During this process, users can rotate and zoom the model. The input is the three-dimensional model displayed on the device, and the output is the user's own understanding and learning experience.

[0621] (Application Example 1)

[0622] 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".

[0623] Current education and training systems have a two-dimensional nature to information transmission, limiting learners' understanding and skill development. Furthermore, learning standard procedures for machine control units in factories is difficult, and improved assembly accuracy is particularly required for newly introduced machine control units.

[0624] 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.

[0625] In this invention, the server includes means for acquiring existing educational or training information, means for analyzing the acquired information using AI to identify objects to be visualized in three dimensions, and means for using the three-dimensional images for knowledge learning in machine control units to improve assembly accuracy. This enables the provision of an interactive three-dimensional learning experience and improves assembly accuracy in factories.

[0626] "Means of incorporating existing educational or training information" refers to technologies for acquiring educational information and training materials provided by educational institutions and companies in digital format.

[0627] "A method for identifying objects to be analyzed and converted into 3D images using AI" refers to a process that utilizes artificial intelligence technology to analyze the collected information and determine which content should be converted into 3D images.

[0628] "Means for generating three-dimensional images and reproducing actions" refers to a function that creates a three-dimensional image model based on identified information and visually reproduces the associated actions.

[0629] "Means for transmitting generated three-dimensional images to a terminal" refers to technology that delivers the created three-dimensional image data to an appropriate receiving device via a network.

[0630] "Means for displaying and enabling manipulation of three-dimensional images on a terminal" refers to an interface function that displays the received three-dimensional image on the terminal's display, allowing the viewer to freely manipulate the image.

[0631] "A means of improving assembly accuracy by utilizing three-dimensional images for knowledge learning in machine control units" refers to a technology that uses three-dimensional image data to enable machine control units to effectively learn and improve accuracy and efficiency in actual work.

[0632] The system for realizing this invention includes a process for converting educational and training information into 3D images. Specifically, a server digitally acquires information provided by educational institutions and companies. Next, the server uses AI software (e.g., TensorFlow or PyTorch) to analyze the acquired information and identify which content should be converted into 3D images. In this process, important actions and procedures are extracted. Then, a 3D modeling tool (e.g., Unity or Blender) is used to generate a 3D model based on the identified information.

[0633] The generated three-dimensional video data is transmitted to the user's terminal via the network. The terminal displays the received video on its screen and allows the user to manipulate the video. This manipulation includes changing the viewpoint and zooming in and out. This allows the user to understand the educational content visually and intuitively. Furthermore, in factory machine control units, three-dimensional video can be used for learning and to improve assembly accuracy.

[0634] As a concrete example, this system is used in factories when new employees learn the work procedures of experienced workers. Through the generated three-dimensional images, new employees can intuitively grasp the procedures and techniques. An example of a prompt message would be, "Convert the assembly procedure for automobile parts into a three-dimensional image and analyze the specific procedures and actions to teach to new robot A." In this way, it is expected that the effectiveness of education and training will be maximized.

[0635] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0636] Step 1:

[0637] The server digitally retrieves educational information and training materials provided by educational institutions and companies. Input is digital data such as PDFs and video files, which are stored in an internal database. Output is the stored digital educational information.

[0638] Step 2:

[0639] The server uses AI software to analyze the captured digital information and identify the objects to be visualized in 3D. The input is the digital information captured in step 1, and a specific algorithm is used to extract important actions and procedures from this information. The output is a set of information to be visualized in 3D.

[0640] Step 3:

[0641] The server uses a 3D modeling tool to convert the identified information into a 3D model. The input is the information extracted in step 2, and a model generation algorithm is applied to create a 3D image. The output is the completed 3D model data. Specifically, the 3D shape and color information are arranged.

[0642] Step 4:

[0643] The server transmits the generated 3D model data to the user's terminal via the network. The input is the 3D model data obtained in step 3, which is compressed into an appropriate format before transmission. The output is the 3D model data displayed on the user's terminal.

[0644] Step 5:

[0645] The terminal displays the received 3D image on its screen, allowing the user to freely manipulate it. The input is the 3D data received in step 4, and interactive features such as viewpoint change and zoom in / zoom out are provided to give the user an interactive experience. The output is the screen display of the 3D image that the user is manipulating.

[0646] Step 6:

[0647] The system records the user's operation history on the terminal, and the server uses this data to evaluate the user's learning effectiveness. The input is user operation history data, and data analysis techniques are used to quantify learning progress and comprehension. The output is an evaluation result showing the learning effectiveness.

[0648] 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.

[0649] One embodiment of the present invention is a system that uses AI to convert existing educational or training information into three-dimensional images and combines this with a user's emotion engine to further enhance the learning experience. This system analyzes the acquired information, generates specific data as three-dimensional images, and in addition recognizes the user's emotions, dynamically adjusting the images and presentation content based on the user's reactions.

[0650] The program's processing involves the server first ingesting education and training-related data and analyzing it using AI. This analysis determines which information should be visualized as a hologram. Next, the server generates a three-dimensional image based on the identified information and sends it to the user's terminal.

[0651] Users can receive this 3D video using their devices and interact with it interactively through the interface. Furthermore, the devices are equipped with an emotion engine that uses sensors such as cameras and microphones to analyze the user's facial expressions and voice information in real time and send the results to the server. Based on this emotion data, the server evaluates the user's level of understanding and concentration, and customizes and redistributes the video and presented content. This process allows users to learn at their own pace, improving the effectiveness of the education.

[0652] A concrete example is in history education, where users experience historical events and culture through 3D video, and an emotion engine detects the user's interest and surprise. The next content presented then becomes more detailed or new perspectives are added in response to that reaction. This feature allows users to engage in more immersive learning, improving the quality of their learning.

[0653] This system aims to provide a deep and effective learning process tailored to individual learners, rather than simply transmitting knowledge in educational and training settings.

[0654] The following describes the processing flow.

[0655] Step 1:

[0656] The server retrieves information related to education and training from the database and imports it into the system. The imported information is classified into text, images, audio, video, etc., and prepared for analysis.

[0657] Step 2:

[0658] The server analyzes the collected information using AI. The analysis extracts key points from text information using natural language processing and detects important actions from images and videos using image recognition. Based on these analysis results, it determines which content should be represented as a three-dimensional image.

[0659] Step 3:

[0660] The server generates three-dimensional video data based on the identified information. The generation process converts image data into a three-dimensional model and adds motion simulations to create realistic images. This three-dimensional video is then prepared for later access by the user.

[0661] Step 4:

[0662] The server compresses the generated 3D video and prepares it for distribution to the terminal. The data is optimized and transmitted for efficient distribution over the network.

[0663] Step 5:

[0664] The user receives three-dimensional video using a device and displays it through a dedicated application. The application provides an interface that allows the user to view the video from multiple angles and freely change their viewpoint.

[0665] Step 6:

[0666] The device uses its camera and microphone to monitor the user's facial expressions and voice in real time, and analyzes them through an emotion engine. Once the user's emotional state is identified, the analysis results are sent to the server.

[0667] Step 7:

[0668] The server evaluates the user's learning progress and level of interest based on data obtained from the emotion engine. It dynamically adjusts 3D images and presentation content as needed, generating and redistributing optimal learning content to the user. This process allows users to continue learning while maintaining their interest.

[0669] (Example 2)

[0670] 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".

[0671] In traditional education and training settings, standardized information delivery was prevalent, making it difficult to customize content to suit the individual learner's level of understanding and interests. In particular, there was a lack of means to dynamically adjust content based on learners' emotional states using visually intuitive three-dimensional displays. As a result, while there was a demand for improved learning quality and immersion, concrete solutions had not been presented.

[0672] 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.

[0673] In this invention, the server includes means for taking in and analyzing existing training information and converting specific information into a three-dimensional image; means for identifying an object to be visualized using a general-purpose data analysis device; means for generating the identified object as a three-dimensional display and reproducing its actions; and means for collecting the user's emotional data using an emotion analysis unit, adjusting the video display, and retransmitting it. This makes it possible to provide a highly customized learning experience that corresponds to the learner's emotional state and improve educational effectiveness.

[0674] "Existing training information" refers to data that has been accumulated and organized for commonly used educational and training purposes, and includes various formats such as text, images, audio, and video.

[0675] A "general-purpose data analysis device" refers to a computer program or hardware capable of processing and analyzing various types of data through self-learning or pattern recognition.

[0676] A "three-dimensional display" refers to a digital content that adds visual depth to a two-dimensional plane, thereby visually representing a three-dimensional space.

[0677] An "emotion analysis unit" refers to software and hardware that analyzes data such as video and audio to estimate the user's emotional state based on changes in their facial expressions and voice.

[0678] A "central control unit" refers to a server or computer system that integrates multiple system components and has control functions to manage the progress of the overall work and operate the system efficiently.

[0679] A "display device" is hardware used to provide visual information to a user, and includes monitors, head-mounted displays, and projectors.

[0680] This system aims to provide a more effective learning experience in the fields of education and training by utilizing three-dimensional displays. The server first retrieves existing training information from databases and analyzes it using general-purpose data analysis tools. This analysis process utilizes artificial intelligence technology to identify which information is suitable for visualization. The software used includes generative AI models such as TensorFlow.

[0681] Next, the server uses Unity or Unreal Engine to generate a 3D representation from the analysis results. This 3D representation is a real-time virtual content designed to make it easier for users to visually understand the information.

[0682] The generated three-dimensional display is transmitted to a display device via a network. This display device, or terminal, includes hardware such as a monitor or head-mounted display to enable three-dimensional display. Users can manipulate the three-dimensional display shown on the terminal, enabling two-way interaction through the interface.

[0683] The device is equipped with an emotion analysis unit that analyzes the user's facial expressions and voice data in real time to estimate their emotional state. The emotion data obtained from this analysis is sent back to the server, which evaluates the user's level of concentration and comprehension. Based on this, the video content is dynamically adjusted and learning content optimized for the user is resent.

[0684] As a concrete example, during history learning, users visually experience historical events through three-dimensional displays. For instance, if a user shows interest in a particular historical scene, an emotion analysis unit detects this interest, and the server adds and presents relevant detailed information. This functionality allows users to gain a deeper understanding of history.

[0685] An example of a prompt is, "Please suggest a method for presenting the social structure of the Edo period as a three-dimensional image in user-generated history education content." Using such prompts helps the generative AI model generate specific and effective content.

[0686] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0687] Step 1:

[0688] The server retrieves existing training information from databases and external sources. Inputs include multimedia data such as text files, images, and videos. The server analyzes this data using AI technology to determine which information should be visualized as a three-dimensional representation. Specifically, it applies natural language processing and image recognition algorithms to extract key items to be understood. The output is a list of data to be visualized.

[0689] Step 2:

[0690] The server generates a 3D representation using Unity or Unreal Engine based on the data identified in Step 1. The input here is the data to be visualized and its associated resource files. The server utilizes 3D modeling and shader programming techniques to create a real-time, interactive 3D image. During this process, the spatial layout and object interactions are configured. The output is visualized 3D image data.

[0691] Step 3:

[0692] The server transmits the generated 3D display to the terminal via the network. Input includes encoded video data and distribution protocol information. The server uses data compression technology to efficiently transfer the data and enable real-time visualization. Output is streaming data viewable on the terminal.

[0693] Step 4:

[0694] The user visually experiences a three-dimensional display received through the device. The input here is streaming data received from a server. The device displays the three-dimensional image on its screen, enabling user interaction. Users explore the content at their own pace using touchscreens or controllers. The output includes user interaction information and emotional data.

[0695] Step 5:

[0696] The device uses an emotion analysis unit to collect and analyze the user's facial expressions and voice information in real time. Inputs include sensor data acquired from the camera and microphone. The device uses an emotion estimation algorithm to evaluate the user's level of interest and concentration. The output is the generated emotion analysis results, which are sent to the server.

[0697] Step 6:

[0698] The server dynamically adjusts video content to improve the user's learning experience based on the sentiment data it receives. Input includes sentiment data and user interaction information. The server applies machine learning models to reconstruct content that meets user needs and redistributes the adjusted video. The output is adaptively adjusted visualization content.

[0699] (Application Example 2)

[0700] 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".

[0701] Traditional technologies for improving the customer experience in physical stores have struggled to grasp customer emotions and interests in real time and provide products and services that respond accordingly. As a result, it has been difficult to provide information and promotions optimized for each individual customer, and the improvement of the customer experience has not been fully achieved.

[0702] 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.

[0703] In this invention, the server includes means for acquiring existing information, means for analyzing the acquired information using artificial intelligence to identify a target for three-dimensional visualization, and means for acquiring user facial expression and voice data and analyzing emotions. This enables dynamic adjustment of three-dimensional video content and personalized information provision in response to customer emotions in physical stores.

[0704] "Existing information" refers to all types of data related to education, training, and commercial activities, and in particular includes content that should be subject to 3D visualization.

[0705] "Artificial intelligence" refers to the technology that enables computer systems to perform processing that mimics human intelligence, and in this context, it is used for information analysis and target identification.

[0706] "Three-dimensional video" refers to video content that is rendered in three dimensions, enabling a more immersive information experience for the viewer.

[0707] "Communication equipment" refers to devices used to send and receive images and information, and primarily includes terminals that enable user operation.

[0708] "Emotional analysis" is the process of inferring a person's emotional state from data obtained from their facial expressions and voice.

[0709] "Dynamic adjustment" means changing the displayed content and interface in real time based on pre-set conditions.

[0710] In an embodiment of this invention, the server is configured as a system that takes in existing information, analyzes it using artificial intelligence functions, and identifies objects that should be converted into three-dimensional images. Based on the identified information, the server generates a three-dimensional image and transmits this image data to a communication device.

[0711] The communication device displays the received three-dimensional image, allowing the user to operate it through an interface. This utilizes visual output devices such as smart glasses. The communication device is equipped with sensors to capture the user's facial expressions and voice, providing a means to analyze emotions in real time.

[0712] The results of the user's sentiment analysis are sent to the server, which dynamically adjusts the content of the displayed 3D image based on this analysis. This provides personalized content that responds to the user's state and reactions.

[0713] A concrete example of this application is a scenario where, in a physical store, a customer wearing smart glasses shows interest or surprise at a product within the store, and then detailed information about that product and related promotions are instantly presented as a 3D image. The emotion analysis used in this scenario could utilize technologies such as OpenCV or Dlib.

[0714] An example of a prompt using a generative AI model is: "Please describe the details of a system that analyzes customer emotions in real time through smart glasses in a physical store and provides personalized promotions."

[0715] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0716] Step 1:

[0717] The server retrieves existing information from the database. The input consists of text and image data related to educational and commercial activities. Based on this data, it performs preparatory processing to determine which information should be used as a 3D image.

[0718] Step 2:

[0719] The server analyzes the acquired information using an artificial intelligence algorithm. The input is the data prepared in step 1, and the output is a list of specific target information that can be visualized in three dimensions. This analysis is performed using a machine learning model, which extracts the necessary information based on the content of the data.

[0720] Step 3:

[0721] The server generates a three-dimensional image based on the identified target information. The input is the result list from step 2, and the output is the generated three-dimensional image data. The generated image is designed to closely resemble physical reality using computer graphics technology.

[0722] Step 4:

[0723] The server transmits the generated 3D video data to the communication device. The input is the 3D video data generated in step 3, and the output is the data transferred to the user's communication device. The data is transmitted efficiently using network protocols.

[0724] Step 5:

[0725] The terminal displays the received three-dimensional image. The input is the three-dimensional image data from step 4, and the user can manipulate this image through the interface. Display is performed using smart glasses or augmented reality devices.

[0726] Step 6:

[0727] The device uses sensors to acquire data on the user's facial expressions and voice, and then analyzes their emotions based on that data. The input is biometric data from the user, and the output is analyzed emotional data. The data is processed in real time, and tools such as OpenCV and Dlib are used for analysis.

[0728] Step 7:

[0729] The server dynamically adjusts the content of the 3D video based on the emotion analysis results and sends it back to the communication device. The input is the emotion data from step 6, and the output is the new, adjusted 3D video data. This process provides personalized video that is tailored to the user's state.

[0730] 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.

[0731] 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.

[0732] 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 robot 414.

[0733] 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.

[0734] 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.

[0735] 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.

[0736] 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.

[0737] 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.

[0738] 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."

[0739] 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.

[0740] 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.

[0741] 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.

[0742] 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.

[0743] 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.

[0744] 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.

[0745] 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.

[0746] 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.

[0747] 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.

[0748] 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.

[0749] 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.

[0750] 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.

[0751] The following is further disclosed regarding the embodiments described above.

[0752] (Claim 1)

[0753] To take in existing information, analyze it, and convert specific data into three-dimensional images.

[0754] Means for incorporating existing education or training information,

[0755] A method for analyzing captured information using AI to identify objects to be visualized in three dimensions,

[0756] A means for generating a three-dimensional image of a specified object and reproducing its movements,

[0757] A means for transmitting the generated three-dimensional image to a terminal,

[0758] A means of displaying and enabling operation of three-dimensional images on a terminal,

[0759] A system that includes this.

[0760] (Claim 2)

[0761] The system according to claim 1, which records the user's operation history on a terminal and evaluates the learning effect.

[0762] (Claim 3)

[0763] The system according to claim 1, which compresses the generated three-dimensional image and efficiently distributes it over a network.

[0764] "Example 1"

[0765] (Claim 1)

[0766] The means of collecting and incorporating information from educational institutions and companies,

[0767] A means of analyzing the acquired information using machine learning algorithms and visual information analysis techniques to extract important features,

[0768] A means of generating a three-dimensional model using computer graphics software based on extracted features,

[0769] A means for optimizing the generated three-dimensional model using data compression technology and distributing it to the user terminal,

[0770] A means of displaying a three-dimensional model in a manipulable state on a user terminal and providing an interactive learning experience,

[0771] A system that includes this.

[0772] (Claim 2)

[0773] The system according to claim 1, which records the history of interactive operations on a user terminal and evaluates the learner's level of understanding and interest.

[0774] (Claim 3)

[0775] The system according to claim 1, which efficiently delivers an optimized three-dimensional model over a network using a compressed format.

[0776] "Application Example 1"

[0777] (Claim 1)

[0778] To take in existing information, analyze it, and convert specific data into three-dimensional images.

[0779] Means for incorporating existing education or training information,

[0780] A method for analyzing captured information using AI to identify objects to be visualized in three dimensions,

[0781] A means for generating a three-dimensional image of a specified object and reproducing its movements,

[0782] A means for transmitting the generated three-dimensional image to a terminal,

[0783] A means of displaying and enabling operation of three-dimensional images on a terminal,

[0784] A means of improving assembly accuracy by utilizing three-dimensional images for knowledge learning in machine control units,

[0785] A system that includes this.

[0786] (Claim 2)

[0787] The system according to claim 1, which records the user's operation history on a terminal and evaluates the learning effect.

[0788] (Claim 3)

[0789] The system according to claim 1, which compresses the generated three-dimensional image and efficiently distributes it over a network.

[0790] "Example 2 of combining an emotion engine"

[0791] (Claim 1)

[0792] A means for taking in and analyzing existing training information and converting specific information into a three-dimensional image,

[0793] A means for analyzing information acquired using a general-purpose data analysis device and identifying the target for visualization,

[0794] A means for generating a three-dimensional representation of a specified object and reproducing its actions,

[0795] Means for transmitting the generated three-dimensional display to a display device,

[0796] A means for displaying and manipulating a three-dimensional object in a display device,

[0797] A means for collecting user emotion data using an emotion analysis unit in a display device and transmitting it to a central control unit,

[0798] A means by which a central control unit adjusts and retransmits video displays based on emotional data,

[0799] A system that includes this.

[0800] (Claim 2)

[0801] The system according to claim 1, which records the user's operation history and emotion data on a display device and evaluates the learning effect.

[0802] (Claim 3)

[0803] The system according to claim 1, which compresses the generated three-dimensional display and efficiently distributes it via a communication network.

[0804] "Application example 2 when combining with an emotional engine"

[0805] (Claim 1)

[0806] To take in existing information, analyze it, and convert specific data into three-dimensional images.

[0807] Means of incorporating existing information,

[0808] A means of analyzing the captured information using artificial intelligence to identify the object to be visualized in three dimensions,

[0809] A means for generating a three-dimensional image of a specified object and reproducing its movements,

[0810] A means for transmitting the generated three-dimensional image to a communication device,

[0811] A means for displaying and enabling operation of three-dimensional images in a communication device,

[0812] A method for acquiring user facial expressions and voice data and analyzing emotions,

[0813] A means of dynamically adjusting the content of a three-dimensional image based on the results of user emotion analysis,

[0814] A system that includes this.

[0815] (Claim 2)

[0816] The system according to claim 1, which records the user's operation history in a communication device and evaluates the learning effect.

[0817] (Claim 3)

[0818] The system according to claim 1, which compresses the generated three-dimensional image and efficiently distributes it via a communication network. [Explanation of symbols]

[0819] 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. To take in existing information, analyze it, and convert specific data into three-dimensional images. Means for incorporating existing education or training information, A method for analyzing captured information using AI to identify objects to be visualized in three dimensions, A means for generating a three-dimensional image of a specified object and reproducing its movements, A means for transmitting the generated three-dimensional image to a terminal, A means of displaying and enabling operation of three-dimensional images on a terminal, A system that includes this.

2. The system according to claim 1, which records the user's operation history on a terminal and evaluates the learning effect.

3. The system according to claim 1, which compresses the generated three-dimensional image and efficiently distributes it over a network.

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