system

Abstract

We provide the system. [Solution] A means for collecting information from a data supply device and training an artificial intelligence model based on said information, A means for creating individual user attributes and managing each user's learning progress based on those user attributes, A means for receiving input from a user and generating information corresponding to that input using an artificial intelligence model, A means for receiving the generated information and displaying it to the user, A means of collecting feedback from the user and using that feedback to improve the system's performance, Means for providing visual and audio instruction for specific equipment, 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, the method including: receiving a user utterance; adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot; encoding the prompt; and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In a modern corporate environment, there is a need to improve the skills of each employee and efficiently cultivate human resources. However, with conventional education methods, it is difficult to meet individual needs, and a great deal of time and cost are required for human resource development. Also, it is difficult to respond to the questions of individual employees on a 24-hour basis. As a result, it has become an issue to provide appropriate educational opportunities, particularly for employees at various career stages.

Means for Solving the Problems

[0005] This invention provides a system that can automatically provide learning support tailored to individual user profiles by collecting business documents and training an artificial intelligence model based on them. This system generates information based on user input, presents that information to the user, and improves performance by utilizing user feedback. Furthermore, by tracking learning progress data, it generates individual progress reports, realizing visualized learning support. It also improves user learning efficiency through promotions that include comprehension support using visual or audio content.

[0006] "Data storage" refers to a storage device used to store business documents and other digital information.

[0007] "Business documents" refer to manuals, regulations, and other materials that contain information related to a company's operations.

[0008] An "artificial intelligence model" is a computational algorithm trained to perform a specific task based on given data.

[0009] A "user profile" is a dataset that contains information and learning history about an individual user.

[0010] "Managing learning progress" refers to the process of tracking a user's progress in their educational process and determining the necessary learning support.

[0011] "User input" refers to queries and data that users send to the system to request information.

[0012] "Information generation" refers to the process by which a computer creates responses and information based on collected data in response to user input.

[0013] "Collecting feedback" is the process of gathering user reactions and evaluations in order to improve the performance of a system.

[0014] The "Progress Report" is a report summarizing the results of analyzing the learning status of users.

[0015] "Visual or audio content" is an information representation form by images, videos, and sounds provided for learning support.

Brief Description 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 the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of the 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, the labeled processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), and the like.

[0020] In the following embodiments, the labeled 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, the labeled 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 disk (e.g., hard disk), or magnetic tape, 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] This invention is implemented as an employee training support system using artificial intelligence. This system provides individually optimized training content to each employee based on business documents held by the company. The system's program processing and specific examples are shown below.

[0038] The server collects business documents and trains the AI ​​model.

[0039] The server retrieves various business documents and internal regulations provided by the company from data storage. Using this retrieved data, it trains an artificial intelligence model to build an algorithm for generating answers to business knowledge-related questions. The accurate functioning of this model ensures a foundation that allows users to easily obtain the information they need.

[0040] Users access the educational support system through their devices.

[0041] Users access the system using terminals with company-specific applications installed. For example, if a user wants to learn how to use new software, they enter a question into their terminal. This question is sent to the server, and the relevant information is generated through an AI model.

[0042] The device presents information to the user.

[0043] The terminal displays information received from the server to the user. For example, it may present a step-by-step guide showing the installation procedure for new software as visual diagrams and text on the terminal. It is also possible to provide voice guidance to instruct the user on the installation procedure.

[0044] Gathering and analyzing feedback

[0045] Users can provide feedback on the information provided via their device. This feedback is collected on the server to improve system performance and enhance the user experience.

[0046] For example, if a new employee asks a question about company policy, the server uses an AI model to refer to the latest policy documentation and generate an appropriate answer. The terminal then provides the answer to the employee and receives feedback on whether the employee understood it. This process enables consistent information delivery and education tailored to individual learning needs. Overall, this system streamlines corporate talent development and enables the creation of highly skilled and immediately productive employees.

[0047] The following describes the processing flow.

[0048] Step 1:

[0049] The server retrieves business documents from data storage. These include the latest manuals, company regulations, and business-related materials. The server prepares these documents as a training set for an artificial intelligence model.

[0050] Step 2:

[0051] The server trains an artificial intelligence model based on business documents it has acquired. The model utilizes natural language processing techniques to improve its ability to understand information within documents and generate answers to user questions.

[0052] Step 3:

[0053] The user operates the device and enters questions related to their learning needs. For example, they might ask questions about specific work procedures, such as "I'd like to know how to log calls with clients."

[0054] Step 4:

[0055] The device sends user input to the server. The server receives this input, analyzes the relevant information using an AI model, and generates a response.

[0056] Step 5:

[0057] The server sends the generated response to the device. The response includes detailed instructions and visual content as needed. The device displays this to the user.

[0058] Step 6:

[0059] Users perform their tasks by referring to information displayed on their devices. If the information is helpful, users can provide feedback on their devices.

[0060] Step 7:

[0061] The server collects user feedback and analyzes it to improve system response accuracy and user experience. The collected feedback is used to improve subsequent models and create new training datasets.

[0062] (Example 1)

[0063] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0064] In today's business environment, there is a need for effective ways to provide employees with diverse business knowledge and skills and to promote their growth. However, conventional training systems struggle to flexibly respond to each employee's learning progress and individual needs, and it is difficult to guarantee consistent learning outcomes. Furthermore, traditional systems have limited information presentation, which fails to enhance the level of understanding by users.

[0065] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0066] In this invention, the server includes means for collecting documents from a data storage device and training a machine learning model based on said documents; means for creating individual user profiles and managing each user's learning progress based on said user profiles; and means for receiving input from users and generating information corresponding to said input using the machine learning model. This makes it possible to provide each employee with appropriately individualized educational content and to enhance user comprehension through the presentation of visual and auditory supplementary information.

[0067] A "data storage device" is a general term for storage media and systems that can store information and retrieve it as needed.

[0068] A "document" refers to a collection of written text or data recorded for a specific purpose or content, and is an information resource that includes both paper and electronic media.

[0069] A "machine learning model" refers to a system of algorithms that learn patterns and rules from data to make predictions and decisions.

[0070] A "user profile" is a dataset that compiles information about the attributes and behaviors of a specific user, with the aim of providing services tailored to individual needs.

[0071] "Visual information" refers to forms of information perceived through the eyes, such as shapes, images, and videos.

[0072] "Auditory information" refers to information provided through sound, specifically sound data transmitted through speech synthesis or recording.

[0073] "Response" refers to the feedback or reply that users give to the information or service presented, and it reflects their experience and satisfaction level.

[0074] This system provides individually optimized educational content based on business documents held by companies. An embodiment of this system is shown below.

[0075] The server collects various documents from data storage devices. This collected data mainly includes business-related documents and internal regulations. Using this data, the server trains generative AI models using machine learning frameworks, such as TENSORFLOW® or PyTorch. This model builds algorithms to generate answers to questions based on different business knowledge. This training provides a foundation for users to easily obtain the information they need.

[0076] Users access this educational support system on a device with a dedicated application installed. For example, if a user needs information on how to use new software, they input the question as a prompt on their device. A specific example of such a prompt might be, "Please tell me how to use the new software." This prompt is sent from the device to the server, where an AI model generates the relevant information, and the device receives the result.

[0077] The device uses information transmitted from the server to present visual or audio information to the user. This process includes displaying visual guides and text, or using a voice assistant to deliver information verbally. Examples include illustrating the installation procedure for new software with diagrams or providing an audio explanation of the installation process. This improves user comprehension and maximizes the effectiveness of educational content.

[0078] Furthermore, users can input feedback on the information they receive into their devices. This feedback is sent to the server and used to improve the entire system. Through the analysis of this feedback, it becomes possible to pursue improvements in the performance of the AI ​​model and the user experience.

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

[0080] Step 1:

[0081] The server collects necessary documents from the data storage device. Specifically, the server accesses the database and downloads business-related documents and internal regulations within the company. The input is a database query, and the output is a collection of documents as raw data. This dataset will form the basis for AI training in the next stage.

[0082] Step 2:

[0083] The server trains a machine learning model using the collected documents. Specifically, the server utilizes machine learning frameworks such as TensorFlow and PyTorch to tokenize the document data and feed it to the model. The input is the document data collected in the previous step, and the output is the trained generative AI model. This process enables the model to answer questions about specific business knowledge.

[0084] Step 3:

[0085] Users access the educational support system via a terminal. For example, a user might input a prompt message such as "Please tell me how to use the new software." The input is the prompt message the user enters into the terminal, and the output is the data of the entered prompt message. This information forms the basis for the next request to the server.

[0086] Step 4:

[0087] The server analyzes the prompt text received from the user and generates corresponding information using a generative AI model. The server inputs the prompt text into the AI ​​model and generates an answer based on the relevant knowledge. The input is the prompt text data received from the user, and the output is the answer data generated by the AI ​​model.

[0088] Step 5:

[0089] The device receives response data from the server and presents it to the user. Specifically, it displays text and diagrams on the UI as visual information, or reads out instructions using a voice assistant function as audio information. The input is the response data received from the server, and the output is the information presented to the user visually or audibly.

[0090] Step 6:

[0091] Users provide feedback on the information provided by entering it into their device. Specifically, they evaluate the quality and usefulness of the information using rating buttons and comment fields on the UI. The input is the feedback information entered by the user, and the output is that feedback data.

[0092] Step 7:

[0093] The server collects user feedback and uses it to improve the overall system performance. Specifically, it analyzes the feedback data to adjust the accuracy of the AI ​​model and reflects this in future response generation. The input is the feedback data collected from users, and the output is the improved system performance based on that feedback.

[0094] (Application Example 1)

[0095] 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."

[0096] The present invention aims to provide knowledge about the technologies used in specific equipment quickly and efficiently, thereby promoting the improvement of users' skills. In particular, it aims to solve the problem of systems that have means of providing accurate information about procedures and maintenance methods visually and audibly.

[0097] 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.

[0098] In this invention, the server includes means for collecting information from a data supply device and training an artificial intelligence model based on said information; means for creating individual user attributes and managing each user's learning progress based on said user attributes; and means for providing visual and audio instruction for specific equipment. This enables users to improve their skills quickly and efficiently.

[0099] A "data supply device" is a device used to collect and provide information.

[0100] An "artificial intelligence model" is a computer program designed to perform a specific task using machine learning algorithms.

[0101] "User attributes" refer to profile information created based on each user's individual characteristics and learning history.

[0102] "Visual and audio instruction" refers to a means of providing information to users and supporting their learning through visual guides and audio instructions.

[0103] "Learning progress" refers to the degree of progress a user has made in deepening their understanding of a particular technology or knowledge.

[0104] This invention provides an effective educational support system for factories and specific facilities. The system consists of a data supply device, an artificial intelligence model, user attribute creation, and visual and audio instruction methods.

[0105] The server collects information from data supply devices via the network. The collected information is fed into a generative AI model trained using Python. This forms an artificial intelligence model based on machine learning algorithms. For data processing, the NLTK library is used as a text mining tool, enabling natural language processing.

[0106] Users access the system via their devices and request information tailored to their learning needs. During this process, the user's progress is tracked, and individualized educational programs are generated based on user attributes. Questions entered by the user are sent from the server to an execution environment used by an artificial intelligence model.

[0107] The terminal provides users with generated content through a visual interface and audio guidance. Specifically, it visualizes data obtained via GraphQL using a GUI toolkit and provides voice guidance using speech synthesis technology built into the robot.

[0108] In the feedback process, user feedback is stored in data storage, and the server uses this data to improve the system. MySQL® is used for database management, which helps in the accumulation and analysis of feedback.

[0109] As a concrete example, when a user asks, "What are the procedures for routine robot maintenance?", the system immediately provides a graphical maintenance procedure and voice guidance. An example of a prompt here is, "Please tell me the routine robot maintenance procedure described in the company's business documents." In this way, an efficient learning environment can be created to support the user's skill improvement.

[0110] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0111] Step 1:

[0112] The server collects information from data supply devices. This information includes factory technical documents and procedures, which are stored in a database. Natural language processing tools are used to clean and classify the data, receiving it as input and obtaining processed text data as output.

[0113] Step 2:

[0114] The server trains a generative AI model using the collected data. Specifically, it uses machine learning algorithms to build information classification and prediction models. The input is processed text data, and the output is a trained AI model. The generative AI model is optimized using Python and the NLTK library.

[0115] Step 3:

[0116] Users input questions into the system via their terminal to receive a personalized learning program based on their user attributes. For example, the user uses a "prompt" to request specific information. This input allows the AI ​​model to generate the optimal answer. It receives user questions as input and generates information as output.

[0117] Step 4:

[0118] The server generates answers to user questions. Using an AI model, it derives appropriate answers to input questions and sends them to the terminal. It executes data queries using the GraphQL protocol to retrieve the necessary information.

[0119] Step 5:

[0120] The terminal presents information received from the server to the user visually and audibly. Specifically, it visualizes the information using a GUI toolkit and converts text to speech using speech synthesis technology. It visualizes and voices AI-generated information as input and provides it to the user as output.

[0121] Step 6:

[0122] Users provide feedback on the information provided. This feedback is sent from the terminal to the server to help improve the system. User feedback is received as input, and performance improvement data is obtained as output.

[0123] Step 7:

[0124] The server stores user feedback in a database and improves system performance through analysis. MySQL is used to manage data storage. The accumulated feedback is used as a guideline for system updates.

[0125] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0126] The system of this invention is built as an information provision platform that combines an emotion engine. This system can analyze the user's emotions in real time and adaptively adjust the content and format of the information provided. The system configuration and specific program processing are described below.

[0127] Server-based processing of business data and training of AI models

[0128] The server retrieves corporate business documents from data storage and uses them to train an artificial intelligence model. This model has the ability to integrate information within the documents and generate appropriate responses to user input. The trained model responds quickly and accurately to a variety of user questions.

[0129] User interface and emotion engine functionality

[0130] When a user uses a device and enters questions or requests for information regarding their learning needs, the device, equipped with an emotion engine, analyzes the user's emotional state. For example, it extracts emotional data from facial expressions and tone of voice using the camera and microphone. The emotion engine analyzes this data to infer whether the user is tense or relaxed, for instance.

[0131] The process of generating and providing information

[0132] The server receives user input and emotional data, and uses a trained AI model to generate optimal information. It adjusts the tone and format of the information provided based on the user's emotional state. For example, if the user is feeling anxious, it provides reassuring language and simple, step-by-step guidance. It also utilizes visual content and voice assistant functions to support user understanding.

[0133] Feedback and system improvements

[0134] Users can provide feedback on the information provided via their device. This feedback is collected on the server and used to further improve system training and enhance the user experience. For example, if a new employee is learning the procedures for a complex project, the system will present information considering their level of understanding and emotional state. This makes the information easier for the user to accept and improves their ability to perform their job.

[0135] This embodiment of the present invention integrates emotion recognition technology to enable the provision of information that is tailored to the user's psychological state, thereby enhancing the ability to provide individualized support compared to conventional educational systems. As a result, companies can achieve effective human resource development for diverse employees.

[0136] The following describes the processing flow.

[0137] Step 1:

[0138] The server retrieves business documents from the company's data storage. This process includes the latest business manuals and policy documents. The server formats these documents for analysis and stores them in a database.

[0139] Step 2:

[0140] The server trains an artificial intelligence model based on business documents. The AI ​​model utilizes natural language processing technology to enhance its ability to generate answers to user questions based on the information in the documents.

[0141] Step 3:

[0142] The user uses a device to access the educational support system. They input specific questions or learning needs, such as "I need help learning how to use the new project management tool."

[0143] Step 4:

[0144] A device equipped with an emotion engine collects emotional data using a camera and microphone when the user is making input. It analyzes the user's emotional state from their facial expressions and voice tone to determine whether they are relaxed or stressed.

[0145] Step 5:

[0146] The device sends user input and emotional data to the server. This includes digital data related to the questions asked and the emotional state.

[0147] Step 6:

[0148] Based on the data received by the server, an artificial intelligence model is used to generate optimal information. The generated information is adjusted in tone and expression according to the user's emotional state. For example, if the user is showing anxiety, a polite and easy-to-understand guide is created to provide reassurance.

[0149] Step 7:

[0150] The server sends generated information and recommended visual or audio content to the device. The device then presents this information to the user to support their learning.

[0151] Step 8:

[0152] The user reviews the information provided and uses it in their work. Further questions and feedback are provided as needed.

[0153] Step 9:

[0154] The device sends user feedback to the server. The server uses this feedback to improve system performance and adjust its response so that the next response is even better.

[0155] (Example 2)

[0156] 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 will be referred to as the "terminal."

[0157] In modern business, personalized information acquisition and learning are crucial challenges. However, there are still few systems that can provide dynamic information tailored to the emotional state and individual needs of users. Conventional systems fail to consider the psychological barriers of users, limiting the effectiveness of information provision. Solving this problem is the objective of this invention.

[0158] 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.

[0159] In this invention, the server includes means for collecting business information from a data storage device and training an intelligent model, means for managing learning progress based on individual user information, and means for analyzing emotional data and adjusting information. This makes it possible to provide appropriate information in accordance with the user's emotions and to provide a more effective learning experience.

[0160] A "data storage device" is hardware or a system that can store digital information and retrieve it as needed.

[0161] "Business information" refers to all information, including document data and electronic records, that a company or organization generates or uses in the course of its daily operations.

[0162] An "intelligent model" is an artificial intelligence algorithm or system that learns patterns and knowledge from data, imitates human intellectual work, and performs specific tasks.

[0163] "User information" refers to data that includes attributes, behavioral history, and preferences of individual users, and is a component that forms a unique profile of that person.

[0164] "Emotional data" refers to data that quantifies or categorizes a user's emotional state, and includes information obtained from facial expressions, tone of voice, behavior, etc.

[0165] "Information adjustment" is the process of optimizing the content and format of information provided according to the user's needs and circumstances.

[0166] An "effective learning experience" is a learning process optimized to help users easily understand, remember, and appropriately utilize information.

[0167] The system of the present invention is an advanced platform designed to dynamically provide digital information. This system is implemented using data storage devices, intelligent models, and terminal components.

[0168] The server efficiently retrieves business information from data storage devices. This includes corporate and organizational document data, which the server extracts as needed via database queries and prepares as training datasets for intelligent models.

[0169] The intelligent model is trained based on acquired business information. Natural language processing techniques are used to learn patterns and knowledge within the information, and the model is adjusted to generate appropriate responses to user input. Specific resource examples include machine learning libraries and cloud-based computing resources.

[0170] The terminal receives direct input from the user and captures questions and information requests through the interface. Similarly, emotional data is analyzed in real time using the camera and microphone. This emotional data analysis uses state-of-the-art emotion recognition software, which analyzes the user's facial expressions and tone of voice.

[0171] The server integrates text input and emotional data received from the terminal and generates information using a generated AI model. During this process, information adjustment is performed, changing the tone and format of the information to match the user's emotional state. For example, if the user is feeling anxious, the server will provide information in a reassuring tone.

[0172] For example, when a user asks a question with a prompt such as, "Please explain the basic setup procedure for the new project management tool. Please make it easy for a beginner," the system generates a visual guide and simple step-by-step instructions, which are immediately provided through the terminal. Furthermore, when user feedback is entered into the terminal, that information is sent to the server and used as data to improve the system's performance.

[0173] Thus, the present invention is a system that provides users with an excellent information experience through integrated processing by data storage, artificial intelligence models, and terminals.

[0174] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0175] Step 1:

[0176] The server retrieves business information from data storage devices. Specifically, the server issues SQL queries to the database to select and extract document data for companies and organizations. The input is database connection information, and the output is document data as business information. This output data is then formatted for use in training subsequent AI models.

[0177] Step 2:

[0178] The server uses acquired business information to train an intelligent model. The intelligent model is an algorithm that employs natural language processing techniques. The server vectorizes document data as input and feeds it into the model using machine learning libraries. The output is a trained model with tuned parameters. This model enables the generation of adaptive responses to each user's inquiry.

[0179] Step 3:

[0180] The terminal receives input from the user through an interface. The user inputs questions and information requests in natural language. The input consists of text-based prompts, as well as sentiment data acquired in real time using the camera and microphone. The output consists of raw text data and sentiment analysis results.

[0181] Step 4:

[0182] The server integrates text input and sentiment data received from the terminal and generates information using a generated AI model. Specifically, the server provides prompt sentences to the model and adjusts the output based on the sentiment data. The input consists of text data and sentiment data, and the output is response information adapted to the user's emotional state.

[0183] Step 5:

[0184] The terminal displays information sent from the server to the user. Specifically, it presents information using text, graphics, and a voice assistant. The input is the response information from the server, and the output is the information provided to the user in visual and audio formats.

[0185] Step 6:

[0186] Users input feedback on information via their terminals. This feedback includes suggestions for improvement and comments on accuracy. The input is captured through a feedback form, and the output is feedback data sent to the server. The server analyzes this feedback and uses it to improve system performance.

[0187] (Application Example 2)

[0188] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0189] Modern information delivery systems present information without considering the user's emotional state, making it difficult for users to accept the information and resulting in insufficient individualized support, particularly in caregiving settings. Current systems are especially inadequate in situations where appropriate responses and information provision based on emotions are required. Therefore, there is an urgent need to develop a system that responds to users' emotions, provides a sense of security, and delivers necessary information.

[0190] 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.

[0191] In this invention, the server includes means for collecting business documents from a data storage device and training an intelligent algorithm based on said business documents; means for creating individual user information and managing the learning progress of each user based on said user information; means for receiving input from users and generating information corresponding to said input using an intelligent algorithm; and means for analyzing the emotional state of users and adjusting the tone and format of the information generated based on the analysis results. This makes it possible to provide information that is in line with the emotional state of users, and in nursing care settings, it becomes possible to provide necessary information effectively while giving users a sense of security.

[0192] A "data storage device" is a physical or virtual device used to collect and store data such as business documents and user information.

[0193] "Business documents" are official or informal documents created and used within an organization or company that contain information related to the performance of business.

[0194] An "intelligent algorithm" is a computational method that has the ability to learn from collected data and generate and integrate information to perform a specific task.

[0195] "User information" refers to data about individual users, including information that indicates their learning progress and characteristics.

[0196] "Learning progress" refers to the process of tracking and evaluating the user's acquisition of knowledge and skills over time.

[0197] "Input" refers to data provided by users, including needs and questions that form the basis for information generation.

[0198] "Emotional state" refers to a state that indicates a user's psychological response and emotional changes, and is analyzed through facial expressions, tone of voice, and other factors.

[0199] "Analysis results" refer to information obtained by analyzing the acquired data and clarifying its content and patterns.

[0200] "Tone and format" refers to the style and method of presentation used when information is presented to the user, and is adjusted to aid in understanding the content.

[0201] The system implementing this invention consists primarily of a smart device worn by the user and a server operating in the backend. The server collects business documents from data storage devices, trains an intelligent algorithm based on these documents, and generates appropriate information in response to user input. By using smart glasses or a device equipped with a display, users can check the necessary information in real time. At this time, an emotion analysis engine analyzes the user's emotional state and adjusts the tone and format of the information based on the analysis results, enabling the provision of more personalized services.

[0202] The hardware used includes devices such as smart glasses and tablets, which are equipped with cameras and microphones. For software, Microsoft® Azure® Face API or equivalent technologies are used for emotion analysis, while TensorFlow and PyTorch are used for training intelligent algorithms. Data is processed in a cloud environment, and the generated information is presented to the user via a digital display device.

[0203] For example, using smart glasses equipped with emotion analysis capabilities in a care setting allows for real-time analysis of the care recipient's emotional state, enabling the provision of appropriate care information to caregivers. Generative AI models can also be used to provide rapid and effective information even in situations requiring individualized attention.

[0204] An example of a prompt is, "If a dementia patient is feeling anxious, please tell me how to reassure them." Using this prompt, a generative AI model is utilized to generate and present the most appropriate response.

[0205] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0206] Step 1:

[0207] The server retrieves business documents from data storage. It receives business document data as input and processes it into a format usable as training data for intelligent algorithms. This processed data provides the foundation for learning by the intelligent algorithms.

[0208] Step 2:

[0209] The server uses an intelligent algorithm to train a model based on business documents. The data processed in step 1 is used as input to train the model. This results in the output of a trained model capable of quickly responding to user input.

[0210] Step 3:

[0211] The terminal receives input data from the user. This input includes questions and information requests. This input data is sent to the server, which requests the generation of appropriate information.

[0212] Step 4:

[0213] The server generates corresponding information using a trained intelligent algorithm based on user input data. The data processing involved is information retrieval based on the input and integration of the results. The output provides a detailed answer to the user's question.

[0214] Step 5:

[0215] The terminal displays information output from the server to the user. The display format is either text or visual content, specifically including on-screen display or audio presentation.

[0216] Step 6:

[0217] The device's emotion analysis engine analyzes the user's emotional state in real time. Using data collected through the camera and microphone as input, it analyzes the user's facial expressions and voice tone, and sends the analysis results to the server.

[0218] Step 7:

[0219] The server receives the results of sentiment analysis and adjusts the tone and format of the output information based on them. Data processing includes reformatting the information according to the analysis results. This generates information optimized for the user.

[0220] Step 8:

[0221] User feedback is collected through the device. This feedback data is sent to a server and used to improve the system. This feedback will also be used as training data for future algorithms.

[0222] Step 9:

[0223] The server analyzes the collected feedback and further improves the model. Based on the challenges indicated by the feedback, the intelligent algorithm is adjusted. As a result, a system capable of more accurate responses is completed.

[0224] Through the steps described above, a system is built that enables the provision of appropriate, emotion-based information to users.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] [Second Embodiment]

[0229] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0230] 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.

[0231] 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).

[0232] 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.

[0233] 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.

[0234] 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).

[0235] 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.

[0236] 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.

[0237] 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.

[0238] 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.

[0239] 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.

[0240] 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".

[0241] This invention is implemented as an employee training support system using artificial intelligence. This system provides individually optimized training content to each employee based on business documents held by the company. The system's program processing and specific examples are shown below.

[0242] The server collects business documents and trains the AI ​​model.

[0243] The server retrieves various business documents and internal regulations provided by the company from data storage. Using this retrieved data, it trains an artificial intelligence model to build an algorithm for generating answers to questions related to business knowledge. The accurate functioning of this model ensures a foundation that allows users to easily obtain the information they need.

[0244] Users access the educational support system through their devices.

[0245] Users access the system using terminals with company-specific applications installed. For example, if a user wants to learn how to use new software, they enter a question into their terminal. This question is sent to the server, and relevant information is generated through an AI model.

[0246] The device presents information to the user.

[0247] The terminal displays information received from the server to the user. For example, it may present a step-by-step guide showing the installation procedure for new software as visual diagrams and text on the terminal. It is also possible to provide voice guidance to instruct the user on the installation procedure.

[0248] Gathering and analyzing feedback

[0249] Users can provide feedback on the information provided via their device. This feedback is collected on the server to improve system performance and enhance the user experience.

[0250] For example, if a new employee asks a question about company policy, the server uses an AI model to refer to the latest policy documentation and generate an appropriate answer. The terminal then provides the answer to the employee and receives feedback on whether the employee understood it. This process enables consistent information delivery and education tailored to individual learning needs. Overall, this system streamlines corporate talent development and enables the creation of highly skilled and immediately productive employees.

[0251] The following describes the processing flow.

[0252] Step 1:

[0253] The server retrieves business documents from data storage. These include the latest manuals, company regulations, and business-related materials. The server prepares these documents as a training set for an artificial intelligence model.

[0254] Step 2:

[0255] The AI ​​model is trained based on business documents acquired by the server. The model utilizes natural language processing technology to improve its ability to understand information within documents and generate answers to user questions.

[0256] Step 3:

[0257] The user operates the device and enters questions related to their learning needs. For example, they might ask questions about specific work procedures, such as "I'd like to know how to log calls with clients."

[0258] Step 4:

[0259] The device sends user input to the server. The server receives this input, analyzes the relevant information using an AI model, and generates a response.

[0260] Step 5:

[0261] The server sends the generated response to the device. The response includes detailed instructions and visual content as needed. The device displays this to the user.

[0262] Step 6:

[0263] Users perform their tasks by referring to information displayed on their devices. If the information is helpful, users can provide feedback on their devices.

[0264] Step 7:

[0265] The server collects user feedback and analyzes it to improve system response accuracy and user experience. The collected feedback is used to improve subsequent models and create new training datasets.

[0266] (Example 1)

[0267] 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".

[0268] In today's business environment, there is a need for effective ways to provide employees with diverse business knowledge and skills and to promote their growth. However, conventional training systems struggle to flexibly respond to each employee's learning progress and individual needs, and it is difficult to guarantee consistent learning outcomes. Furthermore, traditional systems have limited information presentation, which fails to enhance the level of understanding by users.

[0269] 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.

[0270] In this invention, the server includes means for collecting documents from a data storage device and training a machine learning model based on said documents; means for creating individual user profiles and managing each user's learning progress based on said user profiles; and means for receiving input from users and generating information corresponding to said input using the machine learning model. This makes it possible to provide each employee with appropriately individualized educational content and to enhance user comprehension through the presentation of visual and auditory supplementary information.

[0271] A "data storage device" is a general term for storage media and systems that can store information and retrieve it as needed.

[0272] A "document" refers to a collection of written text or data recorded for a specific purpose or content, and is an information resource that includes both paper and electronic media.

[0273] A "machine learning model" refers to a system of algorithms that learn patterns and rules from data to make predictions and decisions.

[0274] A "user profile" is a dataset that compiles information about the attributes and behaviors of a specific user, with the aim of providing services tailored to individual needs.

[0275] "Visual information" refers to forms of information perceived through the eyes, such as shapes, images, and videos.

[0276] "Auditory information" refers to information provided through sound, specifically sound data transmitted through speech synthesis or recording.

[0277] "Response" refers to the feedback or reply that users give to the information or service presented, and it reflects their experience and satisfaction level.

[0278] This system provides individually optimized educational content based on business documents held by companies. An embodiment of this system is shown below.

[0279] The server collects various documents from data storage devices. This collected data mainly includes business-related documents and internal regulations. Using this data, the server trains generative AI models using machine learning frameworks, such as TensorFlow and PyTorch. This model builds algorithms to generate answers to questions based on different business knowledge. This training provides a foundation for users to easily obtain the information they need.

[0280] Users access this educational support system on a device with a dedicated application installed. For example, if a user needs information on how to use new software, they input the question as a prompt on their device. A specific example of such a prompt might be, "Please tell me how to use the new software." This prompt is sent from the device to the server, where an AI model generates the relevant information, and the device receives the result.

[0281] The device uses information transmitted from the server to present visual or audio information to the user. This process includes displaying visual guides and text, or using a voice assistant to deliver information verbally. Examples include illustrating the installation procedure for new software with diagrams or providing an audio explanation of the installation process. This improves user comprehension and maximizes the effectiveness of educational content.

[0282] Furthermore, users can input feedback on the information they receive into their devices. This feedback is sent to the server and used to improve the entire system. Through the analysis of this feedback, it becomes possible to pursue improvements in the performance of the AI ​​model and the user experience.

[0283] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0284] Step 1:

[0285] The server collects the necessary documents from the data storage device. As a specific operation, the server accesses the database and downloads business-related documents and internal regulations within the company. The input is a query to the database, and the output is a group of documents as raw data. This dataset serves as the basis for AI training in the next stage.

[0286] Step 2:

[0287] The server trains a machine learning model using the collected documents. Specifically, the server utilizes a machine learning framework such as TensorFlow or PyTorch to tokenize the document data and feed it into the model. The input is the document data collected in the previous step, and the output is a trained generative AI model. Through this process, the model becomes capable of answering questions regarding specific business knowledge.

[0288] Step 3:

[0289] The user accesses the education support system via a terminal. For example, the user performs an operation of inputting a prompt sentence such as "Please teach me how to use the new software". The input is the prompt sentence entered by the user on the terminal, and the output is the data of the entered prompt sentence. This information serves as the basis for the next request to the server.

[0290] Step 4:

[0291] The server analyzes the prompt sentence received from the user and generates corresponding information using the generative AI model. The server inputs the prompt sentence into the AI model and generates an answer based on the relevant knowledge. The input is the prompt sentence data received from the user, and the output is the answer data generated by the AI model.

[0292] Step 5:

[0293] The device receives response data from the server and presents it to the user. Specifically, it displays text and diagrams on the UI as visual information, or reads out instructions using a voice assistant function as audio information. The input is the response data received from the server, and the output is the information presented to the user visually or audibly.

[0294] Step 6:

[0295] Users provide feedback on the information provided by entering it into their device. Specifically, they evaluate the quality and usefulness of the information using rating buttons and comment fields on the UI. The input is the feedback information entered by the user, and the output is that feedback data.

[0296] Step 7:

[0297] The server collects user feedback and uses it to improve the overall system performance. Specifically, it analyzes the feedback data to adjust the accuracy of the AI ​​model and reflects this in future response generation. The input is the feedback data collected from users, and the output is the improved system performance based on that feedback.

[0298] (Application Example 1)

[0299] 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."

[0300] The present invention aims to provide knowledge about the technologies used in specific equipment quickly and efficiently, thereby promoting the improvement of users' skills. In particular, it aims to solve the problem of systems that have means of providing accurate information about procedures and maintenance methods visually and audibly.

[0301] 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.

[0302] In this invention, the server includes means for collecting information from a data supply device and training an artificial intelligence model based on the information, means for creating individual user attributes and managing the learning progress of each user based on the user attributes, and means for providing visual and audio guidance for specific facilities. Thereby, it becomes possible for users to improve their skills quickly and efficiently.

[0303] The "data supply device" is a device for collecting and providing information.

[0304] The "artificial intelligence model" is a computer program designed to perform specific tasks using machine learning algorithms.

[0305] The "user attribute" is profile information created based on the individual characteristics and learning histories of each user.

[0306] "Visual and audio guidance" is a means of providing information to users through visual guides and audio instructions to support learning.

[0307] The "learning progress" is the degree of progress indicating how much a user has deepened their understanding of specific technologies and knowledge.

[0308] The present invention provides an effective education support system in factories and specific facilities. This system is composed of means for a data supply device, an artificial intelligence model, creation of user attributes, and visual and audio guidance.

[0309] The server collects information from the data supply device via a network. The collected information is supplied to a generative AI model trained using Python. Thereby, an artificial intelligence model based on a machine learning algorithm is formed. For data processing, the NLTK library is used as a text mining tool to enable natural language processing.

[0310] Users access the system via their devices and request information tailored to their learning needs. During this process, the user's progress is tracked, and personalized educational programs are generated based on user attributes. Questions entered by the user are sent from the server to an execution environment used by an artificial intelligence model.

[0311] The terminal provides users with generated content through a visual interface and audio guidance. Specifically, it visualizes data obtained via GraphQL using a GUI toolkit and provides voice guidance using speech synthesis technology built into the robot.

[0312] In the feedback process, user feedback is stored in data storage, and the server uses this data to improve the system. MySQL is used for database management, which helps in the accumulation and analysis of feedback.

[0313] As a concrete example, when a user asks, "What are the procedures for routine robot maintenance?", the system immediately provides a graphical maintenance procedure and voice guidance. An example of a prompt here is, "Please tell me the routine robot maintenance procedure described in the company's business documents." In this way, an efficient learning environment can be created to support the user's skill improvement.

[0314] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0315] Step 1:

[0316] The server collects information from data supply devices. This information includes factory technical documents and procedures, which are stored in a database. Natural language processing tools are used to clean and classify the data, receiving it as input and obtaining processed text data as output.

[0317] Step 2:

[0318] The server trains a generative AI model using the collected data. Specifically, it uses machine learning algorithms to build information classification and prediction models. The input is processed text data, and the output is a trained AI model. The generative AI model is optimized using Python and the NLTK library.

[0319] Step 3:

[0320] Users input questions into the system via their terminal to receive a personalized learning program based on their user attributes. For example, the user uses a "prompt" to request specific information. This input allows the AI ​​model to generate the optimal answer. It receives user questions as input and generates information as output.

[0321] Step 4:

[0322] The server generates answers to user questions. Using an AI model, it derives appropriate answers to input questions and sends them to the terminal. It executes data queries using the GraphQL protocol to retrieve the necessary information.

[0323] Step 5:

[0324] The terminal presents information received from the server to the user visually and audibly. Specifically, it visualizes the information using a GUI toolkit and converts text to speech using speech synthesis technology. It visualizes and voices AI-generated information as input and provides it to the user as output.

[0325] Step 6:

[0326] Users provide feedback on the information provided. This feedback is sent from the terminal to the server to help improve the system. User feedback is received as input, and performance improvement data is obtained as output.

[0327] Step 7:

[0328] The server stores user feedback in a database and improves system performance through analysis. MySQL is used to manage data storage. The accumulated feedback is used as a guideline for system updates.

[0329] 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.

[0330] The system of this invention is built as an information provision platform that combines an emotion engine. This system can analyze the user's emotions in real time and adaptively adjust the content and format of the information provided. The system configuration and specific program processing are described below.

[0331] Server-based processing of business data and training of AI models

[0332] The server retrieves corporate business documents from data storage and uses them to train an artificial intelligence model. This model has the ability to integrate information within the documents and generate appropriate responses to user input. The trained model responds quickly and accurately to a variety of user questions.

[0333] User interface and emotion engine functionality

[0334] When a user uses a device and enters questions or requests for information regarding their learning needs, the device, equipped with an emotion engine, analyzes the user's emotional state. For example, it extracts emotional data from facial expressions and tone of voice using the camera and microphone. The emotion engine analyzes this data to infer whether the user is tense or relaxed, for instance.

[0335] The process of generating and providing information

[0336] The server receives user input and emotional data, and uses a trained AI model to generate optimal information. It adjusts the tone and format of the information provided based on the user's emotional state. For example, if the user is feeling anxious, it provides reassuring language and simple, step-by-step guidance. It also utilizes visual content and voice assistant functions to support user understanding.

[0337] Feedback and system improvements

[0338] Users can provide feedback on the information provided via their device. This feedback is collected on the server and used to further improve system training and enhance the user experience. For example, if a new employee is learning the procedures for a complex project, the system will present information considering their level of understanding and emotional state. This makes the information easier for the user to accept and improves their ability to perform their job.

[0339] This embodiment of the present invention integrates emotion recognition technology to enable the provision of information that is tailored to the user's psychological state, thereby enhancing the ability to provide individualized support compared to conventional educational systems. As a result, companies can achieve effective human resource development for diverse employees.

[0340] The following describes the processing flow.

[0341] Step 1:

[0342] The server retrieves business documents from the company's data storage. This process includes the latest business manuals and policy documents. The server formats these documents for analysis and stores them in a database.

[0343] Step 2:

[0344] The server trains an artificial intelligence model based on business documents. The AI ​​model utilizes natural language processing technology to enhance its ability to generate answers to user questions based on the information in the documents.

[0345] Step 3:

[0346] The user uses a device to access the educational support system. They input specific questions or learning needs, such as "I would like to learn how to use the new project management tool."

[0347] Step 4:

[0348] A device equipped with an emotion engine collects emotional data using a camera and microphone when the user is making input. It analyzes the user's emotional state from their facial expressions and voice tone to determine whether they are relaxed or stressed.

[0349] Step 5:

[0350] The device sends user input and emotional data to the server. This includes digital data related to the questions asked and the emotional state.

[0351] Step 6:

[0352] Based on the data received by the server, an artificial intelligence model is used to generate optimal information. The generated information is adjusted in tone and expression according to the user's emotional state. For example, if the user is showing anxiety, a polite and easy-to-understand guide is created to provide reassurance.

[0353] Step 7:

[0354] The server sends generated information and recommended visual or audio content to the device. The device then presents this information to the user to support their learning.

[0355] Step 8:

[0356] The user reviews the information provided and uses it in their work. Further questions and feedback are provided as needed.

[0357] Step 9:

[0358] The device sends user feedback to the server. The server uses this feedback to improve system performance and adjust its response so that the next response is even better.

[0359] (Example 2)

[0360] 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".

[0361] In modern business, personalized information acquisition and learning are crucial challenges. However, there are still few systems that can provide dynamic information tailored to the emotional state and individual needs of users. Conventional systems fail to consider the psychological barriers of users, limiting the effectiveness of information provision. Solving this problem is the objective of this invention.

[0362] 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.

[0363] In this invention, the server includes means for collecting business information from a data storage device and training an intelligent model, means for managing learning progress based on individual user information, and means for analyzing emotional data and adjusting information. This makes it possible to provide appropriate information in accordance with the user's emotions and to provide a more effective learning experience.

[0364] A "data storage device" is hardware or a system that can store digital information and retrieve it as needed.

[0365] "Business information" refers to all information, including document data and electronic records, that a company or organization generates or uses in the course of its daily operations.

[0366] An "intelligent model" is an artificial intelligence algorithm or system that learns patterns and knowledge from data, imitates human intellectual work, and performs specific tasks.

[0367] "User information" refers to data that includes attributes, behavioral history, and preferences of individual users, and is a component that forms a unique profile of that person.

[0368] "Emotional data" refers to data that quantifies or categorizes a user's emotional state, and includes information obtained from facial expressions, tone of voice, behavior, etc.

[0369] "Information adjustment" is the process of optimizing the content and format of information provided according to the user's needs and circumstances.

[0370] An "effective learning experience" is a learning process optimized to help users easily understand, remember, and appropriately utilize information.

[0371] The system of the present invention is an advanced platform designed to dynamically provide digital information. This system is implemented using data storage devices, intelligent models, and terminal components.

[0372] The server efficiently retrieves business information from data storage devices. This includes document data from companies and organizations, and involves the server extracting this data as needed via database queries and preparing it as training datasets for intelligent models.

[0373] The intelligent model is trained based on acquired business information. Natural language processing techniques are used to learn patterns and knowledge within the information, and the model is adjusted to generate appropriate responses to user input. Specific resource examples include machine learning libraries and cloud-based computing resources.

[0374] The terminal receives direct input from the user and captures questions and information requests through the interface. Similarly, emotional data is analyzed in real time using the camera and microphone. This emotional data analysis uses state-of-the-art emotion recognition software, which analyzes the user's facial expressions and tone of voice.

[0375] The server integrates text input and emotional data received from the terminal and generates information using a generated AI model. During this process, information adjustment is performed, changing the tone and format of the information to match the user's emotional state. For example, if the user is feeling anxious, the server will provide information in a reassuring tone.

[0376] For example, when a user asks a question with a prompt such as, "Please explain the basic setup procedure for the new project management tool. Please make it easy for a beginner," the system generates a visual guide and simple step-by-step instructions, which are immediately provided through the terminal. Furthermore, when user feedback is entered into the terminal, that information is sent to the server and used as data to improve the system's performance.

[0377] Thus, the present invention is a system that provides users with an excellent information experience through integrated processing by data storage, artificial intelligence models, and terminals.

[0378] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0379] Step 1:

[0380] The server retrieves business information from data storage devices. Specifically, the server issues SQL queries to the database to select and extract document data for companies and organizations. The input is database connection information, and the output is document data as business information. This output data is then formatted for use in training subsequent AI models.

[0381] Step 2:

[0382] The server uses acquired business information to train an intelligent model. The intelligent model is an algorithm that employs natural language processing techniques. The server vectorizes document data as input and feeds it into the model using machine learning libraries. The output is a trained model with tuned parameters. This model enables the generation of adaptive responses to each user's inquiry.

[0383] Step 3:

[0384] The terminal receives input from the user through an interface. The user inputs questions and information requests in natural language. The input consists of text-based prompts, as well as sentiment data acquired in real time using the camera and microphone. The output consists of raw text data and sentiment analysis results.

[0385] Step 4:

[0386] The server integrates text input and sentiment data received from the terminal and generates information using a generated AI model. Specifically, the server provides prompt sentences to the model and adjusts the output based on the sentiment data. The input consists of text data and sentiment data, and the output is response information adapted to the user's emotional state.

[0387] Step 5:

[0388] The terminal displays information sent from the server to the user. Specifically, it presents information using text, graphics, and a voice assistant. The input is the response information from the server, and the output is the information provided to the user in visual and audio formats.

[0389] Step 6:

[0390] Users input feedback on information via their terminals. This feedback includes suggestions for improvement and comments on accuracy. The input is captured through a feedback form, and the output is feedback data sent to the server. The server analyzes this feedback and uses it to improve system performance.

[0391] (Application Example 2)

[0392] 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."

[0393] Modern information delivery systems present information without considering the user's emotional state, making it difficult for users to accept the information and resulting in insufficient individualized support, particularly in caregiving settings. Current systems are especially inadequate in situations where appropriate responses and information provision based on emotions are required. Therefore, there is an urgent need to develop a system that responds to users' emotions, provides a sense of security, and delivers necessary information.

[0394] 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.

[0395] In this invention, the server includes means for collecting business documents from a data storage device and training an intelligent algorithm based on said business documents; means for creating individual user information and managing the learning progress of each user based on said user information; means for receiving input from users and generating information corresponding to said input using an intelligent algorithm; and means for analyzing the emotional state of users and adjusting the tone and format of the information generated based on the analysis results. This makes it possible to provide information that is in line with the emotional state of users, and in nursing care settings, it becomes possible to provide necessary information effectively while giving users a sense of security.

[0396] A "data storage device" is a physical or virtual device used to collect and store data such as business documents and user information.

[0397] "Business documents" are official or informal documents created and used within an organization or company that contain information related to the performance of business.

[0398] An "intelligent algorithm" is a computational method that has the ability to learn from collected data and generate and integrate information to perform a specific task.

[0399] "User information" refers to data about individual users, including information that indicates their learning progress and characteristics.

[0400] "Learning progress" refers to the process of tracking and evaluating the user's acquisition of knowledge and skills over time.

[0401] "Input" refers to data provided by users, including needs and questions that form the basis for information generation.

[0402] "Emotional state" refers to a state that indicates a user's psychological response and emotional changes, and is analyzed through facial expressions, tone of voice, and other factors.

[0403] "Analysis results" refer to information obtained by analyzing the acquired data and clarifying its content and patterns.

[0404] "Tone and format" refers to the style and method of presentation used when information is presented to the user, and is adjusted to aid in understanding the content.

[0405] The system implementing this invention consists primarily of a smart device worn by the user and a server operating in the backend. The server collects business documents from data storage devices, trains an intelligent algorithm based on these documents, and generates appropriate information in response to user input. By using smart glasses or a device equipped with a display, users can check the necessary information in real time. At this time, an emotion analysis engine analyzes the user's emotional state and adjusts the tone and format of the information based on the analysis results, enabling the provision of more personalized services.

[0406] The hardware used includes devices such as smart glasses and tablets, which are equipped with cameras and microphones. For software, Microsoft Azure's Face API or equivalent technology is used for emotion analysis, and TensorFlow or PyTorch are used for training intelligent algorithms. Data is processed in a cloud environment, and the generated information is presented to the user via a digital display device.

[0407] For example, using smart glasses equipped with emotion analysis capabilities in a care setting allows for real-time analysis of the care recipient's emotional state, enabling the provision of appropriate care information to caregivers. Generative AI models can also be used to provide rapid and effective information even in situations requiring individualized attention.

[0408] An example of a prompt is, "If a dementia patient is feeling anxious, please tell me how to reassure them." Using this prompt, a generative AI model is utilized to generate and present the most appropriate response.

[0409] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0410] Step 1:

[0411] The server retrieves business documents from data storage. It receives business document data as input and processes it into a format usable as training data for intelligent algorithms. This processed data provides the foundation for learning by the intelligent algorithms.

[0412] Step 2:

[0413] The server uses an intelligent algorithm to train a model based on business documents. The data processed in step 1 is used as input to train the model. This results in the output of a trained model capable of quickly responding to user input.

[0414] Step 3:

[0415] The terminal receives input data from the user. This input includes questions and information requests. This input data is sent to the server, which requests the generation of appropriate information.

[0416] Step 4:

[0417] The server generates corresponding information using a trained intelligent algorithm based on user input data. The data processing involved is information retrieval based on the input and integration of the results. The output provides a detailed answer to the user's question.

[0418] Step 5:

[0419] The terminal displays information output from the server to the user. The display format is either text or visual content, specifically including on-screen display or audio presentation.

[0420] Step 6:

[0421] The device's emotion analysis engine analyzes the user's emotional state in real time. Using data collected through the camera and microphone as input, it analyzes the user's facial expressions and voice tone, and sends the analysis results to the server.

[0422] Step 7:

[0423] The server receives the results of sentiment analysis and adjusts the tone and format of the output information based on them. Data processing includes reformatting the information according to the analysis results. This generates information optimized for the user.

[0424] Step 8:

[0425] User feedback is collected through the device. This feedback data is sent to a server and used to improve the system. This feedback will also be used as training data for future algorithms.

[0426] Step 9:

[0427] The server analyzes the collected feedback and further improves the model. Based on the challenges indicated by the feedback, the intelligent algorithm is adjusted. As a result, a system capable of more accurate responses is completed.

[0428] Through the steps described above, a system is built that enables the provision of appropriate, emotion-based information to users.

[0429] 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.

[0430] 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.

[0431] 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.

[0432] [Third Embodiment]

[0433] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0434] 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.

[0435] 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).

[0436] 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.

[0437] 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.

[0438] 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).

[0439] 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.

[0440] 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.

[0441] 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.

[0442] 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.

[0443] 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.

[0444] 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".

[0445] This invention is implemented as an employee training support system using artificial intelligence. This system provides individually optimized training content to each employee based on business documents held by the company. The system's program processing and specific examples are shown below.

[0446] The server collects business documents and trains the AI ​​model.

[0447] The server retrieves various business documents and internal regulations provided by the company from data storage. Using this retrieved data, it trains an artificial intelligence model to build an algorithm for generating answers to questions related to business knowledge. The accurate functioning of this model ensures a foundation that allows users to easily obtain the information they need.

[0448] Users access the educational support system through their devices.

[0449] Users access the system using terminals with company-specific applications installed. For example, if a user wants to learn how to use new software, they enter a question into their terminal. This question is sent to the server, and relevant information is generated through an AI model.

[0450] The device presents information to the user.

[0451] The terminal displays information received from the server to the user. For example, it may present a step-by-step guide showing the installation procedure for new software as visual diagrams and text on the terminal. It is also possible to provide voice guidance to instruct the user on the installation procedure.

[0452] Gathering and analyzing feedback

[0453] Users can provide feedback on the information provided via their device. This feedback is collected on the server to improve system performance and enhance the user experience.

[0454] For example, if a new employee asks a question about company policy, the server uses an AI model to refer to the latest policy documentation and generate an appropriate answer. The terminal then provides the answer to the employee and receives feedback on whether the employee understood it. This process enables consistent information delivery and education tailored to individual learning needs. Overall, this system streamlines corporate talent development and enables the creation of highly skilled and immediately productive employees.

[0455] The following describes the processing flow.

[0456] Step 1:

[0457] The server retrieves business documents from data storage. These include the latest manuals, company regulations, and business-related materials. The server prepares these documents as a training set for an artificial intelligence model.

[0458] Step 2:

[0459] The AI ​​model is trained based on business documents acquired by the server. The model utilizes natural language processing technology to improve its ability to understand information within documents and generate answers to user questions.

[0460] Step 3:

[0461] The user operates the device and enters questions related to their learning needs. For example, they might ask questions about specific work procedures, such as "I'd like to know how to log calls with clients."

[0462] Step 4:

[0463] The device sends user input to the server. The server receives this input, analyzes the relevant information using an AI model, and generates a response.

[0464] Step 5:

[0465] The server sends the generated response to the device. The response includes detailed instructions and visual content as needed. The device displays this to the user.

[0466] Step 6:

[0467] Users perform their tasks by referring to information displayed on their devices. If the information is helpful, users can provide feedback on their devices.

[0468] Step 7:

[0469] The server collects user feedback and analyzes it to improve system response accuracy and user experience. The collected feedback is used to improve subsequent models and create new training datasets.

[0470] (Example 1)

[0471] 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."

[0472] In today's business environment, there is a need for effective ways to provide employees with diverse business knowledge and skills and to promote their growth. However, conventional training systems struggle to flexibly respond to each employee's learning progress and individual needs, and it is difficult to guarantee consistent learning outcomes. Furthermore, traditional systems have limited information presentation, which fails to enhance the level of understanding by users.

[0473] 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.

[0474] In this invention, the server includes means for collecting documents from a data storage device and training a machine learning model based on said documents; means for creating individual user profiles and managing each user's learning progress based on said user profiles; and means for receiving input from users and generating information corresponding to said input using the machine learning model. This makes it possible to provide each employee with appropriately individualized educational content and to enhance user comprehension through the presentation of visual and auditory supplementary information.

[0475] A "data storage device" is a general term for storage media and systems that can store information and retrieve it as needed.

[0476] A "document" refers to a collection of written text or data recorded for a specific purpose or content, and is an information resource that includes both paper and electronic media.

[0477] A "machine learning model" refers to a system of algorithms that learn patterns and rules from data to make predictions and decisions.

[0478] A "user profile" is a dataset that compiles information about the attributes and behaviors of a specific user, with the aim of providing services tailored to individual needs.

[0479] "Visual information" refers to forms of information perceived through the eyes, such as shapes, images, and videos.

[0480] "Auditory information" refers to information provided through sound, specifically sound data transmitted through speech synthesis or recording.

[0481] "Response" refers to the feedback or reply that users give to the information or service presented, and it reflects their experience and satisfaction level.

[0482] This system provides individually optimized educational content based on business documents held by companies. An embodiment of this system is shown below.

[0483] The server collects various documents from data storage devices. This collected data mainly includes business-related documents and internal regulations. Using this data, the server trains generative AI models using machine learning frameworks, such as TensorFlow and PyTorch. This model builds algorithms to generate answers to questions based on different business knowledge. This training provides a foundation for users to easily obtain the information they need.

[0484] Users access this educational support system on a device with a dedicated application installed. For example, if a user needs information on how to use new software, they input the question as a prompt on their device. A specific example of such a prompt might be, "Please tell me how to use the new software." This prompt is sent from the device to the server, where an AI model generates the relevant information, and the device receives the result.

[0485] The device uses information transmitted from the server to present visual or audio information to the user. This process includes displaying visual guides and text, or using a voice assistant to deliver information verbally. Examples include illustrating the installation procedure for new software with diagrams or providing an audio explanation of the installation process. This improves user comprehension and maximizes the effectiveness of educational content.

[0486] Furthermore, users can input feedback on the information they receive into their devices. This feedback is sent to the server and used to improve the entire system. Through the analysis of this feedback, it becomes possible to pursue improvements in the performance of the AI ​​model and the user experience.

[0487] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0488] Step 1:

[0489] The server collects necessary documents from the data storage device. Specifically, the server accesses the database and downloads business-related documents and internal regulations within the company. The input is a database query, and the output is a collection of documents as raw data. This dataset will form the basis for AI training in the next stage.

[0490] Step 2:

[0491] The server trains a machine learning model using the collected documents. Specifically, the server utilizes machine learning frameworks such as TensorFlow and PyTorch to tokenize the document data and feed it to the model. The input is the document data collected in the previous step, and the output is the trained generative AI model. This process enables the model to answer questions about specific business knowledge.

[0492] Step 3:

[0493] Users access the educational support system via a terminal. For example, a user might input a prompt message such as "Please tell me how to use the new software." The input is the prompt message the user enters into the terminal, and the output is the data of the entered prompt message. This information forms the basis for the next request to the server.

[0494] Step 4:

[0495] The server analyzes the prompt text received from the user and generates corresponding information using a generative AI model. The server inputs the prompt text into the AI ​​model and generates an answer based on the relevant knowledge. The input is the prompt text data received from the user, and the output is the answer data generated by the AI ​​model.

[0496] Step 5:

[0497] The device receives response data from the server and presents it to the user. Specifically, it displays text and diagrams on the UI as visual information, or reads out instructions using a voice assistant function as audio information. The input is the response data received from the server, and the output is the information presented to the user visually or audibly.

[0498] Step 6:

[0499] Users provide feedback on the information provided by entering it into their device. Specifically, they evaluate the quality and usefulness of the information using rating buttons and comment fields on the UI. The input is the feedback information entered by the user, and the output is that feedback data.

[0500] Step 7:

[0501] The server collects user feedback and uses it to improve the overall system performance. Specifically, it analyzes the feedback data to adjust the accuracy of the AI ​​model and reflects this in future response generation. The input is the feedback data collected from users, and the output is the improved system performance based on that feedback.

[0502] (Application Example 1)

[0503] 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."

[0504] The present invention aims to provide knowledge about the technologies used in specific equipment quickly and efficiently, thereby promoting the improvement of users' skills. In particular, it aims to solve the problem of systems that have means of providing accurate information about procedures and maintenance methods visually and audibly.

[0505] 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.

[0506] In this invention, the server includes means for collecting information from a data supply device and training an artificial intelligence model based on said information; means for creating individual user attributes and managing each user's learning progress based on said user attributes; and means for providing visual and audio instruction for specific equipment. This enables users to improve their skills quickly and efficiently.

[0507] A "data supply device" is a device used to collect and provide information.

[0508] An "artificial intelligence model" is a computer program designed to perform a specific task using machine learning algorithms.

[0509] "User attributes" refer to profile information created based on each user's individual characteristics and learning history.

[0510] "Visual and audio instruction" refers to a means of providing information to users and supporting their learning through visual guides and audio instructions.

[0511] "Learning progress" refers to the degree of progress a user has made in deepening their understanding of a particular technology or knowledge.

[0512] This invention provides an effective educational support system for factories and specific facilities. The system consists of a data supply device, an artificial intelligence model, user attribute creation, and visual and audio instruction methods.

[0513] The server collects information from data supply devices via the network. The collected information is fed into a generative AI model trained using Python. This forms an artificial intelligence model based on machine learning algorithms. For data processing, the NLTK library is used as a text mining tool, enabling natural language processing.

[0514] Users access the system via their devices and request information tailored to their learning needs. During this process, the user's progress is tracked, and personalized educational programs are generated based on user attributes. Questions entered by the user are sent from the server to an execution environment used by an artificial intelligence model.

[0515] The terminal provides users with generated content through a visual interface and audio guidance. Specifically, it visualizes data obtained via GraphQL using a GUI toolkit and provides voice guidance using speech synthesis technology built into the robot.

[0516] In the feedback process, user feedback is stored in data storage, and the server uses this data to improve the system. MySQL is used for database management, which helps in the accumulation and analysis of feedback.

[0517] As a concrete example, when a user asks, "What are the procedures for routine robot maintenance?", the system immediately provides a graphical maintenance procedure and voice guidance. An example of a prompt here is, "Please tell me the routine robot maintenance procedure described in the company's business documents." In this way, an efficient learning environment can be created to support the user's skill improvement.

[0518] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0519] Step 1:

[0520] The server collects information from data supply devices. This information includes factory technical documents and procedures, which are stored in a database. Natural language processing tools are used to clean and classify the data, receiving it as input and obtaining processed text data as output.

[0521] Step 2:

[0522] The server trains a generative AI model using the collected data. Specifically, it uses machine learning algorithms to build information classification and prediction models. The input is processed text data, and the output is a trained AI model. The generative AI model is optimized using Python and the NLTK library.

[0523] Step 3:

[0524] Users input questions into the system via their terminal to receive a personalized learning program based on their user attributes. For example, the user uses a "prompt" to request specific information. This input allows the AI ​​model to generate the optimal answer. It receives user questions as input and generates information as output.

[0525] Step 4:

[0526] The server generates answers to user questions. Using an AI model, it derives appropriate answers to input questions and sends them to the terminal. It executes data queries using the GraphQL protocol to retrieve the necessary information.

[0527] Step 5:

[0528] The terminal presents information received from the server to the user visually and audibly. Specifically, it visualizes the information using a GUI toolkit and converts text to speech using speech synthesis technology. It visualizes and voices AI-generated information as input and provides it to the user as output.

[0529] Step 6:

[0530] Users provide feedback on the information provided. This feedback is sent from the terminal to the server to help improve the system. User feedback is received as input, and performance improvement data is obtained as output.

[0531] Step 7:

[0532] The server stores user feedback in a database and improves system performance through analysis. MySQL is used to manage data storage. The accumulated feedback is used as a guideline for system updates.

[0533] 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.

[0534] The system of this invention is built as an information provision platform that combines an emotion engine. This system can analyze the user's emotions in real time and adaptively adjust the content and format of the information provided. The system configuration and specific program processing are described below.

[0535] Server-based processing of business data and training of AI models

[0536] The server retrieves corporate business documents from data storage and uses them to train an artificial intelligence model. This model has the ability to integrate information within the documents and generate appropriate responses to user input. The trained model responds quickly and accurately to a variety of user questions.

[0537] User interface and emotion engine functionality

[0538] When a user uses a device and enters questions or requests for information regarding their learning needs, the device, equipped with an emotion engine, analyzes the user's emotional state. For example, it extracts emotional data from facial expressions and tone of voice using the camera and microphone. The emotion engine analyzes this data to infer whether the user is tense or relaxed, for instance.

[0539] The process of generating and providing information

[0540] The server receives user input and emotional data, and uses a trained AI model to generate optimal information. It adjusts the tone and format of the information provided based on the user's emotional state. For example, if the user is feeling anxious, it provides reassuring language and simple, step-by-step guidance. It also utilizes visual content and voice assistant functions to support user understanding.

[0541] Feedback and system improvements

[0542] Users can provide feedback on the information provided via their device. This feedback is collected on the server and used to further improve system training and enhance the user experience. For example, if a new employee is learning the procedures for a complex project, the system will present information considering their level of understanding and emotional state. This makes the information easier for the user to accept and improves their ability to perform their job.

[0543] This embodiment of the present invention integrates emotion recognition technology to enable the provision of information that is tailored to the user's psychological state, thereby enhancing the ability to provide individualized support compared to conventional educational systems. As a result, companies can achieve effective human resource development for diverse employees.

[0544] The following describes the processing flow.

[0545] Step 1:

[0546] The server retrieves business documents from the company's data storage. This process includes the latest business manuals and policy documents. The server formats these documents for analysis and stores them in a database.

[0547] Step 2:

[0548] The server trains an artificial intelligence model based on business documents. The AI ​​model utilizes natural language processing technology to enhance its ability to generate answers to user questions based on the information in the documents.

[0549] Step 3:

[0550] The user uses a device to access the educational support system. They input specific questions or learning needs, such as "I would like to learn how to use the new project management tool."

[0551] Step 4:

[0552] A device equipped with an emotion engine collects emotional data using a camera and microphone when the user is making input. It analyzes the user's emotional state from their facial expressions and voice tone to determine whether they are relaxed or stressed.

[0553] Step 5:

[0554] The device sends user input and emotional data to the server. This includes digital data related to the questions asked and the emotional state.

[0555] Step 6:

[0556] Based on the data received by the server, an artificial intelligence model is used to generate optimal information. The generated information is adjusted in tone and expression according to the user's emotional state. For example, if the user is showing anxiety, a polite and easy-to-understand guide is created to provide reassurance.

[0557] Step 7:

[0558] The server sends generated information and recommended visual or audio content to the device. The device then presents this information to the user to support their learning.

[0559] Step 8:

[0560] The user reviews the information provided and uses it in their work. Further questions and feedback are provided as needed.

[0561] Step 9:

[0562] The device sends user feedback to the server. The server uses this feedback to improve system performance and adjust its response so that the next response is even better.

[0563] (Example 2)

[0564] 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."

[0565] In modern business, personalized information acquisition and learning are crucial challenges. However, there are still few systems that can provide dynamic information tailored to the emotional state and individual needs of users. Conventional systems fail to consider the psychological barriers of users, limiting the effectiveness of information provision. Solving this problem is the objective of this invention.

[0566] 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.

[0567] In this invention, the server includes means for collecting business information from a data storage device and training an intelligent model, means for managing learning progress based on individual user information, and means for analyzing emotional data and adjusting information. This makes it possible to provide appropriate information in accordance with the user's emotions and to provide a more effective learning experience.

[0568] A "data storage device" is hardware or a system that can store digital information and retrieve it as needed.

[0569] "Business information" refers to all information, including document data and electronic records, that a company or organization generates or uses in the course of its daily operations.

[0570] An "intelligent model" is an artificial intelligence algorithm or system that learns patterns and knowledge from data, imitates human intellectual work, and performs specific tasks.

[0571] "User information" refers to data that includes attributes, behavioral history, and preferences of individual users, and is a component that forms a unique profile of that person.

[0572] "Emotional data" refers to data that quantifies or categorizes a user's emotional state, and includes information obtained from facial expressions, tone of voice, behavior, etc.

[0573] "Information adjustment" is the process of optimizing the content and format of information provided according to the user's needs and circumstances.

[0574] An "effective learning experience" is a learning process optimized to help users easily understand, remember, and appropriately utilize information.

[0575] The system of the present invention is an advanced platform designed to dynamically provide digital information. This system is implemented using data storage devices, intelligent models, and terminal components.

[0576] The server efficiently retrieves business information from data storage devices. This includes document data from companies and organizations, and involves the server extracting this data as needed via database queries and preparing it as training datasets for intelligent models.

[0577] The intelligent model is trained based on acquired business information. Natural language processing techniques are used to learn patterns and knowledge within the information, and the model is adjusted to generate appropriate responses to user input. Specific resource examples include machine learning libraries and cloud-based computing resources.

[0578] The terminal receives direct input from the user and captures questions and information requests through the interface. Similarly, emotional data is analyzed in real time using the camera and microphone. This emotional data analysis uses state-of-the-art emotion recognition software, which analyzes the user's facial expressions and tone of voice.

[0579] The server integrates text input and emotional data received from the terminal and generates information using a generated AI model. During this process, information adjustment is performed, changing the tone and format of the information to match the user's emotional state. For example, if the user is feeling anxious, the server will provide information in a reassuring tone.

[0580] For example, when a user asks a question with a prompt such as, "Please explain the basic setup procedure for the new project management tool. Please make it easy for a beginner," the system generates a visual guide and simple step-by-step instructions, which are immediately provided through the terminal. Furthermore, when user feedback is entered into the terminal, that information is sent to the server and used as data to improve the system's performance.

[0581] Thus, the present invention is a system that provides users with an excellent information experience through integrated processing by data storage, artificial intelligence models, and terminals.

[0582] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0583] Step 1:

[0584] The server retrieves business information from data storage devices. Specifically, the server issues SQL queries to the database to select and extract document data for companies and organizations. The input is database connection information, and the output is document data as business information. This output data is then formatted for use in training subsequent AI models.

[0585] Step 2:

[0586] The server uses acquired business information to train an intelligent model. The intelligent model is an algorithm that employs natural language processing techniques. The server vectorizes document data as input and feeds it into the model using machine learning libraries. The output is a trained model with tuned parameters. This model enables the generation of adaptive responses to each user's inquiry.

[0587] Step 3:

[0588] The terminal receives input from the user through an interface. The user inputs questions and information requests in natural language. The input consists of text-based prompts, as well as sentiment data acquired in real time using the camera and microphone. The output consists of raw text data and sentiment analysis results.

[0589] Step 4:

[0590] The server integrates text input and sentiment data received from the terminal and generates information using a generated AI model. Specifically, the server provides prompt sentences to the model and adjusts the output based on the sentiment data. The input consists of text data and sentiment data, and the output is response information adapted to the user's emotional state.

[0591] Step 5:

[0592] The terminal displays information sent from the server to the user. Specifically, it presents information using text, graphics, and a voice assistant. The input is the response information from the server, and the output is the information provided to the user in visual and audio formats.

[0593] Step 6:

[0594] Users input feedback on information via their terminals. This feedback includes suggestions for improvement and comments on accuracy. The input is captured through a feedback form, and the output is feedback data sent to the server. The server analyzes this feedback and uses it to improve system performance.

[0595] (Application Example 2)

[0596] 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."

[0597] Modern information delivery systems present information without considering the user's emotional state, making it difficult for users to accept the information and resulting in insufficient individualized support, particularly in caregiving settings. Current systems are especially inadequate in situations where appropriate responses and information provision based on emotions are required. Therefore, there is an urgent need to develop a system that responds to users' emotions, provides a sense of security, and delivers necessary information.

[0598] 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.

[0599] In this invention, the server includes means for collecting business documents from a data storage device and training an intelligent algorithm based on said business documents; means for creating individual user information and managing the learning progress of each user based on said user information; means for receiving input from users and generating information corresponding to said input using an intelligent algorithm; and means for analyzing the emotional state of users and adjusting the tone and format of the information generated based on the analysis results. This makes it possible to provide information that is in line with the emotional state of users, and in nursing care settings, it becomes possible to provide necessary information effectively while giving users a sense of security.

[0600] A "data storage device" is a physical or virtual device used to collect and store data such as business documents and user information.

[0601] "Business documents" are official or informal documents created and used within an organization or company that contain information related to the performance of business.

[0602] An "intelligent algorithm" is a computational method that has the ability to learn from collected data and generate and integrate information to perform a specific task.

[0603] "User information" refers to data about individual users, including information that indicates their learning progress and characteristics.

[0604] "Learning progress" refers to the process of tracking and evaluating the user's acquisition of knowledge and skills over time.

[0605] "Input" refers to data provided by users, including needs and questions that form the basis for information generation.

[0606] "Emotional state" refers to a state that indicates a user's psychological response and emotional changes, and is analyzed through facial expressions, tone of voice, and other factors.

[0607] "Analysis results" refer to information obtained by analyzing the acquired data and clarifying its content and patterns.

[0608] "Tone and format" refers to the style and method of presentation used when information is presented to the user, and is adjusted to aid in understanding the content.

[0609] The system implementing this invention consists primarily of a smart device worn by the user and a server operating in the backend. The server collects business documents from data storage devices, trains an intelligent algorithm based on these documents, and generates appropriate information in response to user input. By using smart glasses or a device equipped with a display, users can check the necessary information in real time. At this time, an emotion analysis engine analyzes the user's emotional state and adjusts the tone and format of the information based on the analysis results, enabling the provision of more personalized services.

[0610] The hardware used includes devices such as smart glasses and tablets, which are equipped with cameras and microphones. For software, Microsoft Azure's Face API or equivalent technology is used for emotion analysis, and TensorFlow or PyTorch are used for training intelligent algorithms. Data is processed in a cloud environment, and the generated information is presented to the user via a digital display device.

[0611] For example, using smart glasses equipped with emotion analysis capabilities in a care setting allows for real-time analysis of the care recipient's emotional state, enabling the provision of appropriate care information to caregivers. Generative AI models can also be used to provide rapid and effective information even in situations requiring individualized attention.

[0612] An example of a prompt is, "If a dementia patient is feeling anxious, please tell me how to reassure them." Using this prompt, a generative AI model is utilized to generate and present the most appropriate response.

[0613] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0614] Step 1:

[0615] The server retrieves business documents from data storage. It receives business document data as input and processes it into a format usable as training data for intelligent algorithms. This processed data provides the foundation for learning by the intelligent algorithms.

[0616] Step 2:

[0617] The server uses an intelligent algorithm to train a model based on business documents. The data processed in step 1 is used as input to train the model. This results in the output of a trained model capable of quickly responding to user input.

[0618] Step 3:

[0619] The terminal receives input data from the user. This input includes questions and information requests. This input data is sent to the server, which requests the generation of appropriate information.

[0620] Step 4:

[0621] The server generates corresponding information using a trained intelligent algorithm based on user input data. The data processing involved is information retrieval based on the input and integration of the results. The output provides a detailed answer to the user's question.

[0622] Step 5:

[0623] The terminal displays information output from the server to the user. The display format is either text or visual content, specifically including on-screen display or audio presentation.

[0624] Step 6:

[0625] The device's emotion analysis engine analyzes the user's emotional state in real time. Using data collected through the camera and microphone as input, it analyzes the user's facial expressions and voice tone, and sends the analysis results to the server.

[0626] Step 7:

[0627] The server receives the results of sentiment analysis and adjusts the tone and format of the output information based on them. Data processing includes reformatting the information according to the analysis results. This generates information optimized for the user.

[0628] Step 8:

[0629] User feedback is collected through the device. This feedback data is sent to a server and used to improve the system. This feedback will also be used as training data for future algorithms.

[0630] Step 9:

[0631] The server analyzes the collected feedback and further improves the model. Based on the challenges indicated by the feedback, the intelligent algorithm is adjusted. As a result, a system capable of more accurate responses is completed.

[0632] Through the steps described above, a system is built that enables the provision of appropriate, emotion-based information to users.

[0633] 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.

[0634] 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.

[0635] 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.

[0636] [Fourth Embodiment]

[0637] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0638] 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.

[0639] 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).

[0640] 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.

[0641] 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.

[0642] 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).

[0643] 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.

[0644] 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.

[0645] 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.

[0646] 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.

[0647] 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.

[0648] 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.

[0649] 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".

[0650] This invention is implemented as an employee training support system using artificial intelligence. This system provides individually optimized training content to each employee based on business documents held by the company. The system's program processing and specific examples are shown below.

[0651] The server collects business documents and trains the AI ​​model.

[0652] The server retrieves various business documents and internal regulations provided by the company from data storage. Using this retrieved data, it trains an artificial intelligence model to build an algorithm for generating answers to questions related to business knowledge. The accurate functioning of this model ensures a foundation that allows users to easily obtain the information they need.

[0653] Users access the educational support system through their devices.

[0654] Users access the system using terminals with company-specific applications installed. For example, if a user wants to learn how to use new software, they enter a question into their terminal. This question is sent to the server, and relevant information is generated through an AI model.

[0655] The device presents information to the user.

[0656] The terminal displays information received from the server to the user. For example, it may present a step-by-step guide showing the installation procedure for new software as visual diagrams and text on the terminal. It is also possible to provide voice guidance to instruct the user on the installation procedure.

[0657] Gathering and analyzing feedback

[0658] Users can provide feedback on the information provided via their device. This feedback is collected on the server to improve system performance and enhance the user experience.

[0659] For example, if a new employee asks a question about company policy, the server uses an AI model to refer to the latest policy documentation and generate an appropriate answer. The terminal then provides the answer to the employee and receives feedback on whether the employee understood it. This process enables consistent information delivery and education tailored to individual learning needs. Overall, this system streamlines corporate talent development and enables the creation of highly skilled and immediately productive employees.

[0660] The following describes the processing flow.

[0661] Step 1:

[0662] The server retrieves business documents from data storage. These include the latest manuals, company regulations, and business-related materials. The server prepares these documents as a training set for an artificial intelligence model.

[0663] Step 2:

[0664] The AI ​​model is trained based on business documents acquired by the server. The model utilizes natural language processing technology to improve its ability to understand information within documents and generate answers to user questions.

[0665] Step 3:

[0666] The user operates the device and enters questions related to their learning needs. For example, they might ask questions about specific work procedures, such as "I'd like to know how to log calls with clients."

[0667] Step 4:

[0668] The device sends user input to the server. The server receives this input, analyzes the relevant information using an AI model, and generates a response.

[0669] Step 5:

[0670] The server sends the generated response to the device. The response includes detailed instructions and visual content as needed. The device displays this to the user.

[0671] Step 6:

[0672] Users perform their tasks by referring to information displayed on their devices. If the information is helpful, users can provide feedback on their devices.

[0673] Step 7:

[0674] The server collects user feedback and analyzes it to improve system response accuracy and user experience. The collected feedback is used to improve subsequent models and create new training datasets.

[0675] (Example 1)

[0676] 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".

[0677] In today's business environment, there is a need for effective ways to provide employees with diverse business knowledge and skills and to promote their growth. However, conventional training systems struggle to flexibly respond to each employee's learning progress and individual needs, and it is difficult to guarantee consistent learning outcomes. Furthermore, traditional systems have limited information presentation, which fails to enhance the level of understanding by users.

[0678] 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.

[0679] In this invention, the server includes means for collecting documents from a data storage device and training a machine learning model based on said documents; means for creating individual user profiles and managing each user's learning progress based on said user profiles; and means for receiving input from users and generating information corresponding to said input using the machine learning model. This makes it possible to provide each employee with appropriately individualized educational content and to enhance user comprehension through the presentation of visual and auditory supplementary information.

[0680] A "data storage device" is a general term for storage media and systems that can store information and retrieve it as needed.

[0681] A "document" refers to a collection of written text or data recorded for a specific purpose or content, and is an information resource that includes both paper and electronic media.

[0682] A "machine learning model" refers to a system of algorithms that learn patterns and rules from data to make predictions and decisions.

[0683] A "user profile" is a dataset that compiles information about the attributes and behaviors of a specific user, with the aim of providing services tailored to individual needs.

[0684] "Visual information" refers to forms of information perceived through the eyes, such as shapes, images, and videos.

[0685] "Auditory information" refers to information provided through sound, specifically sound data transmitted through speech synthesis or recording.

[0686] "Response" refers to the feedback or reply that users give to the information or service presented, and it reflects their experience and satisfaction level.

[0687] This system provides individually optimized educational content based on business documents held by companies. An embodiment of this system is shown below.

[0688] The server collects various documents from data storage devices. This collected data mainly includes business-related documents and internal regulations. Using this data, the server trains generative AI models using machine learning frameworks, such as TensorFlow and PyTorch. This model builds algorithms to generate answers to questions based on different business knowledge. This training provides a foundation for users to easily obtain the information they need.

[0689] Users access this educational support system on a device with a dedicated application installed. For example, if a user needs information on how to use new software, they input the question as a prompt on their device. A specific example of such a prompt might be, "Please tell me how to use the new software." This prompt is sent from the device to the server, where an AI model generates the relevant information, and the device receives the result.

[0690] The device uses information transmitted from the server to present visual or audio information to the user. This process includes displaying visual guides and text, or using a voice assistant to deliver information verbally. Examples include illustrating the installation procedure for new software with diagrams or providing an audio explanation of the installation process. This improves user comprehension and maximizes the effectiveness of educational content.

[0691] Furthermore, users can input feedback on the information they receive into their devices. This feedback is sent to the server and used to improve the entire system. Through the analysis of this feedback, it becomes possible to pursue improvements in the performance of the AI ​​model and the user experience.

[0692] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0693] Step 1:

[0694] The server collects necessary documents from the data storage device. Specifically, the server accesses the database and downloads business-related documents and internal regulations within the company. The input is a database query, and the output is a collection of documents as raw data. This dataset will form the basis for AI training in the next stage.

[0695] Step 2:

[0696] The server trains a machine learning model using the collected documents. Specifically, the server utilizes machine learning frameworks such as TensorFlow and PyTorch to tokenize the document data and feed it to the model. The input is the document data collected in the previous step, and the output is the trained generative AI model. This process enables the model to answer questions about specific business knowledge.

[0697] Step 3:

[0698] Users access the educational support system via a terminal. For example, a user might input a prompt message such as "Please tell me how to use the new software." The input is the prompt message the user enters into the terminal, and the output is the data of the entered prompt message. This information forms the basis for the next request to the server.

[0699] Step 4:

[0700] The server analyzes the prompt text received from the user and generates corresponding information using a generative AI model. The server inputs the prompt text into the AI ​​model and generates an answer based on the relevant knowledge. The input is the prompt text data received from the user, and the output is the answer data generated by the AI ​​model.

[0701] Step 5:

[0702] The device receives response data from the server and presents it to the user. Specifically, it displays text and diagrams on the UI as visual information, or reads out instructions using a voice assistant function as audio information. The input is the response data received from the server, and the output is the information presented to the user visually or audibly.

[0703] Step 6:

[0704] Users provide feedback on the information provided by entering it into their device. Specifically, they evaluate the quality and usefulness of the information using rating buttons and comment fields on the UI. The input is the feedback information entered by the user, and the output is that feedback data.

[0705] Step 7:

[0706] The server collects user feedback and uses it to improve the overall system performance. Specifically, it analyzes the feedback data to adjust the accuracy of the AI ​​model and reflects this in future response generation. The input is the feedback data collected from users, and the output is the improved system performance based on that feedback.

[0707] (Application Example 1)

[0708] 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".

[0709] The present invention aims to provide knowledge about the technologies used in specific equipment quickly and efficiently, thereby promoting the improvement of users' skills. In particular, it aims to solve the problem of systems that have means of providing accurate information about procedures and maintenance methods visually and audibly.

[0710] 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.

[0711] In this invention, the server includes means for collecting information from a data supply device and training an artificial intelligence model based on said information; means for creating individual user attributes and managing each user's learning progress based on said user attributes; and means for providing visual and audio instruction for specific equipment. This enables users to improve their skills quickly and efficiently.

[0712] A "data supply device" is a device used to collect and provide information.

[0713] An "artificial intelligence model" is a computer program designed to perform a specific task using machine learning algorithms.

[0714] "User attributes" refer to profile information created based on each user's individual characteristics and learning history.

[0715] "Visual and audio instruction" refers to a means of providing information to users and supporting their learning through visual guides and audio instructions.

[0716] "Learning progress" refers to the degree of progress a user has made in deepening their understanding of a particular technology or knowledge.

[0717] This invention provides an effective educational support system for factories and specific facilities. The system consists of a data supply device, an artificial intelligence model, user attribute creation, and visual and audio instruction methods.

[0718] The server collects information from data supply devices via the network. The collected information is fed into a generative AI model trained using Python. This forms an artificial intelligence model based on machine learning algorithms. For data processing, the NLTK library is used as a text mining tool, enabling natural language processing.

[0719] Users access the system via their devices and request information tailored to their learning needs. During this process, the user's progress is tracked, and personalized educational programs are generated based on user attributes. Questions entered by the user are sent from the server to an execution environment used by an artificial intelligence model.

[0720] The terminal provides users with generated content through a visual interface and audio guidance. Specifically, it visualizes data obtained via GraphQL using a GUI toolkit and provides voice guidance using speech synthesis technology built into the robot.

[0721] In the feedback process, user feedback is stored in data storage, and the server uses this data to improve the system. MySQL is used for database management, which helps in the accumulation and analysis of feedback.

[0722] As a concrete example, when a user asks, "What are the procedures for routine robot maintenance?", the system immediately provides a graphical maintenance procedure and voice guidance. An example of a prompt here is, "Please tell me the routine robot maintenance procedure described in the company's business documents." In this way, an efficient learning environment can be created to support the user's skill improvement.

[0723] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0724] Step 1:

[0725] The server collects information from data supply devices. This information includes factory technical documents and procedures, which are stored in a database. Natural language processing tools are used to clean and classify the data, receiving it as input and obtaining processed text data as output.

[0726] Step 2:

[0727] The server trains a generative AI model using the collected data. Specifically, it uses machine learning algorithms to build information classification and prediction models. The input is processed text data, and the output is a trained AI model. The generative AI model is optimized using Python and the NLTK library.

[0728] Step 3:

[0729] Users input questions into the system via their terminal to receive a personalized learning program based on their user attributes. For example, the user uses a "prompt" to request specific information. This input allows the AI ​​model to generate the optimal answer. It receives user questions as input and generates information as output.

[0730] Step 4:

[0731] The server generates answers to user questions. Using an AI model, it derives appropriate answers to input questions and sends them to the terminal. It executes data queries using the GraphQL protocol to retrieve the necessary information.

[0732] Step 5:

[0733] The terminal presents information received from the server to the user visually and audibly. Specifically, it visualizes the information using a GUI toolkit and converts text to speech using speech synthesis technology. It visualizes and voices AI-generated information as input and provides it to the user as output.

[0734] Step 6:

[0735] Users provide feedback on the information provided. This feedback is sent from the terminal to the server to help improve the system. User feedback is received as input, and performance improvement data is obtained as output.

[0736] Step 7:

[0737] The server stores user feedback in a database and improves system performance through analysis. MySQL is used to manage data storage. The accumulated feedback is used as a guideline for system updates.

[0738] 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.

[0739] The system of this invention is built as an information provision platform that combines an emotion engine. This system can analyze the user's emotions in real time and adaptively adjust the content and format of the information provided. The system configuration and specific program processing are described below.

[0740] Server-based processing of business data and training of AI models

[0741] The server retrieves corporate business documents from data storage and uses them to train an artificial intelligence model. This model has the ability to integrate information within the documents and generate appropriate responses to user input. The trained model responds quickly and accurately to a variety of user questions.

[0742] User interface and emotion engine functionality

[0743] When a user uses a device and enters questions or requests for information regarding their learning needs, the device, equipped with an emotion engine, analyzes the user's emotional state. For example, it extracts emotional data from facial expressions and tone of voice using the camera and microphone. The emotion engine analyzes this data to infer whether the user is tense or relaxed, for instance.

[0744] The process of generating and providing information

[0745] The server receives user input and emotional data, and uses a trained AI model to generate optimal information. It adjusts the tone and format of the information provided based on the user's emotional state. For example, if the user is feeling anxious, it provides reassuring language and simple, step-by-step guidance. It also utilizes visual content and voice assistant functions to support user understanding.

[0746] Feedback and system improvements

[0747] Users can provide feedback on the information provided via their device. This feedback is collected on the server and used to further improve system training and enhance the user experience. For example, if a new employee is learning the procedures for a complex project, the system will present information considering their level of understanding and emotional state. This makes the information easier for the user to accept and improves their ability to perform their job.

[0748] This embodiment of the present invention integrates emotion recognition technology to enable the provision of information that is tailored to the user's psychological state, thereby enhancing the ability to provide individualized support compared to conventional educational systems. As a result, companies can achieve effective human resource development for diverse employees.

[0749] The following describes the processing flow.

[0750] Step 1:

[0751] The server retrieves business documents from the company's data storage. This process includes the latest business manuals and policy documents. The server formats these documents for analysis and stores them in a database.

[0752] Step 2:

[0753] The server trains an artificial intelligence model based on business documents. The AI ​​model utilizes natural language processing technology to enhance its ability to generate answers to user questions based on the information in the documents.

[0754] Step 3:

[0755] The user uses a device to access the educational support system. They input specific questions or learning needs, such as "I would like to learn how to use the new project management tool."

[0756] Step 4:

[0757] A device equipped with an emotion engine collects emotional data using a camera and microphone when the user is making input. It analyzes the user's emotional state from their facial expressions and voice tone to determine whether they are relaxed or stressed.

[0758] Step 5:

[0759] The device sends user input and emotional data to the server. This includes digital data related to the questions asked and the emotional state.

[0760] Step 6:

[0761] Based on the data received by the server, an artificial intelligence model is used to generate optimal information. The generated information is adjusted in tone and expression according to the user's emotional state. For example, if the user is showing anxiety, a polite and easy-to-understand guide is created to provide reassurance.

[0762] Step 7:

[0763] The server sends generated information and recommended visual or audio content to the device. The device then presents this information to the user to support their learning.

[0764] Step 8:

[0765] The user reviews the information provided and uses it in their work. Further questions and feedback are provided as needed.

[0766] Step 9:

[0767] The device sends user feedback to the server. The server uses this feedback to improve system performance and adjust its response so that the next response is even better.

[0768] (Example 2)

[0769] 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".

[0770] In modern business, personalized information acquisition and learning are crucial challenges. However, there are still few systems that can provide dynamic information tailored to the emotional state and individual needs of users. Conventional systems fail to consider the psychological barriers of users, limiting the effectiveness of information provision. Solving this problem is the objective of this invention.

[0771] 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.

[0772] In this invention, the server includes means for collecting business information from a data storage device and training an intelligent model, means for managing learning progress based on individual user information, and means for analyzing emotional data and adjusting information. This makes it possible to provide appropriate information in accordance with the user's emotions and to provide a more effective learning experience.

[0773] A "data storage device" is hardware or a system that can store digital information and retrieve it as needed.

[0774] "Business information" refers to all information, including document data and electronic records, that a company or organization generates or uses in the course of its daily operations.

[0775] An "intelligent model" is an artificial intelligence algorithm or system that learns patterns and knowledge from data, imitates human intellectual work, and performs specific tasks.

[0776] "User information" refers to data that includes attributes, behavioral history, and preferences of individual users, and is a component that forms a unique profile of that person.

[0777] "Emotional data" refers to data that quantifies or categorizes a user's emotional state, and includes information obtained from facial expressions, tone of voice, behavior, etc.

[0778] "Information adjustment" is the process of optimizing the content and format of information provided according to the user's needs and circumstances.

[0779] An "effective learning experience" is a learning process optimized to help users easily understand, remember, and appropriately utilize information.

[0780] The system of the present invention is an advanced platform designed to dynamically provide digital information. This system is implemented using data storage devices, intelligent models, and terminal components.

[0781] The server efficiently retrieves business information from data storage devices. This includes document data from companies and organizations, and involves the server extracting this data as needed via database queries and preparing it as training datasets for intelligent models.

[0782] The intelligent model is trained based on acquired business information. Natural language processing techniques are used to learn patterns and knowledge within the information, and the model is adjusted to generate appropriate responses to user input. Specific resource examples include machine learning libraries and cloud-based computing resources.

[0783] The terminal receives direct input from the user and captures questions and information requests through the interface. Similarly, emotional data is analyzed in real time using the camera and microphone. This emotional data analysis uses state-of-the-art emotion recognition software, which analyzes the user's facial expressions and tone of voice.

[0784] The server integrates text input and emotional data received from the terminal and generates information using a generated AI model. During this process, information adjustment is performed, changing the tone and format of the information to match the user's emotional state. For example, if the user is feeling anxious, the server will provide information in a reassuring tone.

[0785] For example, when a user asks a question with a prompt such as, "Please explain the basic setup procedure for the new project management tool. Please make it easy for a beginner," the system generates a visual guide and simple step-by-step instructions, which are immediately provided through the terminal. Furthermore, when user feedback is entered into the terminal, that information is sent to the server and used as data to improve the system's performance.

[0786] Thus, the present invention is a system that provides users with an excellent information experience through integrated processing by data storage, artificial intelligence models, and terminals.

[0787] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0788] Step 1:

[0789] The server retrieves business information from data storage devices. Specifically, the server issues SQL queries to the database to select and extract document data for companies and organizations. The input is database connection information, and the output is document data as business information. This output data is then formatted for use in training subsequent AI models.

[0790] Step 2:

[0791] The server uses acquired business information to train an intelligent model. The intelligent model is an algorithm that employs natural language processing techniques. The server vectorizes document data as input and feeds it into the model using machine learning libraries. The output is a trained model with tuned parameters. This model enables the generation of adaptive responses to each user's inquiry.

[0792] Step 3:

[0793] The terminal receives input from the user through an interface. The user inputs questions and information requests in natural language. The input consists of text-based prompts, as well as sentiment data acquired in real time using the camera and microphone. The output consists of raw text data and sentiment analysis results.

[0794] Step 4:

[0795] The server integrates text input and sentiment data received from the terminal and generates information using a generated AI model. Specifically, the server provides prompt sentences to the model and adjusts the output based on the sentiment data. The input consists of text data and sentiment data, and the output is response information adapted to the user's emotional state.

[0796] Step 5:

[0797] The terminal displays information sent from the server to the user. Specifically, it presents information using text, graphics, and a voice assistant. The input is the response information from the server, and the output is the information provided to the user in visual and audio formats.

[0798] Step 6:

[0799] Users input feedback on information via their terminals. This feedback includes suggestions for improvement and comments on accuracy. The input is captured through a feedback form, and the output is feedback data sent to the server. The server analyzes this feedback and uses it to improve system performance.

[0800] (Application Example 2)

[0801] 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".

[0802] Modern information delivery systems present information without considering the user's emotional state, making it difficult for users to accept the information and resulting in insufficient individualized support, particularly in caregiving settings. Current systems are especially inadequate in situations where appropriate responses and information provision based on emotions are required. Therefore, there is an urgent need to develop a system that responds to users' emotions, provides a sense of security, and delivers necessary information.

[0803] 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.

[0804] In this invention, the server includes means for collecting business documents from a data storage device and training an intelligent algorithm based on said business documents; means for creating individual user information and managing the learning progress of each user based on said user information; means for receiving input from users and generating information corresponding to said input using an intelligent algorithm; and means for analyzing the emotional state of users and adjusting the tone and format of the information generated based on the analysis results. This makes it possible to provide information that is in line with the emotional state of users, and in nursing care settings, it becomes possible to provide necessary information effectively while giving users a sense of security.

[0805] A "data storage device" is a physical or virtual device used to collect and store data such as business documents and user information.

[0806] "Business documents" are official or informal documents created and used within an organization or company that contain information related to the performance of business.

[0807] An "intelligent algorithm" is a computational method that has the ability to learn from collected data and generate and integrate information to perform a specific task.

[0808] "User information" refers to data about individual users, including information that indicates their learning progress and characteristics.

[0809] "Learning progress" refers to the process of tracking and evaluating the user's acquisition of knowledge and skills over time.

[0810] "Input" refers to data provided by users, including needs and questions that form the basis for information generation.

[0811] "Emotional state" refers to a state that indicates a user's psychological response and emotional changes, and is analyzed through facial expressions, tone of voice, and other factors.

[0812] "Analysis results" refer to information obtained by analyzing the acquired data and clarifying its content and patterns.

[0813] "Tone and format" refers to the style and method of presentation used when information is presented to the user, and is adjusted to aid in understanding the content.

[0814] The system implementing this invention consists primarily of a smart device worn by the user and a server operating in the backend. The server collects business documents from data storage devices, trains an intelligent algorithm based on these documents, and generates appropriate information in response to user input. By using smart glasses or a device equipped with a display, users can check the necessary information in real time. At this time, an emotion analysis engine analyzes the user's emotional state and adjusts the tone and format of the information based on the analysis results, enabling the provision of more personalized services.

[0815] The hardware used includes devices such as smart glasses and tablets, which are equipped with cameras and microphones. For software, Microsoft Azure's Face API or equivalent technology is used for emotion analysis, and TensorFlow or PyTorch are used for training intelligent algorithms. Data is processed in a cloud environment, and the generated information is presented to the user via a digital display device.

[0816] For example, using smart glasses equipped with emotion analysis capabilities in a care setting allows for real-time analysis of the care recipient's emotional state, enabling the provision of appropriate care information to caregivers. Generative AI models can also be used to provide rapid and effective information even in situations requiring individualized attention.

[0817] An example of a prompt is, "If a dementia patient is feeling anxious, please tell me how to reassure them." Using this prompt, a generative AI model is utilized to generate and present the most appropriate response.

[0818] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0819] Step 1:

[0820] The server retrieves business documents from data storage. It receives business document data as input and processes it into a format usable as training data for intelligent algorithms. This processed data provides the foundation for learning by the intelligent algorithms.

[0821] Step 2:

[0822] The server uses an intelligent algorithm to train a model based on business documents. The data processed in step 1 is used as input to train the model. This results in the output of a trained model capable of quickly responding to user input.

[0823] Step 3:

[0824] The terminal receives input data from the user. This input includes questions and information requests. This input data is sent to the server, which requests the generation of appropriate information.

[0825] Step 4:

[0826] The server generates corresponding information using a trained intelligent algorithm based on user input data. The data processing involved is information retrieval based on the input and integration of the results. The output provides a detailed answer to the user's question.

[0827] Step 5:

[0828] The terminal displays information output from the server to the user. The display format is either text or visual content, specifically including on-screen display or audio presentation.

[0829] Step 6:

[0830] The device's emotion analysis engine analyzes the user's emotional state in real time. Using data collected through the camera and microphone as input, it analyzes the user's facial expressions and voice tone, and sends the analysis results to the server.

[0831] Step 7:

[0832] The server receives the results of sentiment analysis and adjusts the tone and format of the output information based on them. Data processing includes reformatting the information according to the analysis results. This generates information optimized for the user.

[0833] Step 8:

[0834] User feedback is collected through the device. This feedback data is sent to a server and used to improve the system. This feedback will also be used as training data for future algorithms.

[0835] Step 9:

[0836] The server analyzes the collected feedback and further improves the model. Based on the challenges indicated by the feedback, the intelligent algorithm is adjusted. As a result, a system capable of more accurate responses is completed.

[0837] Through the steps described above, a system is built that enables the provision of appropriate, emotion-based information to users.

[0838] 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.

[0839] 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.

[0840] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0841] 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.

[0842] 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.

[0843] 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.

[0844] 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.

[0845] 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.

[0846] 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."

[0847] 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.

[0848] 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.

[0849] 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.

[0850] 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.

[0851] 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.

[0852] 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.

[0853] 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.

[0854] 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.

[0855] 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.

[0856] 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.

[0857] 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.

[0858] 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 as being incorporated by reference.

[0859] The following is further disclosed regarding the embodiments described above.

[0860] (Claim 1)

[0861] A means for collecting business documents from data storage and training an artificial intelligence model based on said business documents,

[0862] A means for creating individual user profiles and managing each user's learning progress based on those user profiles,

[0863] A means for receiving user input and generating information corresponding to that input using an artificial intelligence model,

[0864] A means for receiving the generated information and displaying it to the user,

[0865] A means for collecting feedback from the user and using that feedback to improve the system's performance,

[0866] A system that includes this.

[0867] (Claim 2)

[0868] The system according to claim 1, further comprising means for tracking the user's learning progress data and generating a progress report based on said data.

[0869] (Claim 3)

[0870] The system according to claim 1, further comprising means for providing the user with visual or audio content related to the generated information and for assisting the user's understanding.

[0871] "Example 1"

[0872] (Claim 1)

[0873] A means for collecting documents from a data storage device and training a machine learning model based on said documents,

[0874] A means for creating individual user profiles and managing each user's learning progress based on those user profiles,

[0875] A means for receiving input from a user and generating information corresponding to that input using a machine learning model,

[0876] A means of receiving the generated information and presenting it to the user,

[0877] A means for collecting feedback from the user and using that feedback to improve the system's performance,

[0878] A means by which a device presents visual or audio information to the user as an aid,

[0879] A system that includes this.

[0880] (Claim 2)

[0881] The system according to claim 1, further comprising means for tracking the user's learning management data and generating progress reports based on said data.

[0882] (Claim 3)

[0883] The system according to claim 1, further comprising means for performing feedback analysis in response to the user's reaction and reflecting it in system improvements.

[0884] "Application Example 1"

[0885] (Claim 1)

[0886] A means for collecting information from a data supply device and training an artificial intelligence model based on said information,

[0887] A means for creating individual user attributes and managing each user's learning progress based on those user attributes,

[0888] A means for receiving input from a user and generating information corresponding to that input using an artificial intelligence model,

[0889] A means for receiving the generated information and displaying it to the user,

[0890] A means of collecting feedback from the user and using that feedback to improve the system's performance,

[0891] Means for providing visual and audio instruction for specific equipment,

[0892] A system that includes this.

[0893] (Claim 2)

[0894] The system according to claim 1, further comprising means for tracking the user's learning progress data and generating progress reports based on said data.

[0895] (Claim 3)

[0896] The system according to claim 1, further comprising means for providing the user with visual or audio guidance related to the generated information and for supporting the user's understanding.

[0897] "Example 2 of combining an emotion engine"

[0898] (Claim 1)

[0899] A means for collecting business information from a data storage device and training an intelligent model based on said business information,

[0900] A means for creating individual user information and managing each user's learning progress based on said user information,

[0901] A means for analyzing user emotional data via a terminal and adjusting the information provided based on said emotional data,

[0902] A means for receiving input from a user and generating information corresponding to said input and emotional data using an intelligent model,

[0903] A means for receiving the generated information and displaying it to the user,

[0904] A means of collecting evaluations from the user and using those evaluations to improve the performance of the system,

[0905] A system that includes this.

[0906] (Claim 2)

[0907] The system according to claim 1, further comprising means for tracking the user's learning status and emotional state data and generating progress reports based on said data.

[0908] (Claim 3)

[0909] The system according to claim 1, further comprising means for providing the user with visual or audio media related to the generated information and for supporting the user's understanding.

[0910] "Application example 2 when combining with an emotional engine"

[0911] (Claim 1)

[0912] A means for collecting business documents from a data storage device and training an intelligent algorithm based on said business documents,

[0913] A means for creating individual user information and managing the learning progress of each user based on said user information,

[0914] A means for receiving input from a user and generating information corresponding to that input using an intelligent algorithm,

[0915] A means for receiving the generated information and displaying it to the user,

[0916] A means for collecting feedback from the user and using that feedback to improve the system's performance,

[0917] A means for analyzing the emotional state of the user and adjusting the tone and format of the information generated based on the analysis results,

[0918] A system that includes this.

[0919] (Claim 2)

[0920] The system according to claim 1, further comprising means for tracking the user's learning progress data and generating progress reports based on said data.

[0921] (Claim 3)

[0922] The system according to claim 1, further comprising means for providing the user with visual or audio content related to the generated information and for assisting the user's understanding. [Explanation of symbols]

[0923] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for collecting information from a data supply device and training an artificial intelligence model based on said information, A means for creating individual user attributes and managing each user's learning progress based on those user attributes, A means for receiving input from a user and generating information corresponding to that input using an artificial intelligence model, A means for receiving the generated information and displaying it to the user, A means of collecting feedback from the user and using that feedback to improve the system's performance, Means for providing visual and audio instruction for specific equipment, A system that includes this.

2. The system according to claim 1, further comprising means for tracking the user's learning progress data and generating progress reports based on said data.

3. The system according to claim 1, further comprising means for providing the user with visual or audio guidance related to the generated information and for supporting the user's understanding.

Images