system

Abstract

We provide the system. [Solution] A means to enable users to create and participate in learning communities, A means of automatically responding to user questions using generative artificial intelligence models specialized in each field, A means for displaying the responses of generative artificial intelligence and user-submitted information on the user interface, 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 steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In conventional learning and research activities, high costs and long-term investments are required for acquiring specialized knowledge. Also, educational equality is not ensured, and educational disparities occur due to geographical and resource constraints. Furthermore, there is a problem that it is difficult to provide a place where users can freely share knowledge and learn from each other.

Means for Solving the Problems

[0005] This invention provides means for enabling users to easily create and participate in learning communities, and means for automatically responding quickly and accurately to user questions using generative artificial intelligence models specialized in each field. Furthermore, by providing means for visually displaying the responses of the generative artificial intelligence and user-submitted information on a user interface, it provides low-cost, high-quality education and expands learning opportunities broadly and equally.

[0006] A "user" is an entity that utilizes the system, and is an individual or group that participates in the learning community or asks questions.

[0007] A "learning community" is an online gathering created for the purpose of users sharing knowledge with each other, and can be a forum or group specializing in a particular academic field.

[0008] A "generative artificial intelligence model" is a model that uses natural language processing technology to automatically generate responses to user questions, and is an algorithm that provides general or specialized information.

[0009] "Automatic response" refers to a function in which a generative artificial intelligence model instantly generates an appropriate answer to a user's question based on prior data and learning.

[0010] A "user interface" refers to the visual or operational elements that a user uses to interact with a system, including screens and components for inputting and displaying information. [Brief explanation of the drawing]

[0011] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3]This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0012] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0013] First, let's explain the terminology used in the following explanation.

[0014] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0015] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0016] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0017] In the following embodiments, the numbered communication I / F (Interface) is an interface that includes a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

[0018] 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."

[0019] [First Embodiment]

[0020] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0021] 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.

[0022] 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).

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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.

[0027] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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".

[0032] This invention provides a system that allows users to easily create and participate in learning communities and obtain specialized knowledge from generative artificial intelligence models. Specific embodiments thereof are described below.

[0033] First, the server prepares generative artificial intelligence models tailored to each area of ​​expertise. These AI models are trained on vast datasets and possess the ability to provide accurate responses to a wide range of user questions. The server also provides a function that allows users to create new online learning communities. Activity history and posts within these communities are managed by the server and stored in a database.

[0034] The terminal provides the primary means for users to access this system. A user-friendly interface allows users to directly input questions into the generative artificial intelligence and easily search for and join learning communities of interest. The answers provided by the generative AI are displayed on the terminal in a highly visible format.

[0035] For example, if a user wants to learn more about the theory of evolution in biology, they input their question into the device. A generative artificial intelligence model on the server analyzes relevant academic information and generates a detailed explanation of evolution in real time. The device then clearly displays the answer on the user's screen to support their understanding.

[0036] Furthermore, users can deepen their knowledge by exchanging opinions and engaging in discussions with other participants based on the information they obtain. The server also manages these discussions and helps ensure that all users have fair access to the information. In this way, the present invention can realize an online environment that widely disseminates high-quality education and provides diverse learning opportunities.

[0037] The following describes the processing flow.

[0038] Step 1:

[0039] The server initializes generative artificial intelligence models corresponding to each specialized field. This includes the process of loading relevant datasets and appropriately training the AI ​​models.

[0040] Step 2:

[0041] Users access the system's user interface via a terminal and create an account. User information is sent to the server and stored in a database.

[0042] Step 3:

[0043] Users create communities based on academic fields or topics that interest them. The server registers community information and updates the database so that other users can join.

[0044] Step 4:

[0045] The user enters a question for a generative artificial intelligence model on their device. The entered question is sent to a server and passed to the appropriate AI model.

[0046] Step 5:

[0047] A generative artificial intelligence model on the server analyzes the received question, searches for relevant knowledge, and generates an answer. The generated answer is sent to the terminal via the server for the user to return.

[0048] Step 6:

[0049] The device displays the responses obtained from the generative artificial intelligence model in an easy-to-understand format for the user. The user can read this and proceed with their learning.

[0050] Step 7:

[0051] Users share the information they obtain and their own insights within the community. This stimulates discussion and exchange of opinions among other users.

[0052] Step 8:

[0053] The server monitors activity within the community and records updates in a database in real time. It also collects user activity history to help provide a more personalized learning experience.

[0054] (Example 1)

[0055] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0056] In today's educational environment, there is a lack of opportunities to efficiently acquire and share specialized knowledge with others. Furthermore, there is a demand for updating specialized knowledge in each field based on ever-changing information. Therefore, an efficient system is needed that allows users to easily create and participate in learning communities and resolve specialized questions.

[0057] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0058] In this invention, the server includes a device that enables learners to create and join learning groups, a device that automatically responds to learners' questions using generative intelligence models specialized in each field, and a device that displays the generative intelligence responses and learner submissions on a display unit. This allows users to access a wide range of high-quality educational and learning opportunities.

[0059] A "learner" refers to an individual or group whose purpose is to access an educational system and acquire or share knowledge.

[0060] A "learning group" refers to an organization or community formed by learners with specific goals or interests, with the purpose of sharing and exchanging knowledge.

[0061] A "generative intelligence model" refers to an artificial intelligence program that is trained on a large dataset and has the ability to automatically generate responses to questions related to specific expertise.

[0062] An "educational data set" refers to a collection of data containing a large amount of knowledge information used to train generative intelligence models.

[0063] The term "memory unit" refers to a device or area used to store information such as a learner's registration information and learning history.

[0064] An "information processing system" refers to a combination of hardware and software configured to collect, process, store, and distribute various types of information.

[0065] "Device" refers to a machine or software component designed to perform a specific function.

[0066] This invention provides an information processing system that utilizes generative intelligence models to enable learners to efficiently acquire specialized knowledge and to form and participate in learning groups. Specific embodiments thereof are described below.

[0067] 1. Model preparation and management

[0068] The server prepares generative intelligence models tailored to each specialized field. These models are trained using large educational data sets and have the ability to generate accurate answers to questions related to specific fields. The server regularly updates the training datasets for these models, improving their accuracy by reflecting the latest information.

[0069] 2. Creating and participating in learning groups

[0070] The terminal provides learners with the primary means of accessing the system. The user-friendly interface on the terminal allows learners to create and join learning groups based on their interests and needs. This enables learners to share knowledge with others who share common interests.

[0071] 3. Generating prompts and responses

[0072] Users input the information they want to know or their questions in the form of prompts through their terminal. For example, they can input specific questions such as, "I want to know more about the theory of evolution in biology." The server then uses a generative intelligence model to generate a detailed and accurate response to the prompt and displays the result on the terminal.

[0073] Specific example

[0074] When a user enters the prompt "Please tell me about the impact of climate change on biodiversity," a generative intelligence model on the server analyzes the latest academic information related to this question and provides an answer in an easy-to-understand format. The terminal displays the generated information in a highly visible format to assist the user in acquiring knowledge.

[0075] This system is designed to facilitate access to specialized knowledge and provide diverse educational opportunities. Through it, learners can deepen their knowledge and grow together with other participants.

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

[0077] Step 1:

[0078] The user enters the information or question they want to know as an input prompt through the interface on the terminal. This input is in text format and may include specific technical questions. For example, they might enter a specific prompt such as, "Please tell me about the impact of climate change on biodiversity."

[0079] Step 2:

[0080] The terminal sends the prompts entered by the user to the server. This transmission is carried out through a secure communication protocol and serves to ensure that the user's input is properly delivered to the server. At this point, the input prompts are ready to be parsed by the server.

[0081] Step 3:

[0082] The server analyzes the received prompt and selects the most suitable generative intelligence model. At this stage, the server searches for relevant data based on the content of the prompt and applies the intelligence model to process the data. This generates an appropriate response to the prompt.

[0083] Step 4:

[0084] A generative intelligence model generates response data to prompts. The model extracts information relevant to the prompts from a vast dataset of educational data and constructs specific answers. This output data is in a format that contains information useful to the user.

[0085] Step 5:

[0086] The server sends the generated response to the terminal. This process is designed to efficiently compress the response data and send it quickly.

[0087] Step 6:

[0088] The terminal displays the received response data in a user-friendly format. This allows the user to easily view and understand the answers to their questions. The display helps the user decide how to use the information for their next steps.

[0089] (Application Example 1)

[0090] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0091] The present invention aims to provide a system that allows users to easily participate in a group of educational programs designed to deepen their specialized knowledge. It also aims to create an environment where users can acquire knowledge quickly and accurately using a generative intelligence model. In particular, it requires the visual presentation of specialized video content and the individualization of the user's learning experience to enable efficient understanding. Furthermore, means to facilitate the exchange of opinions among users and activate knowledge sharing are also important.

[0092] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0093] In this invention, the server includes means for enabling users to form and participate in educational groups, means for automatically responding to user questions using generative intelligence models specialized in each area of ​​expertise, means for outputting the generative intelligence responses and user-submitted information to an information display device, means for providing users with personalized educational information by allowing them to acquire specialized video content in real time using a visual display device, and means for facilitating the exchange of opinions with other users. This enables users to efficiently acquire specialized knowledge and accommodate diverse learning styles.

[0094] An "educational group" is a group intended for users to participate in or form, for the purpose of sharing specific specialized knowledge and information.

[0095] A "generative intelligence model" is an artificial intelligence technology that learns from vast amounts of data and can automatically generate responses to user questions.

[0096] An "information display device" is a device or software used to visually output knowledge and information to a user.

[0097] A "visual display device" is a device that allows users to visually receive video content in real time, enabling the provision of personalized educational information.

[0098] "Exchange of opinions" is a means of communication where users share knowledge and views with each other, thereby gaining new perspectives and understandings.

[0099] This system is designed to enable users to efficiently acquire specialized knowledge. First, the server provides an online platform for users to form and participate in educational groups of interest. This platform utilizes generative intelligence models specialized in each field, enabling them to automatically provide accurate responses to user questions. The generative intelligence models installed on the server perform advanced computational processing based on updated training datasets to appropriately respond to user inquiries.

[0100] Furthermore, the terminal is equipped with an information display device, which can output responses from generative intelligence and various user-submitted information in real time. In addition, through the visual display device, users can receive personalized educational video content and deepen their understanding visually.

[0101] Through this system, users can easily exchange opinions with other users and acquire new perspectives and knowledge. This interaction is an important function for deepening knowledge and bringing about diverse viewpoints. For example, by entering a prompt such as "Please tell me about the technology used to build the pyramids of ancient Egypt," users can obtain detailed information and engage in discussions based on it.

[0102] The entire system operates on devices such as smartphones and head-mounted displays, and generative intelligence is implemented using OpenAI® GPT, among other technologies. This enables an intuitive and effective learning experience for the user.

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

[0104] Step 1:

[0105] Users access educational programs through their devices.

[0106] Input: User's areas of interest and registration information

[0107] Processing: Send user information to the server and execute a database query to search for relevant educational groups.

[0108] Output: A list of educational groups suitable for the user is displayed.

[0109] Step 2:

[0110] The server uses a generative intelligence model to generate a response based on the user's prompt.

[0111] Input: The prompt text entered by the user (e.g., "Please tell me about the technology used to build the pyramids of ancient Egypt.")

[0112] Processing: Analyze the prompt text and perform data calculations to generate relevant information using a generative AI model.

[0113] Output: The generated answer is sent to the device.

[0114] Step 3:

[0115] The terminal outputs generative intelligence responses to the user via an information display device.

[0116] Input: Response from a generative intelligence model

[0117] Processing: Structure the responses and display them in a visually easy-to-understand format on the interface.

[0118] Output: The user can view detailed information on the display device.

[0119] Step 4:

[0120] Users view videos through their devices and receive personalized educational information.

[0121] Input: Video information related to the content selected by the user.

[0122] Processing: Utilize visual display devices to stream or play video in real time.

[0123] Output: Images are presented to the user's vision, facilitating learning.

[0124] Step 5:

[0125] The server provides a platform to facilitate the exchange of opinions among users.

[0126] Input: User comments and opinions

[0127] Processing: Data is transmitted to distribute user-submitted information to other users in real time, providing a platform for discussion.

[0128] Output: Other users' opinions and comments are displayed, facilitating knowledge sharing.

[0129] 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.

[0130] This invention is a system that enhances the effectiveness of generative artificial intelligence models and learning communities by incorporating an emotion engine that recognizes user emotions. To realize this system, the following embodiments will be described.

[0131] The server features generative artificial intelligence models with expertise in various fields, as well as an emotion engine that analyzes user input and dialogue data to recognize emotions. This emotion engine uses natural language processing techniques and machine learning algorithms to determine the emotional state of the user from the text and actions they express.

[0132] The device is designed to allow users to easily engage in emotionally charged interactions. Emotional information recognized by the emotion engine is sent to a generative artificial intelligence model, which generates flexible responses tailored to the user's emotional state. This makes it possible to provide users with a more personalized learning experience.

[0133] For example, if a user inputs into the system, "My recent math assignments have been very difficult and I'm exhausted," the emotion engine analyzes this input and recognizes that the user is feeling tired or frustrated. The generative artificial intelligence model then responds in a gentle tone, such as, "It's okay to take a short break. When you find math problems difficult, try breaking them down into smaller tasks."

[0134] Furthermore, the emotion engine accumulates the user's emotional history and uses this to provide recommendations that more efficiently support their learning approach. The server utilizes this information to facilitate interaction with other users and work to improve the quality of learning across the entire community. It also plays a role in promoting empathetic interaction by visually displaying the emotional information of other participants to the user on their device.

[0135] In this way, the present invention aims to provide an interactive learning environment that goes beyond mere information provision and responds to emotions, thereby realizing a richer educational experience.

[0136] The following describes the processing flow.

[0137] Step 1:

[0138] Users access the learning community platform through their devices and take action to join new or existing communities. A user's participation request is sent to the server.

[0139] Step 2:

[0140] The server approves user participation requests and updates the database to keep community information and participant lists up-to-date. It also reviews the user's past sentiment data to prepare personalized support.

[0141] Step 3:

[0142] The user inputs questions to a generative artificial intelligence model on their device. During this process, the emotion engine monitors the user's language and choices, and analyzes their emotional state.

[0143] Step 4:

[0144] The emotion engine determines the emotional state (e.g., stress, excitement, sadness, etc.) obtained from the user's input and provides that information to a generative artificial intelligence model.

[0145] Step 5:

[0146] Generative artificial intelligence models generate responses tailored to the user's feelings based on emotional information received from an emotion engine. These responses take into account the user's learned data and current mental state.

[0147] Step 6:

[0148] The device displays the generated response to the user. The display format is customized to engage the user's interest and is designed to make it easier for the user to choose their next action based on their emotions.

[0149] Step 7:

[0150] Users can use the information they gain to continue learning and exchange opinions with other users within the community. They can also receive empathetic feedback from other users based on their emotions.

[0151] Step 8:

[0152] The server stores user feedback and communication history, using this data to improve the quality of future interactions. It also analyzes learning and emotional expression trends to help improve the system.

[0153] (Example 2)

[0154] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0155] In conventional learning support systems, information is provided without considering the user's emotional state, resulting in a uniform learning experience and making it difficult to provide optimal support tailored to individual users. Furthermore, the inability to obtain responses that reflect the user's emotions posed challenges in maintaining user motivation and improving learning efficiency.

[0156] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0157] In this invention, the server includes means for enabling users to create and participate in learning groups, means for automatically responding to user questions using generative artificial intelligence models specialized in each field, means for using an emotion engine to analyze user input data and identify emotional states, and means for displaying the responses of the generative artificial intelligence and responses based on the user's emotional state on a user interface. This enables users to receive a personalized learning experience tailored to their emotional state.

[0158] The term "user" refers to an individual or group of people who use a system to engage in learning activities.

[0159] A "learning group" refers to a community organized for educational purposes that users can belong to.

[0160] A "generative artificial intelligence model" refers to artificial intelligence technology that has learned knowledge in various fields in advance and has the ability to automatically answer user questions.

[0161] An "emotion engine" refers to a technology that analyzes user input data to identify their emotional state, utilizing natural language processing and machine learning algorithms.

[0162] "User interface" is a concept that refers to the screens and means of operation that allow a system and a user to interact with each other.

[0163] "Generative artificial intelligence response" refers to the answers or information provided by a generative artificial intelligence model based on user input.

[0164] "Emotional state" refers to a psychological or emotional condition detected based on the user's expressions and behavior.

[0165] A "database" refers to a collection of information that systematically stores and manages collected user registration information, learning history, and other data.

[0166] This invention is a system designed to provide users with a personalized learning experience, primarily achieved through the interplay of three parties: a server, a terminal, and the user, each fulfilling their respective roles.

[0167] The server plays a central role in this invention and is equipped with a generative artificial intelligence model and an emotion engine. The emotion engine uses natural language processing techniques and machine learning algorithms to analyze text data entered by the user and identify their emotional state. This allows the server to generate appropriate responses that take into account the user's psychological situation.

[0168] A terminal is a device used by users to interact with the system. It receives user input through the user interface and transmits that data to the server. It can also present responses from the server to the user visually or audibly.

[0169] Users can use this system to receive support for their learning tasks by inputting information. For example, based on specific input such as "My recent math assignments have been very difficult and I'm exhausted," they can receive responses that address their emotions.

[0170] As a concrete example, when a user inputs "tired" into the terminal, the server's emotion engine analyzes the input and identifies the emotion as "fatigue." In response, the generative artificial intelligence model generates advice such as "You should take a short break," which is then presented to the user through the terminal. Predefined prompt sentences are used to generate this response; for example, a specific prompt sentence such as "What suggestion should be made when a user feels tired?" is used.

[0171] In this way, the present invention realizes flexible and effective learning support that is tailored to the user's emotions.

[0172] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0173] Step 1:

[0174] The terminal receives user input. This input can be in text or voice format; for example, it can receive a message such as, "My recent math assignments were very difficult and I'm exhausted." This input is then sent to the server as digital data.

[0175] Step 2:

[0176] The server passes user input sent from the terminal to the emotion engine. The emotion engine uses natural language processing technology to analyze the received text data. This analysis determines the user's emotional state and identifies emotions such as "fatigue" or "frustration." This emotional information becomes the output of the emotion engine.

[0177] Step 3:

[0178] The server sends the emotional information received from the emotion engine to the generative AI model. The generative AI model receives the emotional information as input and generates responses as instructed by the prompt. For example, the prompt might provide an indicator such as "gentle advice when the user is tired," and based on this information, it generates a response such as "Try taking a short break." This is the output of the generative AI model.

[0179] Step 4:

[0180] The server sends the response obtained from the generated AI model to the terminal. The terminal presents this response to the user visually or audibly, providing the user with an emotionally relevant response. Specifically, this may involve text being displayed on the user's screen or the message being read aloud using speech synthesis.

[0181] Step 5:

[0182] The server further records and stores the user's emotions and response history in a database. This information will be used to personalize future learning experiences. In this process, historical data is processed and saved to the database as new entries.

[0183] (Application Example 2)

[0184] 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".

[0185] In recent years, recognizing user emotions and providing more personalized responses has become increasingly important for making interactions within data processing sets more meaningful. However, conventional technologies lack the ability to generate responses that take into account the user's emotional state, making it difficult to improve engagement within data processing sets. Solving this problem is crucial.

[0186] 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.

[0187] In this invention, the server includes means for enabling users to create and participate in data processing sets, means for automatically responding to user questions using generative intelligence models specialized in each field, means for displaying the generative intelligence responses and user information on the user screen, and means for an emotion mechanism that recognizes diverse emotional states and adjusts responses based on the user's emotional information. This enables flexible responses in accordance with the user's emotions and improves the quality of communication within the data processing set.

[0188] A "data processing set" is a virtual environment where users can participate and exchange information, serving as a platform for sharing knowledge and information from different specialized fields.

[0189] A "generative intelligence model" is a type of artificial intelligence that learns from a specific dataset and has the ability to generate responses to a variety of questions.

[0190] A "user interface" is a visual display that allows users to interact with the system through an interface, and enables the input and output of information.

[0191] An "emotional mechanism" is a technical means of analyzing and determining emotions from user input and behavior, thereby enabling an understanding of the user's emotional state.

[0192] A "recording device" is a database or other storage medium that securely stores a user's personal information and history, and allows them to be retrieved as needed.

[0193] "User verification" is a process that verifies the user's identity and authority, and is a means of ensuring safe and reliable access.

[0194] "Empathic interaction" refers to interactions that promote the sharing and understanding of emotions among users, and is an activity aimed at deepening relationships within a data processing set.

[0195] The system for implementing the present invention mainly consists of three components: a server, a terminal, and a user. The server is equipped with a generative intelligence model and has an emotion mechanism built in to analyze the user's input information and emotional state. The emotion mechanism uses natural language processing technology and machine learning algorithms to process text and behavioral data input by the user and to determine the user's emotional state. This processing uses specialized software such as TENSORFLOW® and NLTK.

[0196] The terminal is a device for users to participate in a data processing set and exchange information through a user interface, and smartphones and tablets are examples of such devices. User interactions are displayed on this interface, and responses generated by generative intelligence models are also displayed visually.

[0197] As a concrete example, if a customer in a physical store says, "I'm having trouble finding a staff member," this statement is captured by the terminal and sent as text data to the server. The emotion mechanism on the server analyzes the statement and detects the customer's confusion and frustration. Based on this emotion recognition, the generative intelligence model immediately provides the terminal with an appropriate response, such as, "A staff member will be with you shortly. Please wait a moment." This allows the customer to receive prompt and accurate service.

[0198] An example of a prompt message input to a generative AI model might be, "The customer seems stressed. What is the best course of action?" This allows the generative intelligence model to provide personalized responses that respond to the user's emotions.

[0199] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0200] Step 1:

[0201] The user initiates voice input into the device. The device uses a speech recognition system to convert the voice data into text data. This text data is sent to the server. The input is voice data, and the output is text data. A service such as Google® Speech-to-Text API is used for speech recognition.

[0202] Step 2:

[0203] The server inputs the received text data into the sentiment mechanism. The sentiment mechanism uses natural language processing algorithms to analyze the sentiment attributes of the text. As a result of the analysis, the user's emotional state (e.g., confused, irritated) is output. This process utilizes NLTK and sentiment analysis libraries.

[0204] Step 3:

[0205] The server inputs the emotional state and original text data into a generative AI model. The generative AI model generates the optimal response based on this data. The content of the response (e.g., "A staff member will be with you shortly. Please wait a moment.") is output. TensorFlow or PyTorch are used to operate the generative AI model.

[0206] Step 4:

[0207] The server sends the generated response to the terminal. The terminal displays the received response to the user visually or audibly. The user decides on the next action based on this response. In this step, the user can verify whether the response is appropriate.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] [Second Embodiment]

[0212] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0213] 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.

[0214] 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).

[0215] 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.

[0216] 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.

[0217] 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).

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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".

[0224] This invention provides a system that allows users to easily create and participate in learning communities and obtain specialized knowledge from generative artificial intelligence models. Specific embodiments thereof are described below.

[0225] First, the server prepares generative artificial intelligence models tailored to each area of ​​expertise. These AI models are trained on vast datasets and possess the ability to provide accurate responses to a wide range of user questions. The server also provides a function that allows users to create new online learning communities. Activity history and posts within these communities are managed by the server and stored in a database.

[0226] The terminal provides the primary means for users to access this system. A user-friendly interface allows users to directly input questions into the generative artificial intelligence and easily search for and join learning communities of interest. The answers provided by the generative AI are displayed on the terminal in a highly visible format.

[0227] For example, if a user wants to learn more about the theory of evolution in biology, they input their question into the device. A generative artificial intelligence model on the server analyzes relevant academic information and generates a detailed explanation of evolution in real time. The device then clearly displays the answer on the user's screen to support their understanding.

[0228] Furthermore, users can deepen their knowledge by exchanging opinions and engaging in discussions with other participants based on the information they obtain. The server also manages these discussions and helps ensure that all users have fair access to the information. In this way, the present invention can realize an online environment that widely disseminates high-quality education and provides diverse learning opportunities.

[0229] The following describes the processing flow.

[0230] Step 1:

[0231] The server initializes generative artificial intelligence models corresponding to each specialized field. This includes the process of loading relevant datasets and appropriately training the AI ​​models.

[0232] Step 2:

[0233] Users access the system's user interface via a terminal and create an account. User information is sent to the server and stored in a database.

[0234] Step 3:

[0235] Users create communities based on academic fields or topics that interest them. The server registers community information and updates the database so that other users can join.

[0236] Step 4:

[0237] The user enters a question for a generative artificial intelligence model on their device. The entered question is sent to a server and passed to the appropriate AI model.

[0238] Step 5:

[0239] A generative artificial intelligence model on the server analyzes the received question, searches for relevant knowledge, and generates an answer. The generated answer is sent to the terminal via the server for the user to return.

[0240] Step 6:

[0241] The device displays the responses obtained from the generative artificial intelligence model in an easy-to-understand format for the user. The user can read this and proceed with their learning.

[0242] Step 7:

[0243] Users share the information they obtain and their own insights within the community. This stimulates discussion and exchange of opinions among other users.

[0244] Step 8:

[0245] The server monitors activity within the community and records updates in a database in real time. It also collects user activity history to help provide a more personalized learning experience.

[0246] (Example 1)

[0247] 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".

[0248] In today's educational environment, there is a lack of opportunities to efficiently acquire and share specialized knowledge with others. Furthermore, there is a demand for updating specialized knowledge in each field based on ever-changing information. Therefore, an efficient system is needed that allows users to easily create and participate in learning communities and resolve specialized questions.

[0249] 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.

[0250] In this invention, the server includes a device that enables learners to create and join learning groups, a device that automatically responds to learners' questions using generative intelligence models specialized in each field, and a device that displays the generative intelligence responses and learner submissions on a display unit. This allows users to access a wide range of high-quality educational and learning opportunities.

[0251] A "learner" refers to an individual or group whose purpose is to access an educational system and acquire or share knowledge.

[0252] A "learning group" refers to an organization or community formed by learners with specific goals or interests, with the purpose of sharing and exchanging knowledge.

[0253] A "generative intelligence model" refers to an artificial intelligence program that is trained on a large dataset and has the ability to automatically generate responses to questions related to specific expertise.

[0254] An "educational data set" refers to a collection of data containing a large amount of knowledge information used to train generative intelligence models.

[0255] The term "memory unit" refers to a device or area used to store information such as a learner's registration information and learning history.

[0256] An "information processing system" refers to a combination of hardware and software configured to collect, process, store, and distribute various types of information.

[0257] "Device" refers to a machine or software component designed to perform a specific function.

[0258] This invention provides an information processing system that utilizes generative intelligence models to enable learners to efficiently acquire specialized knowledge and to form and participate in learning groups. Specific embodiments thereof are described below.

[0259] 1. Model preparation and management

[0260] The server prepares generative intelligence models tailored to each specialized field. These models are trained using large educational data sets and have the ability to generate accurate answers to questions related to specific fields. The server regularly updates the training datasets for these models, improving their accuracy by reflecting the latest information.

[0261] 2. Creating and participating in learning groups

[0262] The terminal provides learners with the primary means of accessing the system. The user-friendly interface on the terminal allows learners to create and join learning groups based on their interests and needs. This enables learners to share knowledge with others who share common interests.

[0263] 3. Generating prompts and responses

[0264] Users input the information they want to know or their questions in the form of prompts through their terminal. For example, they can input specific questions such as, "I want to know more about the theory of evolution in biology." The server then uses a generative intelligence model to generate a detailed and accurate response to the prompt and displays the result on the terminal.

[0265] Specific example

[0266] When a user enters the prompt "Please tell me about the impact of climate change on biodiversity," a generative intelligence model on the server analyzes the latest academic information related to this question and provides an answer in an easy-to-understand format. The terminal displays the generated information in a highly visible format to assist the user in acquiring knowledge.

[0267] This system is designed to facilitate access to specialized knowledge and provide diverse educational opportunities. Through it, learners can deepen their knowledge and grow together with other participants.

[0268] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0269] Step 1:

[0270] The user enters the information or question they want to know as an input prompt through the interface on the terminal. This input is in text format and may include specific technical questions. For example, they might enter a specific prompt such as, "Please tell me about the impact of climate change on biodiversity."

[0271] Step 2:

[0272] The terminal sends the prompts entered by the user to the server. This transmission is carried out through a secure communication protocol and serves to ensure that the user's input is properly delivered to the server. At this point, the input prompts are ready to be parsed by the server.

[0273] Step 3:

[0274] The server analyzes the received prompt and selects the most suitable generative intelligence model. At this stage, the server searches for relevant data based on the content of the prompt and applies the intelligence model to process the data. This generates an appropriate response to the prompt.

[0275] Step 4:

[0276] A generative intelligence model generates response data to prompts. The model extracts information relevant to the prompts from a vast dataset of educational data and constructs specific answers. This output data is in a format that contains information useful to the user.

[0277] Step 5:

[0278] The server sends the generated response to the terminal. This process is designed to efficiently compress the response data and send it quickly.

[0279] Step 6:

[0280] The terminal displays the received response data in a user-friendly format. This enables the user to easily view and understand the answers to the questions. The display helps the user determine how to use the information as the next step.

[0281] (Application Example 1)

[0282] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0283] The present invention aims to provide a system that forms an educational group for users to deepen their specialized knowledge and can be easily participated in. And there is a problem of constructing an environment where users can acquire knowledge quickly and accurately using a generative intelligent model. In particular, it is required to visually provide specialized video content and enable efficient understanding by individualizing the user's learning experience. Also, means to promote the exchange of opinions among users and activate the sharing of knowledge are important.

[0284] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0285] In this invention, the server includes means that enable users to form and participate in an educational group, means that automatically respond to users' questions using a generative intelligent model specialized in each specialized field, means that output the responses of the generative intelligence and the posted information from users to an information display device, means that use a visual display device for users to obtain specialized video content in real time and provide individualized educational information, and means that facilitate the exchange of opinions with other users. Thereby, users can efficiently learn specialized knowledge and can correspond to various learning styles.

[0286] An "educational group" is a group for sharing specific specialized knowledge or information, which is intended for users to participate in or form.

[0287] A "generative intelligence model" is an artificial intelligence technology that learns based on a vast amount of data and can automatically generate responses to questions from users.

[0288] An "information display device" is a device or software used to visually output knowledge and information to users.

[0289] A "visual display device" is a device for users to receive video content visually in real time and enables the provision of personalized educational information.

[0290] "Opinion exchange" is a communication means for users to share knowledge and opinions with each other to obtain new perspectives and understandings.

[0291] [[ID=I8]]This system is a mechanism for users to efficiently learn specialized knowledge. First, the server provides an online platform for users to form and participate in educational groups of interest. On this platform, it is possible to utilize generative intelligence models specialized in each field of expertise to automatically provide accurate responses to users' questions. The generative intelligence model installed on the server performs advanced computational processing based on an updated training dataset to appropriately respond to users' inquiries.

[0292] In addition, the terminal is equipped with an information display device and can output in real time the responses of generative intelligence and various submission information from users. Furthermore, through the visual display device, users can receive personalized educational video content and deepen their visual understanding.

[0293] Through this system, users can easily exchange opinions with other users and acquire new perspectives and knowledge. This interaction is an important function for deepening knowledge and bringing about diverse viewpoints. For example, by entering a prompt such as "Please tell me about the technology used to build the pyramids of ancient Egypt," users can obtain detailed information and engage in discussions based on it.

[0294] The entire system operates on devices such as smartphones and head-mounted displays, and generative intelligence is implemented using technologies like OpenAI GPT. This enables an intuitive and effective learning experience for the user.

[0295] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0296] Step 1:

[0297] Users access educational programs through their devices.

[0298] Input: User's areas of interest and registration information

[0299] Processing: Send user information to the server and execute a database query to search for relevant educational groups.

[0300] Output: A list of educational groups suitable for the user is displayed.

[0301] Step 2:

[0302] The server uses a generative intelligence model to generate a response based on the user's prompt.

[0303] Input: The prompt text entered by the user (e.g., "Please tell me about the technology used to build the pyramids of ancient Egypt.")

[0304] Processing: Analyze the prompt text and perform data calculations to generate relevant information using a generative AI model.

[0305] Output: The generated answer is sent to the terminal.

[0306] Step 3:

[0307] The terminal outputs the response of the generative intelligence to the user via the information display device.

[0308] Input: Answer from the generative intelligence model

[0309] Process: Structure the answer and display it on the interface in a visually understandable format.

[0310] Output: The user can view detailed information on the display device.

[0311] Step 4:

[0312] The user watches videos through the terminal and obtains personalized educational information.

[0313] Input: Video information related to the content selected by the user

[0314] Process: Utilize the visual display device to stream or play the video in real time.

[0315] Output: The video is presented to the user's vision and learning is promoted.

[0316] Step 5:

[0317] The server provides a platform to facilitate the exchange of opinions among users.

[0318] Input: User comments and opinions

[0319] Process: Distribute the user's posted information to other users in real time and perform data transmission to provide a discussion venue.

[0320] Output: Other users' opinions and comments are displayed, facilitating knowledge sharing.

[0321] 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.

[0322] This invention is a system that enhances the effectiveness of generative artificial intelligence models and learning communities by incorporating an emotion engine that recognizes user emotions. To realize this system, the following embodiments will be described.

[0323] The server features generative artificial intelligence models with expertise in various fields, as well as an emotion engine that analyzes user input and dialogue data to recognize emotions. This emotion engine uses natural language processing techniques and machine learning algorithms to determine the emotional state of the user from the text and actions they express.

[0324] The device is designed to allow users to easily engage in emotionally charged interactions. Emotional information recognized by the emotion engine is sent to a generative artificial intelligence model, which generates flexible responses tailored to the user's emotional state. This makes it possible to provide users with a more personalized learning experience.

[0325] For example, if a user inputs into the system, "My recent math assignments have been very difficult and I'm exhausted," the emotion engine analyzes this input and recognizes that the user is feeling tired or frustrated. The generative artificial intelligence model then responds in a gentle tone, such as, "It's okay to take a short break. When you find math problems difficult, try breaking them down into smaller tasks."

[0326] Furthermore, the emotion engine accumulates the user's emotional history and uses this to provide recommendations that more efficiently support their learning approach. The server utilizes this information to facilitate interaction with other users and work to improve the quality of learning across the entire community. It also plays a role in promoting empathetic interaction by visually displaying the emotional information of other participants to the user on their device.

[0327] In this way, the present invention aims to provide an interactive learning environment that goes beyond mere information provision and responds to emotions, thereby realizing a richer educational experience.

[0328] The following describes the processing flow.

[0329] Step 1:

[0330] Users access the learning community platform through their devices and take action to join new or existing communities. A user's participation request is sent to the server.

[0331] Step 2:

[0332] The server approves user participation requests and updates the database to keep community information and participant lists up-to-date. It also reviews the user's past sentiment data to prepare personalized support.

[0333] Step 3:

[0334] The user inputs questions to a generative artificial intelligence model on their device. During this process, the emotion engine monitors the user's language and choices, and analyzes their emotional state.

[0335] Step 4:

[0336] The emotion engine determines the emotional state (e.g., stress, excitement, sadness, etc.) obtained from the user's input and provides that information to a generative artificial intelligence model.

[0337] Step 5:

[0338] Generative artificial intelligence models generate responses tailored to the user's feelings based on emotional information received from an emotion engine. These responses take into account the user's learned data and current mental state.

[0339] Step 6:

[0340] The device displays the generated response to the user. The display format is customized to engage the user's interest and is designed to make it easier for the user to choose their next action based on their emotions.

[0341] Step 7:

[0342] Users can use the information they gain to continue learning and exchange opinions with other users within the community. They can also receive empathetic feedback from other users based on their emotions.

[0343] Step 8:

[0344] The server stores user feedback and communication history, using this data to improve the quality of future interactions. It also analyzes learning and emotional expression trends to help improve the system.

[0345] (Example 2)

[0346] 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".

[0347] In conventional learning support systems, information is provided without considering the user's emotional state, resulting in a uniform learning experience and making it difficult to provide optimal support tailored to individual users. Furthermore, the inability to obtain responses that reflect the user's emotions posed challenges in maintaining user motivation and improving learning efficiency.

[0348] 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.

[0349] In this invention, the server includes means for enabling users to create and participate in learning groups, means for automatically responding to user questions using generative artificial intelligence models specialized in each field, means for using an emotion engine to analyze user input data and identify emotional states, and means for displaying the responses of the generative artificial intelligence and responses based on the user's emotional state on a user interface. This enables users to receive a personalized learning experience tailored to their emotional state.

[0350] The term "user" refers to an individual or group of people who use a system to engage in learning activities.

[0351] A "learning group" refers to a community organized for educational purposes that users can belong to.

[0352] A "generative artificial intelligence model" refers to artificial intelligence technology that has learned knowledge in various fields in advance and has the ability to automatically answer user questions.

[0353] An "emotion engine" refers to a technology that analyzes user input data to identify their emotional state, utilizing natural language processing and machine learning algorithms.

[0354] "User interface" is a concept that refers to the screens and means of operation that allow a system and a user to interact with each other.

[0355] "Generative artificial intelligence response" refers to the answers or information provided by a generative artificial intelligence model based on user input.

[0356] "Emotional state" refers to a psychological or emotional condition detected based on the user's expressions and behavior.

[0357] A "database" refers to a collection of information that systematically stores and manages collected user registration information, learning history, and other data.

[0358] This invention is a system designed to provide users with a personalized learning experience, primarily achieved through the interplay of three parties: a server, a terminal, and the user, each fulfilling their respective roles.

[0359] The server plays a central role in this invention and is equipped with a generative artificial intelligence model and an emotion engine. The emotion engine uses natural language processing techniques and machine learning algorithms to analyze text data entered by the user and identify their emotional state. This allows the server to generate appropriate responses that take into account the user's psychological situation.

[0360] A terminal is a device used by users to interact with the system. It receives user input through the user interface and transmits that data to the server. It can also present responses from the server to the user visually or audibly.

[0361] Users can use this system to receive support for their learning tasks by inputting information. For example, based on specific input such as "My recent math assignments have been very difficult and I'm exhausted," they can receive responses that address their emotions.

[0362] As a concrete example, when a user inputs "tired" into the terminal, the server's emotion engine analyzes the input and identifies the emotion as "fatigue." In response, the generative artificial intelligence model generates advice such as "You should take a short break," which is then presented to the user through the terminal. Predefined prompt sentences are used to generate this response; for example, a specific prompt sentence such as "What suggestion should be made when a user feels tired?" is used.

[0363] In this way, the present invention realizes flexible and effective learning support that is tailored to the user's emotions.

[0364] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0365] Step 1:

[0366] The terminal receives user input. This input can be in text or voice format; for example, it can receive a message such as, "My recent math assignments were very difficult and I'm exhausted." This input is then sent to the server as digital data.

[0367] Step 2:

[0368] The server passes user input sent from the terminal to the emotion engine. The emotion engine uses natural language processing technology to analyze the received text data. This analysis determines the user's emotional state and identifies emotions such as "fatigue" or "frustration." This emotional information becomes the output of the emotion engine.

[0369] Step 3:

[0370] The server sends the emotional information received from the emotion engine to the generative AI model. The generative AI model receives the emotional information as input and generates responses as instructed by the prompt. For example, the prompt might provide an indicator such as "gentle advice when the user is tired," and based on this information, it generates a response such as "Try taking a short break." This is the output of the generative AI model.

[0371] Step 4:

[0372] The server sends the response obtained from the generated AI model to the terminal. The terminal presents this response to the user visually or audibly, providing the user with an emotionally relevant response. Specifically, this may involve text being displayed on the user's screen or the message being read aloud using speech synthesis.

[0373] Step 5:

[0374] The server further records and stores the user's emotions and response history in a database. This information will be used to personalize future learning experiences. In this process, historical data is processed and saved to the database as new entries.

[0375] (Application Example 2)

[0376] 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."

[0377] In recent years, recognizing user emotions and providing more personalized responses has become increasingly important for making interactions within data processing sets more meaningful. However, conventional technologies lack the ability to generate responses that take into account the user's emotional state, making it difficult to improve engagement within data processing sets. Solving this problem is crucial.

[0378] 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.

[0379] In this invention, the server includes means for enabling users to create and participate in data processing sets, means for automatically responding to user questions using generative intelligence models specialized in each field, means for displaying the generative intelligence responses and user information on the user screen, and means for an emotion mechanism that recognizes diverse emotional states and adjusts responses based on the user's emotional information. This enables flexible responses in accordance with the user's emotions and improves the quality of communication within the data processing set.

[0380] A "data processing set" is a virtual environment where users can participate and exchange information, serving as a platform for sharing knowledge and information from different specialized fields.

[0381] A "generative intelligence model" is a type of artificial intelligence that learns from a specific dataset and has the ability to generate responses to a variety of questions.

[0382] A "user interface" is a visual display that allows users to interact with the system through an interface, and enables the input and output of information.

[0383] An "emotional mechanism" is a technical means of analyzing and determining emotions from user input and behavior, thereby enabling an understanding of the user's emotional state.

[0384] A "recording device" is a database or other storage medium that securely stores a user's personal information and history, and allows them to be retrieved as needed.

[0385] "User verification" is a process that verifies the user's identity and authority, and is a means of ensuring safe and reliable access.

[0386] "Empathic interaction" refers to interactions that promote the sharing and understanding of emotions among users, and is an activity aimed at deepening relationships within a data processing set.

[0387] The system for implementing the present invention mainly consists of three components: a server, a terminal, and a user. The server is equipped with a generative intelligence model and has an emotion mechanism built in to analyze the user's input information and emotional state. The emotion mechanism uses natural language processing technology and machine learning algorithms to process text and behavioral data input by the user and to determine the user's emotional state. Dedicated software such as TensorFlow and NLTK is used for this processing.

[0388] The terminal is a device for users to participate in a data processing set and exchange information through a user interface, and smartphones and tablets are examples of such devices. User interactions are displayed on this interface, and responses generated by generative intelligence models are also displayed visually.

[0389] As a concrete example, if a customer in a physical store says, "I'm having trouble finding a staff member," this statement is captured by the terminal and sent as text data to the server. The emotion mechanism on the server analyzes the statement and detects the customer's confusion and frustration. Based on this emotion recognition, the generative intelligence model immediately provides the terminal with an appropriate response, such as, "A staff member will be with you shortly. Please wait a moment." This allows the customer to receive prompt and accurate service.

[0390] An example of a prompt message input to a generative AI model might be, "The customer seems stressed. What is the best course of action?" This allows the generative intelligence model to provide personalized responses that respond to the user's emotions.

[0391] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0392] Step 1:

[0393] The user initiates voice input into the device. The device uses a speech recognition system to convert the voice data into text data. This text data is then sent to the server. The input is voice data, and the output is text data. A service such as the Google Speech-to-Text API is used for speech recognition.

[0394] Step 2:

[0395] The server inputs the received text data into the sentiment mechanism. The sentiment mechanism uses natural language processing algorithms to analyze the sentiment attributes of the text. As a result of the analysis, the user's emotional state (e.g., confused, irritated) is output. This process utilizes NLTK and sentiment analysis libraries.

[0396] Step 3:

[0397] The server inputs the emotional state and original text data into a generative AI model. The generative AI model generates the optimal response based on this data. The content of the response (e.g., "A staff member will be with you shortly. Please wait a moment.") is output. TensorFlow or PyTorch are used to operate the generative AI model.

[0398] Step 4:

[0399] The server sends the generated response to the terminal. The terminal displays the received response to the user visually or audibly. The user decides on the next action based on this response. In this step, the user can verify whether the response is appropriate.

[0400] 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.

[0401] 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.

[0402] 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.

[0403] [Third Embodiment]

[0404] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0405] 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.

[0406] 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).

[0407] 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.

[0408] 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.

[0409] 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).

[0410] 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.

[0411] 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.

[0412] 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.

[0413] 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.

[0414] 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.

[0415] 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".

[0416] This invention provides a system that allows users to easily create and participate in learning communities and obtain specialized knowledge from generative artificial intelligence models. Specific embodiments thereof are described below.

[0417] First, the server prepares generative artificial intelligence models tailored to each area of ​​expertise. These AI models are trained on vast datasets and possess the ability to provide accurate responses to a wide range of user questions. The server also provides a function that allows users to create new online learning communities. Activity history and posts within these communities are managed by the server and stored in a database.

[0418] The terminal provides the primary means for users to access this system. A user-friendly interface allows users to directly input questions into the generative artificial intelligence and easily search for and join learning communities of interest. The answers provided by the generative AI are displayed on the terminal in a highly visible format.

[0419] For example, if a user wants to learn more about the theory of evolution in biology, they input their question into the device. A generative artificial intelligence model on the server analyzes relevant academic information and generates a detailed explanation of evolution in real time. The device then clearly displays the answer on the user's screen to support their understanding.

[0420] Furthermore, users can deepen their knowledge by exchanging opinions and engaging in discussions with other participants based on the information they obtain. The server also manages these discussions and helps ensure that all users have fair access to the information. In this way, the present invention can realize an online environment that widely disseminates high-quality education and provides diverse learning opportunities.

[0421] The following describes the processing flow.

[0422] Step 1:

[0423] The server initializes generative artificial intelligence models corresponding to each specialized field. This includes the process of loading relevant datasets and appropriately training the AI ​​models.

[0424] Step 2:

[0425] Users access the system's user interface via a terminal and create an account. User information is sent to the server and stored in a database.

[0426] Step 3:

[0427] Users create communities based on academic fields or topics that interest them. The server registers community information and updates the database so that other users can join.

[0428] Step 4:

[0429] The user enters a question for a generative artificial intelligence model on their device. The entered question is sent to a server and passed to the appropriate AI model.

[0430] Step 5:

[0431] A generative artificial intelligence model on the server analyzes the received question, searches for relevant knowledge, and generates an answer. The generated answer is sent to the terminal via the server for the user to return.

[0432] Step 6:

[0433] The device displays the responses obtained from the generative artificial intelligence model in an easy-to-understand format for the user. The user can read this and proceed with their learning.

[0434] Step 7:

[0435] Users share the information they obtain and their own insights within the community. This stimulates discussion and exchange of opinions among other users.

[0436] Step 8:

[0437] The server monitors activity within the community and records updates in a database in real time. It also collects user activity history to help provide a more personalized learning experience.

[0438] (Example 1)

[0439] 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."

[0440] In today's educational environment, there is a lack of opportunities to efficiently acquire and share specialized knowledge with others. Furthermore, there is a demand for updating specialized knowledge in each field based on ever-changing information. Therefore, an efficient system is needed that allows users to easily create and participate in learning communities and resolve specialized questions.

[0441] 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.

[0442] In this invention, the server includes a device that enables learners to create and join learning groups, a device that automatically responds to learners' questions using generative intelligence models specialized in each field, and a device that displays the generative intelligence responses and learner submissions on a display unit. This allows users to access a wide range of high-quality educational and learning opportunities.

[0443] A "learner" refers to an individual or group whose purpose is to access an educational system and acquire or share knowledge.

[0444] A "learning group" refers to an organization or community formed by learners with specific goals or interests, with the purpose of sharing and exchanging knowledge.

[0445] A "generative intelligence model" refers to an artificial intelligence program that is trained on a large dataset and has the ability to automatically generate responses to questions related to specific expertise.

[0446] An "educational data set" refers to a collection of data containing a large amount of knowledge information used to train generative intelligence models.

[0447] The term "memory unit" refers to a device or area used to store information such as a learner's registration information and learning history.

[0448] An "information processing system" refers to a combination of hardware and software configured to collect, process, store, and distribute various types of information.

[0449] "Device" refers to a machine or software component designed to perform a specific function.

[0450] This invention provides an information processing system that utilizes generative intelligence models to enable learners to efficiently acquire specialized knowledge and to form and participate in learning groups. Specific embodiments thereof are described below.

[0451] 1. Model preparation and management

[0452] The server prepares generative intelligence models tailored to each specialized field. These models are trained using large educational data sets and have the ability to generate accurate answers to questions related to specific fields. The server regularly updates the training datasets for these models, improving their accuracy by reflecting the latest information.

[0453] 2. Creating and participating in learning groups

[0454] The terminal provides learners with the primary means of accessing the system. The user-friendly interface on the terminal allows learners to create and join learning groups based on their interests and needs. This enables learners to share knowledge with others who share common interests.

[0455] 3. Generating prompts and responses

[0456] Users input the information they want to know or their questions in the form of prompts through their terminal. For example, they can input specific questions such as, "I want to know more about the theory of evolution in biology." The server then uses a generative intelligence model to generate a detailed and accurate response to the prompt and displays the result on the terminal.

[0457] Specific example

[0458] When a user enters the prompt "Please tell me about the impact of climate change on biodiversity," a generative intelligence model on the server analyzes the latest academic information related to this question and provides an answer in an easy-to-understand format. The terminal displays the generated information in a highly visible format to assist the user in acquiring knowledge.

[0459] This system is designed to facilitate access to specialized knowledge and provide diverse educational opportunities. Through it, learners can deepen their knowledge and grow together with other participants.

[0460] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0461] Step 1:

[0462] The user enters the information or question they want to know as an input prompt through the interface on the terminal. This input is in text format and may include specific technical questions. For example, they might enter a specific prompt such as, "Please tell me about the impact of climate change on biodiversity."

[0463] Step 2:

[0464] The terminal sends the prompts entered by the user to the server. This transmission is carried out through a secure communication protocol and serves to ensure that the user's input is properly delivered to the server. At this point, the input prompts are ready to be parsed by the server.

[0465] Step 3:

[0466] The server analyzes the received prompt and selects the most suitable generative intelligence model. At this stage, the server searches for relevant data based on the content of the prompt and applies the intelligence model to process the data. This generates an appropriate response to the prompt.

[0467] Step 4:

[0468] A generative intelligence model generates response data to prompts. The model extracts information relevant to the prompts from a vast dataset of educational data and constructs specific answers. This output data is in a format that contains information useful to the user.

[0469] Step 5:

[0470] The server sends the generated response to the terminal. This process is designed to efficiently compress the response data and send it quickly.

[0471] Step 6:

[0472] The terminal displays the received response data in a user-friendly format. This allows the user to easily view and understand the answers to their questions. The display helps the user decide how to use the information for their next steps.

[0473] (Application Example 1)

[0474] 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."

[0475] The present invention aims to provide a system that allows users to easily participate in a group of educational programs designed to deepen their specialized knowledge. It also aims to create an environment where users can acquire knowledge quickly and accurately using a generative intelligence model. In particular, it requires the visual presentation of specialized video content and the individualization of the user's learning experience to enable efficient understanding. Furthermore, means to facilitate the exchange of opinions among users and activate knowledge sharing are also important.

[0476] 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.

[0477] In this invention, the server includes means for enabling users to form and participate in educational groups, means for automatically responding to user questions using generative intelligence models specialized in each area of ​​expertise, means for outputting the generative intelligence responses and user-submitted information to an information display device, means for providing users with personalized educational information by allowing them to acquire specialized video content in real time using a visual display device, and means for facilitating the exchange of opinions with other users. This enables users to efficiently acquire specialized knowledge and accommodate diverse learning styles.

[0478] An "educational group" is a group intended for users to participate in or form, for the purpose of sharing specific specialized knowledge and information.

[0479] A "generative intelligence model" is an artificial intelligence technology that learns from vast amounts of data and can automatically generate responses to user questions.

[0480] An "information display device" is a device or software used to visually output knowledge and information to a user.

[0481] A "visual display device" is a device that allows users to visually receive video content in real time, enabling the provision of personalized educational information.

[0482] "Exchange of opinions" is a means of communication where users share knowledge and views with each other, thereby gaining new perspectives and understandings.

[0483] This system is designed to enable users to efficiently acquire specialized knowledge. First, the server provides an online platform for users to form and participate in educational groups of interest. This platform utilizes generative intelligence models specialized in each field, enabling them to automatically provide accurate responses to user questions. The generative intelligence models installed on the server perform advanced computational processing based on updated training datasets to appropriately respond to user inquiries.

[0484] Furthermore, the terminal is equipped with an information display device, which can output responses from generative intelligence and various user-submitted information in real time. In addition, through the visual display device, users can receive personalized educational video content and deepen their understanding visually.

[0485] Through this system, users can easily exchange opinions with other users and acquire new perspectives and knowledge. This interaction is an important function for deepening knowledge and bringing about diverse viewpoints. For example, by entering a prompt such as "Please tell me about the technology used to build the pyramids of ancient Egypt," users can obtain detailed information and engage in discussions based on it.

[0486] The entire system operates on devices such as smartphones and head-mounted displays, and generative intelligence is implemented using technologies like OpenAI GPT. This enables an intuitive and effective learning experience for the user.

[0487] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0488] Step 1:

[0489] Users access educational programs through their devices.

[0490] Input: User's areas of interest and registration information

[0491] Processing: Send user information to the server and execute a database query to search for relevant educational groups.

[0492] Output: A list of educational groups suitable for the user is displayed.

[0493] Step 2:

[0494] The server uses a generative intelligence model to generate a response based on the user's prompt.

[0495] Input: The prompt text entered by the user (e.g., "Please tell me about the technology used to build the pyramids of ancient Egypt.")

[0496] Processing: Analyze the prompt text and perform data calculations to generate relevant information using a generative AI model.

[0497] Output: The generated answer is sent to the device.

[0498] Step 3:

[0499] The terminal outputs generative intelligence responses to the user via an information display device.

[0500] Input: Response from a generative intelligence model

[0501] Processing: Structure the responses and display them in a visually easy-to-understand format on the interface.

[0502] Output: The user can view detailed information on the display device.

[0503] Step 4:

[0504] Users view videos through their devices and receive personalized educational information.

[0505] Input: Video information related to the content selected by the user.

[0506] Processing: Utilize visual display devices to stream or play video in real time.

[0507] Output: Images are presented to the user's vision, facilitating learning.

[0508] Step 5:

[0509] The server provides a platform to facilitate the exchange of opinions among users.

[0510] Input: User comments and opinions

[0511] Processing: Data is transmitted to distribute user-submitted information to other users in real time, providing a platform for discussion.

[0512] Output: Other users' opinions and comments are displayed, facilitating knowledge sharing.

[0513] 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.

[0514] This invention is a system that enhances the effectiveness of generative artificial intelligence models and learning communities by incorporating an emotion engine that recognizes user emotions. To realize this system, the following embodiments will be described.

[0515] The server features generative artificial intelligence models with expertise in various fields, as well as an emotion engine that analyzes user input and dialogue data to recognize emotions. This emotion engine uses natural language processing techniques and machine learning algorithms to determine the emotional state of the user from the text and actions they express.

[0516] The device is designed to allow users to easily engage in emotionally charged interactions. Emotional information recognized by the emotion engine is sent to a generative artificial intelligence model, which generates flexible responses tailored to the user's emotional state. This makes it possible to provide users with a more personalized learning experience.

[0517] For example, if a user inputs into the system, "My recent math assignments have been very difficult and I'm exhausted," the emotion engine analyzes this input and recognizes that the user is feeling tired or frustrated. The generative artificial intelligence model then responds in a gentle tone, such as, "It's okay to take a short break. When you find math problems difficult, try breaking them down into smaller tasks."

[0518] Furthermore, the emotion engine accumulates the user's emotional history and uses this to provide recommendations that more efficiently support their learning approach. The server utilizes this information to facilitate interaction with other users and work to improve the quality of learning across the entire community. It also plays a role in promoting empathetic interaction by visually displaying the emotional information of other participants to the user on their device.

[0519] In this way, the present invention aims to provide an interactive learning environment that goes beyond mere information provision and responds to emotions, thereby realizing a richer educational experience.

[0520] The following describes the processing flow.

[0521] Step 1:

[0522] Users access the learning community platform through their devices and take action to join new or existing communities. A user's participation request is sent to the server.

[0523] Step 2:

[0524] The server approves user participation requests and updates the database to keep community information and participant lists up-to-date. It also reviews the user's past sentiment data to prepare personalized support.

[0525] Step 3:

[0526] The user inputs questions to a generative artificial intelligence model on their device. During this process, the emotion engine monitors the user's language and choices, and analyzes their emotional state.

[0527] Step 4:

[0528] The emotion engine determines the emotional state (e.g., stress, excitement, sadness, etc.) obtained from the user's input and provides that information to a generative artificial intelligence model.

[0529] Step 5:

[0530] Generative artificial intelligence models generate responses tailored to the user's feelings based on emotional information received from an emotion engine. These responses take into account the user's learned data and current mental state.

[0531] Step 6:

[0532] The device displays the generated response to the user. The display format is customized to engage the user's interest and is designed to make it easier for the user to choose their next action based on their emotions.

[0533] Step 7:

[0534] Users can use the information they gain to continue learning and exchange opinions with other users within the community. They can also receive empathetic feedback from other users based on their emotions.

[0535] Step 8:

[0536] The server stores user feedback and communication history, using this data to improve the quality of future interactions. It also analyzes learning and emotional expression trends to help improve the system.

[0537] (Example 2)

[0538] 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."

[0539] In conventional learning support systems, information is provided without considering the user's emotional state, resulting in a uniform learning experience and making it difficult to provide optimal support tailored to individual users. Furthermore, the inability to obtain responses that reflect the user's emotions posed challenges in maintaining user motivation and improving learning efficiency.

[0540] 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.

[0541] In this invention, the server includes means for enabling users to create and participate in learning groups, means for automatically responding to user questions using generative artificial intelligence models specialized in each field, means for using an emotion engine to analyze user input data and identify emotional states, and means for displaying the responses of the generative artificial intelligence and responses based on the user's emotional state on a user interface. This enables users to receive a personalized learning experience tailored to their emotional state.

[0542] The term "user" refers to an individual or group of people who use a system to engage in learning activities.

[0543] A "learning group" refers to a community organized for educational purposes that users can belong to.

[0544] A "generative artificial intelligence model" refers to artificial intelligence technology that has learned knowledge in various fields in advance and has the ability to automatically answer user questions.

[0545] An "emotion engine" refers to a technology that analyzes user input data to identify their emotional state, utilizing natural language processing and machine learning algorithms.

[0546] "User interface" is a concept that refers to the screens and means of operation that allow a system and a user to interact with each other.

[0547] "Generative artificial intelligence response" refers to the answers or information provided by a generative artificial intelligence model based on user input.

[0548] "Emotional state" refers to a psychological or emotional condition detected based on the user's expressions and behavior.

[0549] A "database" refers to a collection of information that systematically stores and manages collected user registration information, learning history, and other data.

[0550] This invention is a system designed to provide users with a personalized learning experience, primarily achieved through the interplay of three parties: a server, a terminal, and the user, each fulfilling their respective roles.

[0551] The server plays a central role in this invention and is equipped with a generative artificial intelligence model and an emotion engine. The emotion engine uses natural language processing techniques and machine learning algorithms to analyze text data entered by the user and identify their emotional state. This allows the server to generate appropriate responses that take into account the user's psychological situation.

[0552] A terminal is a device used by users to interact with the system. It receives user input through the user interface and transmits that data to the server. It can also present responses from the server to the user visually or audibly.

[0553] Users can use this system to receive support for their learning tasks by inputting information. For example, based on specific input such as "My recent math assignments have been very difficult and I'm exhausted," they can receive responses that address their emotions.

[0554] As a concrete example, when a user inputs "tired" into the terminal, the server's emotion engine analyzes the input and identifies the emotion as "fatigue." In response, the generative artificial intelligence model generates advice such as "You should take a short break," which is then presented to the user through the terminal. Predefined prompt sentences are used to generate this response; for example, a specific prompt sentence such as "What suggestion should be made when a user feels tired?" is used.

[0555] In this way, the present invention realizes flexible and effective learning support that is tailored to the user's emotions.

[0556] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0557] Step 1:

[0558] The terminal receives user input. This input can be in text or voice format; for example, it can receive a message such as, "My recent math assignments were very difficult and I'm exhausted." This input is then sent to the server as digital data.

[0559] Step 2:

[0560] The server passes user input sent from the terminal to the emotion engine. The emotion engine uses natural language processing technology to analyze the received text data. This analysis determines the user's emotional state and identifies emotions such as "fatigue" or "frustration." This emotional information becomes the output of the emotion engine.

[0561] Step 3:

[0562] The server sends the emotional information received from the emotion engine to the generative AI model. The generative AI model receives the emotional information as input and generates responses as instructed by the prompt. For example, the prompt might provide an indicator such as "gentle advice when the user is tired," and based on this information, it generates a response such as "Try taking a short break." This is the output of the generative AI model.

[0563] Step 4:

[0564] The server sends the response obtained from the generated AI model to the terminal. The terminal presents this response to the user visually or audibly, providing the user with an emotionally relevant response. Specifically, this may involve text being displayed on the user's screen or the message being read aloud using speech synthesis.

[0565] Step 5:

[0566] The server further records and stores the user's emotions and response history in a database. This information will be used to personalize future learning experiences. In this process, historical data is processed and saved to the database as new entries.

[0567] (Application Example 2)

[0568] 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."

[0569] In recent years, recognizing user emotions and providing more personalized responses has become increasingly important for making interactions within data processing sets more meaningful. However, conventional technologies lack the ability to generate responses that take into account the user's emotional state, making it difficult to improve engagement within data processing sets. Solving this problem is crucial.

[0570] 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.

[0571] In this invention, the server includes means for enabling users to create and participate in data processing sets, means for automatically responding to user questions using generative intelligence models specialized in each field, means for displaying the generative intelligence responses and user information on the user screen, and means for an emotion mechanism that recognizes diverse emotional states and adjusts responses based on the user's emotional information. This enables flexible responses in accordance with the user's emotions and improves the quality of communication within the data processing set.

[0572] A "data processing set" is a virtual environment where users can participate and exchange information, serving as a platform for sharing knowledge and information from different specialized fields.

[0573] A "generative intelligence model" is a type of artificial intelligence that learns from a specific dataset and has the ability to generate responses to a variety of questions.

[0574] A "user interface" is a visual display that allows users to interact with the system through an interface, and enables the input and output of information.

[0575] An "emotional mechanism" is a technical means of analyzing and determining emotions from user input and behavior, thereby enabling an understanding of the user's emotional state.

[0576] A "recording device" is a database or other storage medium that securely stores a user's personal information and history, and allows them to be retrieved as needed.

[0577] "User verification" is a process that verifies the user's identity and authority, and is a means of ensuring safe and reliable access.

[0578] "Empathic interaction" refers to interactions that promote the sharing and understanding of emotions among users, and is an activity aimed at deepening relationships within a data processing set.

[0579] The system for implementing the present invention mainly consists of three components: a server, a terminal, and a user. The server is equipped with a generative intelligence model and has an emotion mechanism built in to analyze the user's input information and emotional state. The emotion mechanism uses natural language processing technology and machine learning algorithms to process text and behavioral data input by the user and to determine the user's emotional state. Dedicated software such as TensorFlow and NLTK is used for this processing.

[0580] The terminal is a device for users to participate in a data processing set and exchange information through a user interface, and smartphones and tablets are examples of such devices. User interactions are displayed on this interface, and responses generated by generative intelligence models are also displayed visually.

[0581] As a concrete example, if a customer in a physical store says, "I'm having trouble finding a staff member," this statement is captured by the terminal and sent as text data to the server. The emotion mechanism on the server analyzes the statement and detects the customer's confusion and frustration. Based on this emotion recognition, the generative intelligence model immediately provides the terminal with an appropriate response, such as, "A staff member will be with you shortly. Please wait a moment." This allows the customer to receive prompt and accurate service.

[0582] An example of a prompt message input to a generative AI model might be, "The customer seems stressed. What is the best course of action?" This allows the generative intelligence model to provide personalized responses that respond to the user's emotions.

[0583] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0584] Step 1:

[0585] The user initiates voice input into the device. The device uses a speech recognition system to convert the voice data into text data. This text data is then sent to the server. The input is voice data, and the output is text data. A service such as the Google Speech-to-Text API is used for speech recognition.

[0586] Step 2:

[0587] The server inputs the received text data into the sentiment mechanism. The sentiment mechanism uses natural language processing algorithms to analyze the sentiment attributes of the text. As a result of the analysis, the user's emotional state (e.g., confused, irritated) is output. This process utilizes NLTK and sentiment analysis libraries.

[0588] Step 3:

[0589] The server inputs the emotional state and original text data into a generative AI model. The generative AI model generates the optimal response based on this data. The content of the response (e.g., "A staff member will be with you shortly. Please wait a moment.") is output. TensorFlow or PyTorch are used to operate the generative AI model.

[0590] Step 4:

[0591] The server sends the generated response to the terminal. The terminal displays the received response to the user visually or audibly. The user decides on the next action based on this response. In this step, the user can verify whether the response is appropriate.

[0592] 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.

[0593] 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.

[0594] 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.

[0595] [Fourth Embodiment]

[0596] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0597] 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.

[0598] 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).

[0599] 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.

[0600] 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.

[0601] 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).

[0602] 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.

[0603] 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.

[0604] 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.

[0605] 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.

[0606] 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.

[0607] 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.

[0608] 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".

[0609] This invention provides a system that allows users to easily create and participate in learning communities and obtain specialized knowledge from generative artificial intelligence models. Specific embodiments thereof are described below.

[0610] First, the server prepares generative artificial intelligence models tailored to each area of ​​expertise. These AI models are trained on vast datasets and possess the ability to provide accurate responses to a wide range of user questions. The server also provides a function that allows users to create new online learning communities. Activity history and posts within these communities are managed by the server and stored in a database.

[0611] The terminal provides the primary means for users to access this system. A user-friendly interface allows users to directly input questions into the generative artificial intelligence and easily search for and join learning communities of interest. The answers provided by the generative AI are displayed on the terminal in a highly visible format.

[0612] For example, if a user wants to learn more about the theory of evolution in biology, they input their question into the device. A generative artificial intelligence model on the server analyzes relevant academic information and generates a detailed explanation of evolution in real time. The device then clearly displays the answer on the user's screen to support their understanding.

[0613] Furthermore, users can deepen their knowledge by exchanging opinions and engaging in discussions with other participants based on the information they obtain. The server also manages these discussions and helps ensure that all users have fair access to the information. In this way, the present invention can realize an online environment that widely disseminates high-quality education and provides diverse learning opportunities.

[0614] The following describes the processing flow.

[0615] Step 1:

[0616] The server initializes generative artificial intelligence models corresponding to each specialized field. This includes the process of loading relevant datasets and appropriately training the AI ​​models.

[0617] Step 2:

[0618] Users access the system's user interface via a terminal and create an account. User information is sent to the server and stored in a database.

[0619] Step 3:

[0620] Users create communities based on academic fields or topics that interest them. The server registers community information and updates the database so that other users can join.

[0621] Step 4:

[0622] The user enters a question for a generative artificial intelligence model on their device. The entered question is sent to a server and passed to the appropriate AI model.

[0623] Step 5:

[0624] A generative artificial intelligence model on the server analyzes the received question, searches for relevant knowledge, and generates an answer. The generated answer is sent to the terminal via the server for the user to return.

[0625] Step 6:

[0626] The device displays the responses obtained from the generative artificial intelligence model in an easy-to-understand format for the user. The user can read this and proceed with their learning.

[0627] Step 7:

[0628] Users share the information they obtain and their own insights within the community. This stimulates discussion and exchange of opinions among other users.

[0629] Step 8:

[0630] The server monitors activity within the community and records updates in a database in real time. It also collects user activity history to help provide a more personalized learning experience.

[0631] (Example 1)

[0632] 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".

[0633] In today's educational environment, there is a lack of opportunities to efficiently acquire and share specialized knowledge with others. Furthermore, there is a demand for updating specialized knowledge in each field based on ever-changing information. Therefore, an efficient system is needed that allows users to easily create and participate in learning communities and resolve specialized questions.

[0634] 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.

[0635] In this invention, the server includes a device that enables learners to create and join learning groups, a device that automatically responds to learners' questions using generative intelligence models specialized in each field, and a device that displays the generative intelligence responses and learner submissions on a display unit. This allows users to access a wide range of high-quality educational and learning opportunities.

[0636] A "learner" refers to an individual or group whose purpose is to access an educational system and acquire or share knowledge.

[0637] A "learning group" refers to an organization or community formed by learners with specific goals or interests, with the purpose of sharing and exchanging knowledge.

[0638] A "generative intelligence model" refers to an artificial intelligence program that is trained on a large dataset and has the ability to automatically generate responses to questions related to specific expertise.

[0639] An "educational data set" refers to a collection of data containing a large amount of knowledge information used to train generative intelligence models.

[0640] The term "memory unit" refers to a device or area used to store information such as a learner's registration information and learning history.

[0641] An "information processing system" refers to a combination of hardware and software configured to collect, process, store, and distribute various types of information.

[0642] "Device" refers to a machine or software component designed to perform a specific function.

[0643] This invention provides an information processing system that utilizes generative intelligence models to enable learners to efficiently acquire specialized knowledge and to form and participate in learning groups. Specific embodiments thereof are described below.

[0644] 1. Model preparation and management

[0645] The server prepares generative intelligence models tailored to each specialized field. These models are trained using large educational data sets and have the ability to generate accurate answers to questions related to specific fields. The server regularly updates the training datasets for these models, improving their accuracy by reflecting the latest information.

[0646] 2. Creating and participating in learning groups

[0647] The terminal provides learners with the primary means of accessing the system. The user-friendly interface on the terminal allows learners to create and join learning groups based on their interests and needs. This enables learners to share knowledge with others who share common interests.

[0648] 3. Generating prompts and responses

[0649] Users input the information they want to know or their questions in the form of prompts through their terminal. For example, they can input specific questions such as, "I want to know more about the theory of evolution in biology." The server then uses a generative intelligence model to generate a detailed and accurate response to the prompt and displays the result on the terminal.

[0650] Specific example

[0651] When a user enters the prompt "Please tell me about the impact of climate change on biodiversity," a generative intelligence model on the server analyzes the latest academic information related to this question and provides an answer in an easy-to-understand format. The terminal displays the generated information in a highly visible format to assist the user in acquiring knowledge.

[0652] This system is designed to facilitate access to specialized knowledge and provide diverse educational opportunities. Through it, learners can deepen their knowledge and grow together with other participants.

[0653] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0654] Step 1:

[0655] The user enters the information or question they want to know as an input prompt through the interface on the terminal. This input is in text format and may include specific technical questions. For example, they might enter a specific prompt such as, "Please tell me about the impact of climate change on biodiversity."

[0656] Step 2:

[0657] The terminal sends the prompts entered by the user to the server. This transmission is carried out through a secure communication protocol and serves to ensure that the user's input is properly delivered to the server. At this point, the input prompts are ready to be parsed by the server.

[0658] Step 3:

[0659] The server analyzes the received prompt and selects the most suitable generative intelligence model. At this stage, the server searches for relevant data based on the content of the prompt and applies the intelligence model to process the data. This generates an appropriate response to the prompt.

[0660] Step 4:

[0661] A generative intelligence model generates response data to prompts. The model extracts information relevant to the prompts from a vast dataset of educational data and constructs specific answers. This output data is in a format that contains information useful to the user.

[0662] Step 5:

[0663] The server sends the generated response to the terminal. This process is designed to efficiently compress the response data and send it quickly.

[0664] Step 6:

[0665] The terminal displays the received response data in a user-friendly format. This allows the user to easily view and understand the answers to their questions. The display helps the user decide how to use the information for their next steps.

[0666] (Application Example 1)

[0667] 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".

[0668] The present invention aims to provide a system that allows users to easily participate in a group of educational programs designed to deepen their specialized knowledge. It also aims to create an environment where users can acquire knowledge quickly and accurately using a generative intelligence model. In particular, it requires the visual presentation of specialized video content and the individualization of the user's learning experience to enable efficient understanding. Furthermore, means to facilitate the exchange of opinions among users and activate knowledge sharing are also important.

[0669] 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.

[0670] In this invention, the server includes means for enabling users to form and participate in educational groups, means for automatically responding to user questions using generative intelligence models specialized in each area of ​​expertise, means for outputting the generative intelligence responses and user-submitted information to an information display device, means for providing users with personalized educational information by allowing them to acquire specialized video content in real time using a visual display device, and means for facilitating the exchange of opinions with other users. This enables users to efficiently acquire specialized knowledge and accommodate diverse learning styles.

[0671] An "educational group" is a group intended for users to participate in or form, for the purpose of sharing specific specialized knowledge and information.

[0672] A "generative intelligence model" is an artificial intelligence technology that learns from vast amounts of data and can automatically generate responses to user questions.

[0673] An "information display device" is a device or software used to visually output knowledge and information to a user.

[0674] A "visual display device" is a device that allows users to visually receive video content in real time, enabling the provision of personalized educational information.

[0675] "Exchange of opinions" is a means of communication where users share knowledge and views with each other, thereby gaining new perspectives and understandings.

[0676] This system is designed to enable users to efficiently acquire specialized knowledge. First, the server provides an online platform for users to form and participate in educational groups of interest. This platform utilizes generative intelligence models specialized in each field, enabling them to automatically provide accurate responses to user questions. The generative intelligence models installed on the server perform advanced computational processing based on updated training datasets to appropriately respond to user inquiries.

[0677] Furthermore, the terminal is equipped with an information display device, which can output responses from generative intelligence and various user-submitted information in real time. In addition, through the visual display device, users can receive personalized educational video content and deepen their understanding visually.

[0678] Through this system, users can easily exchange opinions with other users and acquire new perspectives and knowledge. This interaction is an important function for deepening knowledge and bringing about diverse viewpoints. For example, by entering a prompt such as "Please tell me about the technology used to build the pyramids of ancient Egypt," users can obtain detailed information and engage in discussions based on it.

[0679] The entire system operates on devices such as smartphones and head-mounted displays, and generative intelligence is implemented using technologies like OpenAI GPT. This enables an intuitive and effective learning experience for the user.

[0680] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0681] Step 1:

[0682] Users access educational programs through their devices.

[0683] Input: User's areas of interest and registration information

[0684] Processing: Send user information to the server and execute a database query to search for relevant educational groups.

[0685] Output: A list of educational groups suitable for the user is displayed.

[0686] Step 2:

[0687] The server uses a generative intelligence model to generate a response based on the user's prompt.

[0688] Input: The prompt text entered by the user (e.g., "Please tell me about the technology used to build the pyramids of ancient Egypt.")

[0689] Processing: Analyze the prompt text and perform data calculations to generate relevant information using a generative AI model.

[0690] Output: The generated answer is sent to the device.

[0691] Step 3:

[0692] The terminal outputs generative intelligence responses to the user via an information display device.

[0693] Input: Response from a generative intelligence model

[0694] Processing: Structure the responses and display them in a visually easy-to-understand format on the interface.

[0695] Output: The user can view detailed information on the display device.

[0696] Step 4:

[0697] Users view videos through their devices and receive personalized educational information.

[0698] Input: Video information related to the content selected by the user.

[0699] Processing: Utilize visual display devices to stream or play video in real time.

[0700] Output: Images are presented to the user's vision, facilitating learning.

[0701] Step 5:

[0702] The server provides a platform to facilitate the exchange of opinions among users.

[0703] Input: User comments and opinions

[0704] Processing: Data is transmitted to distribute user-submitted information to other users in real time, providing a platform for discussion.

[0705] Output: Other users' opinions and comments are displayed, facilitating knowledge sharing.

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

[0707] This invention is a system that enhances the effectiveness of generative artificial intelligence models and learning communities by incorporating an emotion engine that recognizes user emotions. To realize this system, the following embodiments will be described.

[0708] The server features generative artificial intelligence models with expertise in various fields, as well as an emotion engine that analyzes user input and dialogue data to recognize emotions. This emotion engine uses natural language processing techniques and machine learning algorithms to determine the emotional state of the user from the text and actions they express.

[0709] The device is designed to allow users to easily engage in emotionally charged interactions. Emotional information recognized by the emotion engine is sent to a generative artificial intelligence model, which generates flexible responses tailored to the user's emotional state. This makes it possible to provide users with a more personalized learning experience.

[0710] For example, if a user inputs into the system, "My recent math assignments have been very difficult and I'm exhausted," the emotion engine analyzes this input and recognizes that the user is feeling tired or frustrated. The generative artificial intelligence model then responds in a gentle tone, such as, "It's okay to take a short break. When you find math problems difficult, try breaking them down into smaller tasks."

[0711] Furthermore, the emotion engine accumulates the user's emotional history and uses this to provide recommendations that more efficiently support their learning approach. The server utilizes this information to facilitate interaction with other users and work to improve the quality of learning across the entire community. It also plays a role in promoting empathetic interaction by visually displaying the emotional information of other participants to the user on their device.

[0712] In this way, the present invention aims to provide an interactive learning environment that goes beyond mere information provision and responds to emotions, thereby realizing a richer educational experience.

[0713] The following describes the processing flow.

[0714] Step 1:

[0715] Users access the learning community platform through their devices and take action to join new or existing communities. A user's participation request is sent to the server.

[0716] Step 2:

[0717] The server approves user participation requests and updates the database to keep community information and participant lists up-to-date. It also reviews the user's past sentiment data to prepare personalized support.

[0718] Step 3:

[0719] The user inputs questions to a generative artificial intelligence model on their device. During this process, the emotion engine monitors the user's language and choices, and analyzes their emotional state.

[0720] Step 4:

[0721] The emotion engine determines the emotional state (e.g., stress, excitement, sadness, etc.) obtained from the user's input and provides that information to a generative artificial intelligence model.

[0722] Step 5:

[0723] Generative artificial intelligence models generate responses tailored to the user's feelings based on emotional information received from an emotion engine. These responses take into account the user's learned data and current mental state.

[0724] Step 6:

[0725] The device displays the generated response to the user. The display format is customized to engage the user's interest and is designed to make it easier for the user to choose their next action based on their emotions.

[0726] Step 7:

[0727] Users can use the information they gain to continue learning and exchange opinions with other users within the community. They can also receive empathetic feedback from other users based on their emotions.

[0728] Step 8:

[0729] The server stores user feedback and communication history, using this data to improve the quality of future interactions. It also analyzes learning and emotional expression trends to help improve the system.

[0730] (Example 2)

[0731] 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".

[0732] In conventional learning support systems, information is provided without considering the user's emotional state, resulting in a uniform learning experience and making it difficult to provide optimal support tailored to individual users. Furthermore, the inability to obtain responses that reflect the user's emotions posed challenges in maintaining user motivation and improving learning efficiency.

[0733] 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.

[0734] In this invention, the server includes means for enabling users to create and participate in learning groups, means for automatically responding to user questions using generative artificial intelligence models specialized in each field, means for using an emotion engine to analyze user input data and identify emotional states, and means for displaying the responses of the generative artificial intelligence and responses based on the user's emotional state on a user interface. This enables users to receive a personalized learning experience tailored to their emotional state.

[0735] The term "user" refers to an individual or group of people who use a system to engage in learning activities.

[0736] A "learning group" refers to a community organized for educational purposes that users can belong to.

[0737] A "generative artificial intelligence model" refers to artificial intelligence technology that has learned knowledge in various fields in advance and has the ability to automatically answer user questions.

[0738] An "emotion engine" refers to a technology that analyzes user input data to identify their emotional state, utilizing natural language processing and machine learning algorithms.

[0739] "User interface" is a concept that refers to the screens and means of operation that allow a system and a user to interact with each other.

[0740] "Generative artificial intelligence response" refers to the answers or information provided by a generative artificial intelligence model based on user input.

[0741] "Emotional state" refers to a psychological or emotional condition detected based on the user's expressions and behavior.

[0742] A "database" refers to a collection of information that systematically stores and manages collected user registration information, learning history, and other data.

[0743] This invention is a system designed to provide users with a personalized learning experience, primarily achieved through the interplay of three parties: a server, a terminal, and the user, each fulfilling their respective roles.

[0744] The server plays a central role in this invention and is equipped with a generative artificial intelligence model and an emotion engine. The emotion engine uses natural language processing techniques and machine learning algorithms to analyze text data entered by the user and identify their emotional state. This allows the server to generate appropriate responses that take into account the user's psychological situation.

[0745] A terminal is a device used by users to interact with the system. It receives user input through the user interface and transmits that data to the server. It can also present responses from the server to the user visually or audibly.

[0746] Users can use this system to receive support for their learning tasks by inputting information. For example, based on specific input such as "My recent math assignments have been very difficult and I'm exhausted," they can receive responses that address their emotions.

[0747] As a concrete example, when a user inputs "tired" into the terminal, the server's emotion engine analyzes the input and identifies the emotion as "fatigue." In response, the generative artificial intelligence model generates advice such as "You should take a short break," which is then presented to the user through the terminal. Predefined prompt sentences are used to generate this response; for example, a specific prompt sentence such as "What suggestion should be made when a user feels tired?" is used.

[0748] In this way, the present invention realizes flexible and effective learning support that is tailored to the user's emotions.

[0749] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0750] Step 1:

[0751] The terminal receives user input. This input can be in text or voice format; for example, it can receive a message such as, "My recent math assignments were very difficult and I'm exhausted." This input is then sent to the server as digital data.

[0752] Step 2:

[0753] The server passes user input sent from the terminal to the emotion engine. The emotion engine uses natural language processing technology to analyze the received text data. This analysis determines the user's emotional state and identifies emotions such as "fatigue" or "frustration." This emotional information becomes the output of the emotion engine.

[0754] Step 3:

[0755] The server sends the emotional information received from the emotion engine to the generative AI model. The generative AI model receives the emotional information as input and generates responses as instructed by the prompt. For example, the prompt might provide an indicator such as "gentle advice when the user is tired," and based on this information, it generates a response such as "Try taking a short break." This is the output of the generative AI model.

[0756] Step 4:

[0757] The server sends the response obtained from the generated AI model to the terminal. The terminal presents this response to the user visually or audibly, providing the user with an emotionally relevant response. Specifically, this may involve text being displayed on the user's screen or the message being read aloud using speech synthesis.

[0758] Step 5:

[0759] The server further records and stores the user's emotions and response history in a database. This information will be used to personalize future learning experiences. In this process, historical data is processed and saved to the database as new entries.

[0760] (Application Example 2)

[0761] 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".

[0762] In recent years, recognizing user emotions and providing more personalized responses has become increasingly important for making interactions within data processing sets more meaningful. However, conventional technologies lack the ability to generate responses that take into account the user's emotional state, making it difficult to improve engagement within data processing sets. Solving this problem is crucial.

[0763] 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.

[0764] In this invention, the server includes means for enabling users to create and participate in data processing sets, means for automatically responding to user questions using generative intelligence models specialized in each field, means for displaying the generative intelligence responses and user information on the user screen, and means for an emotion mechanism that recognizes diverse emotional states and adjusts responses based on the user's emotional information. This enables flexible responses in accordance with the user's emotions and improves the quality of communication within the data processing set.

[0765] A "data processing set" is a virtual environment where users can participate and exchange information, serving as a platform for sharing knowledge and information from different specialized fields.

[0766] A "generative intelligence model" is a type of artificial intelligence that learns from a specific dataset and has the ability to generate responses to a variety of questions.

[0767] A "user interface" is a visual display that allows users to interact with the system through an interface, and enables the input and output of information.

[0768] An "emotional mechanism" is a technical means of analyzing and determining emotions from user input and behavior, thereby enabling an understanding of the user's emotional state.

[0769] A "recording device" is a database or other storage medium that securely stores a user's personal information and history, and allows them to be retrieved as needed.

[0770] "User verification" is a process that verifies the user's identity and authority, and is a means of ensuring safe and reliable access.

[0771] "Empathic interaction" refers to interactions that promote the sharing and understanding of emotions among users, and is an activity aimed at deepening relationships within a data processing set.

[0772] The system for implementing the present invention mainly consists of three components: a server, a terminal, and a user. The server is equipped with a generative intelligence model and has an emotion mechanism built in to analyze the user's input information and emotional state. The emotion mechanism uses natural language processing technology and machine learning algorithms to process text and behavioral data input by the user and to determine the user's emotional state. Dedicated software such as TensorFlow and NLTK is used for this processing.

[0773] The terminal is a device for users to participate in a data processing set and exchange information through a user interface, and smartphones and tablets are examples of such devices. User interactions are displayed on this interface, and responses generated by generative intelligence models are also displayed visually.

[0774] As a concrete example, if a customer in a physical store says, "I'm having trouble finding a staff member," this statement is captured by the terminal and sent as text data to the server. The emotion mechanism on the server analyzes the statement and detects the customer's confusion and frustration. Based on this emotion recognition, the generative intelligence model immediately provides the terminal with an appropriate response, such as, "A staff member will be with you shortly. Please wait a moment." This allows the customer to receive prompt and accurate service.

[0775] An example of a prompt message input to a generative AI model might be, "The customer seems stressed. What is the best course of action?" This allows the generative intelligence model to provide personalized responses that respond to the user's emotions.

[0776] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0777] Step 1:

[0778] The user initiates voice input into the device. The device uses a speech recognition system to convert the voice data into text data. This text data is then sent to the server. The input is voice data, and the output is text data. A service such as the Google Speech-to-Text API is used for speech recognition.

[0779] Step 2:

[0780] The server inputs the received text data into the sentiment mechanism. The sentiment mechanism uses natural language processing algorithms to analyze the sentiment attributes of the text. As a result of the analysis, the user's emotional state (e.g., confused, irritated) is output. This process utilizes NLTK and sentiment analysis libraries.

[0781] Step 3:

[0782] The server inputs the emotional state and original text data into a generative AI model. The generative AI model generates the optimal response based on this data. The content of the response (e.g., "A staff member will be with you shortly. Please wait a moment.") is output. TensorFlow or PyTorch are used to operate the generative AI model.

[0783] Step 4:

[0784] The server sends the generated response to the terminal. The terminal displays the received response to the user visually or audibly. The user decides on the next action based on this response. In this step, the user can verify whether the response is appropriate.

[0785] 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.

[0786] 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.

[0787] 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.

[0788] 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.

[0789] 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.

[0790] 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.

[0791] 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.

[0792] 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.

[0793] 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."

[0794] 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.

[0795] 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.

[0796] 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.

[0797] 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.

[0798] 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.

[0799] 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.

[0800] 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.

[0801] 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.

[0802] 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.

[0803] 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.

[0804] 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.

[0805] 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.

[0806] The following is further disclosed regarding the embodiments described above.

[0807] (Claim 1)

[0808] A means to enable users to create and participate in learning communities,

[0809] A means of automatically responding to user questions using generative artificial intelligence models specialized in each field,

[0810] A means for displaying the responses of generative artificial intelligence and user-submitted information on the user interface,

[0811] A system that includes this.

[0812] (Claim 2)

[0813] The system according to claim 1, comprising means for periodically updating the training dataset of a generative artificial intelligence model and improving the accuracy of the model.

[0814] (Claim 3)

[0815] The system according to claim 1, comprising means for storing user registration information and learning history in a database and for performing user authentication.

[0816] "Example 1"

[0817] (Claim 1)

[0818] A device that enables learners to create and participate in learning groups,

[0819] A device that automatically responds to learners' questions using generative intelligence models specialized in each field,

[0820] A device that displays generative intelligence responses and learner submissions on a display unit,

[0821] A device for recording and managing group activities utilizing generative intelligence,

[0822] An information processing system that includes this.

[0823] (Claim 2)

[0824] The information processing system according to claim 1, comprising a device for periodically updating a set of educational data for a generative intelligence model and improving the accuracy of the model.

[0825] (Claim 3)

[0826] The information processing system according to claim 1, comprising a device that stores learner registration information and learning history in a storage unit and performs learner authentication.

[0827] "Application Example 1"

[0828] (Claim 1)

[0829] Means that enable users to form and participate in educational groups,

[0830] A means of automatically responding to user questions using generative intelligence models specialized in each field,

[0831] Means for outputting the response of generative intelligence and user-submitted information to an information display device,

[0832] A means of providing personalized educational information to users by allowing them to acquire specialized video content in real time using a visual display device,

[0833] A means to facilitate the exchange of opinions with other users,

[0834] A system that includes this.

[0835] (Claim 2)

[0836] The system according to claim 1, comprising means for periodically updating the training information set of a generative intelligence model and improving the accuracy of the model.

[0837] (Claim 3)

[0838] The system according to claim 1, comprising means for storing user registration information and learning history in an information recording device and for performing user authentication.

[0839] "Example 2 of combining an emotion engine"

[0840] (Claim 1)

[0841] A means to enable users to create and join learning groups,

[0842] A means of automatically responding to user questions using generative artificial intelligence models specialized in each field,

[0843] A means of using an emotion engine to analyze user input data and identify emotional states,

[0844] A means for displaying responses from generative artificial intelligence and responses based on the user's emotional state on the user interface,

[0845] A system that includes this.

[0846] (Claim 2)

[0847] The system according to claim 1, comprising means for periodically updating the training dataset of a generative artificial intelligence model and improving the accuracy of the model.

[0848] (Claim 3)

[0849] The system according to claim 1, comprising means for storing user registration information, emotion history, and learning history in a database and for authenticating the user.

[0850] "Application example 2 when combining with an emotional engine"

[0851] (Claim 1)

[0852] Means that enable users to create and participate in data processing sets,

[0853] A means of automatically responding to user questions using generative intelligence models specialized in each field,

[0854] A means for displaying the responses of generative intelligence and information from the user on the user screen,

[0855] It is equipped with an emotional mechanism that recognizes diverse emotional states and means for adjusting responses based on the user's emotional information,

[0856] A system that includes this.

[0857] (Claim 2)

[0858] The system according to claim 1, comprising means for periodically updating the training data set of a generative intelligence model to improve the model's accuracy, and further comprising means for accumulating user emotional information and recommending the optimal learning method based on the emotional history.

[0859] (Claim 3)

[0860] The system according to claim 1, comprising means for storing user registration information and data processing history in a recording device, for verifying users, and further comprising means for visually displaying emotional information of other participants in order to promote empathetic interaction among users. [Explanation of Symbols]

[0861] 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 to enable users to create and participate in learning communities, A means of automatically responding to user questions using generative artificial intelligence models specialized in each field, A means for displaying the responses of generative artificial intelligence and user-submitted information on the user interface, A system that includes this.

2. The system according to claim 1, comprising means for periodically updating the training dataset of a generative artificial intelligence model and improving the accuracy of the model.

3. The system according to claim 1, comprising means for storing user registration information and learning history in a database and for performing user authentication.

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