system

Abstract

We provide the system. [Solution] A user input / output means that receives user input and initiates dialogue, A dialogue control means that analyzes the user's input and identifies related knowledge based on the generated information, A knowledge-providing means that provides the user with the identified knowledge and supports their understanding of the generated AI, A learning support means that improves the performance of a machine learning model using information collected through the aforementioned interaction with the user, Information processing means that personalize information based on the user's history and interests, A system that includes this.

Description

Technical Field

[0004] , , , ,

[0005] , , , , ,

[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, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] The present invention relates to a system for providing information related to generative AI technology, and particularly aims to solve the problem of effectively eliminating the difficulties and lack of understanding that users have in learning support using generative AI. At present, due to the complexity and specialization in each technical field of generative AI, there is a problem that it is difficult for general users to easily use and understand. Therefore, there is a demand for a system that can provide users with appropriate information according to their needs and deepen their understanding of generative AI technology.

Means for Solving the Problems

[0005] The present invention provides a system that includes a user interface means for receiving user input and initiating a dialogue, a dialogue management means for analyzing the user input and identifying relevant information based on the generated data, an information provision means for providing the identified information to the user and supporting their understanding of the generative AI, and a learning means for improving the performance of a machine learning model using data collected through dialogue with the user. This system has a function to identify the user's areas of weakness and optimize the dialogue strategy based on this. Furthermore, it has template information related to multiple generative AI technologies and can adaptively select one according to the user's input, thereby enabling the provision of appropriate and effective information to the user.

[0006] "User interface means" refers to a function that provides a screen or device for the user to input information and initiate interaction.

[0007] A "dialogue management means" is a function within a system that analyzes user input and identifies related information.

[0008] "Information provision means" refers to functions that provide identified information to users in an easy-to-understand manner and support their understanding of the generated AI.

[0009] "Learning methods" refer to functions that utilize data collected through interaction with users to improve the performance of machine learning models.

[0010] "Identifying areas of weakness" means identifying areas of generative AI technology that users find difficult to understand or are not proficient in.

[0011] "Optimizing dialogue strategies" means adjusting methods to effectively conduct dialogue based on the user's identified areas of weakness.

[0012] "Template information" refers to pre-prepared patterns and examples of information related to generative AI technology, which are adaptively selected according to the user's input. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

[0021] [First Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0034] This invention describes an implementation in which it is carried out as an interactive system to make it easier for the user to understand information related to the generated AI. This system consists of multiple computer programs and operates through the interaction of a server, a terminal, and the user.

[0035] The server first receives user input from the terminal. User input is text data such as questions or points of confusion regarding the generated AI. The server passes this received data to the dialogue management module, where it analyzes the input content using natural language processing. In this analysis, the module specifically identifies the user's areas of difficulty and extracts related information. In doing so, the dialogue management module considers the user's historical interactions to learn their areas of difficulty and needs.

[0036] Next, an information provision module operates within the server and generates appropriate information based on the analysis results. This information includes a basic explanation of the generating AI and answers to specific questions the user is particularly seeking. The server sends this generated information to the terminal, which then presents the information to the user. The presentation method is primarily text format, but links to images and videos are also provided as needed.

[0037] When the user views the presented information and asks a new question, that input is sent back to the server, and the dialogue management module repeats the analysis and information provision process. Through this process, the user can gradually deepen their understanding of the generative AI.

[0038] For example, if a user asks, "How do I use image generation AI?", the server's dialogue management module analyzes this question, and the information provision module generates information such as, "Image generation AI is used in many applications. For example, it can be used to convert photos into art styles or generate new images." This allows users to deepen their understanding of the technology through concrete use cases.

[0039] Furthermore, this system utilizes data collected through interaction with the user to update the machine learning model using the server's learning module. This update enables the provision of more precise information in subsequent interactions, improving the overall response quality of the system. This feedback loop ensures the learning effect of this invention.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The device displays a screen that accepts user input. The user enters a question about the generated AI and presses the submit button.

[0043] Step 2:

[0044] The server receives user input sent from the terminal. The input is basically treated as text data.

[0045] Step 3:

[0046] The server's dialogue management module analyzes the user's input. Here, natural language processing techniques are used to identify the user's intentions and areas of difficulty from the input.

[0047] Step 4:

[0048] The server's information module generates appropriate answers to the user's questions based on the analysis results. The generated information includes relevant explanations and specific usage examples.

[0049] Step 5:

[0050] The server sends the generated information to the terminal.

[0051] Step 6:

[0052] The device displays information it has received to the user. This display may include links to relevant images and videos, as needed.

[0053] Step 7:

[0054] After the user views the presented information, they will enter their information again if they have new questions or require further information. This input will then be processed again starting from step 2.

[0055] Step 8:

[0056] The server's learning module analyzes the data collected through interactions with the user and updates the machine learning model. This update improves the accuracy of information provided in subsequent interactions.

[0057] (Example 1)

[0058] 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."

[0059] With the advancement of modern generative AI technology, it is becoming increasingly important for ordinary users to effectively understand and utilize advanced AI technologies. However, users' understanding of generative AI depends on their individual experience and knowledge, and providing information tailored to each user's specific needs is necessary to deepen that understanding. Furthermore, continuous learning and feedback are crucial to improving the accuracy of responses in such dialogue systems.

[0060] 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.

[0061] In this invention, the server includes an input device that receives user input and initiates a dialogue, a dialogue management device that analyzes the user input and identifies relevant information, and an information provision device that provides the identified information to the user and supports their understanding. This enables the provision of information tailored to the needs of individual users, promotes a deeper understanding of the generated AI by the user, and improves the overall response quality of the system through continuous model updates by the learning device.

[0062] An "input device" is a device that receives input from the user and initiates a dialogue.

[0063] A "dialogue management device" is a device that analyzes user input and identifies relevant information based on the generated data.

[0064] An "information provision device" is a device that provides identified information to the user and supports their understanding of the generated AI.

[0065] A "learning device" is a device that improves the performance of a machine learning model using data collected through interaction with the user.

[0066] A "communication device" is a device that transmits data from an input device to a server device and receives data from a server device to an input device.

[0067] "Generative AI technology" is a form of artificial intelligence technology that creates information and content for specific purposes.

[0068] "Historical dialogue data" refers to data that records a user's past interactions and is used to identify areas of difficulty and interests for the user.

[0069] To implement this invention, it is necessary to construct a system in which a server, terminal, and user work in cooperation. A specific example of such a system is shown below.

[0070] First, the server includes an input device, a dialogue management device, an information provision device, a learning device, and a communication device. Ideally, this server should be implemented on a computer with high processing power. The software implemented on the server includes libraries for natural language processing and machine learning frameworks. This allows the server to analyze prompt sentences sent by the user and generate relevant information. A concrete example of this prompt sentence is "How do I use image generation AI?". From this input, the server provides specific usage examples and basic explanations of the generation AI.

[0071] Next, the terminal is responsible for receiving user input and sending it to the server. Terminals are implemented in devices such as personal computers, smartphones, and tablets. These devices are equipped with user interfaces to facilitate interaction with the user. The terminal also presents the information received from the server to the user. The presented information is primarily in text format, but can include image and video links as needed.

[0072] The user interacts with the system through a terminal, inputting questions and points of confusion about the generative AI as prompts. This iterative process allows the user to gradually deepen their knowledge of the generative AI. Based on the user's input, the server collects the interaction data and processes it in a learning device. This allows the system to continuously improve the accuracy of its responses.

[0073] By implementing this approach, it becomes possible to provide information optimized for individual users and to realize a system that supports understanding of the generated AI.

[0074] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0075] Step 1:

[0076] The user enters questions about the image generation AI using the terminal's user interface. These questions are in text format, such as, "How do I use the image generation AI?" The entered data is then sent to the server by the terminal.

[0077] Step 2:

[0078] The terminal sends the prompt text entered by the user as digital data to the server. The input reaches the server using the HTTP protocol. Data encryption technology (SSL / TLS) is used during this communication process to ensure the security of the transmitted content.

[0079] Step 3:

[0080] The server passes the received prompt text to the dialogue management device, where it is analyzed using natural language processing. The input here is the user's question text, and the output is the analysis result identifying the user's intent and areas of interest. This analysis utilizes morphological analysis techniques to extract keywords from the text.

[0081] Step 4:

[0082] The information provider within the server generates appropriate information based on the analysis results of the dialogue management device. Specifically, it combines knowledge of usage examples and generation AI technology to generate information most relevant to the user's question. Using AI technology, it creates content such as, for example, "An example of using image generation AI is a service that transforms photos into art."

[0083] Step 5:

[0084] The server sends the generated information to the terminal. This includes a text message and related links as response data. This information is also encrypted using SSL / TLS, ensuring security when transmitted to the terminal.

[0085] Step 6:

[0086] The terminal displays information received from the server to the user. The displayed content is in text format, and relevant images and links can be displayed as needed. The information is provided intuitively and clearly through the user interface.

[0087] Step 7:

[0088] Users can review and deepen their understanding of the presented information. If they want to know more, they can enter a new prompt, and the cycle continues from step 1. This allows users to gradually deepen their understanding of the generated AI.

[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] Understanding the complex information and diverse technologies related to generative AI is difficult for the average user, hindering effective learning and application. Furthermore, the lack of appropriate information tailored to each user's interests and level of understanding hinders the effective use of this information.

[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 user input / output means, dialogue control means, knowledge provision means, learning support means, and information processing means. This enables the provision of information based on the individual needs and interests of users, and facilitates a deep understanding of and effective utilization of generative AI technology.

[0094] "User input / output means" refers to a device or system that has the function of receiving user input and initiating dialogue.

[0095] "Dialogue control means" refers to a device or system that has a control function for analyzing user input and identifying relevant knowledge based on the generated information.

[0096] A "knowledge provision means" is a device or system that has the function of providing specific knowledge to the user and supporting their understanding of generated AI.

[0097] A "learning support device" is a device or system that has auxiliary functions to improve the performance of a machine learning model using information collected through interaction with the user.

[0098] "Information processing means" refers to a device or system that has processing functions for personalizing information based on the user's history and interests.

[0099] This invention realizes a system for providing users with information related to generative AI technology using a server and a terminal. The server receives user input through user input / output means and uses dialogue control means to analyze the content of that input. This dialogue control means uses natural language processing libraries such as spaCy and NLTK to analyze the input text and identify relevant knowledge. Then, knowledge provision means provides the generated knowledge to the user in an easy-to-understand format. In this process, generative AI libraries such as OpenAI® and GPT-3® are utilized to dynamically generate information tailored to the user's interests.

[0100] Furthermore, the device utilizes information processing means to present personalized information to the user. This information processing means personalizes information based on the user's history and interests, taking into account their history, and highlights particularly important content. Through this process, the device deepens its understanding of the user, and the collected information is used to improve the performance of machine learning models using learning support means.

[0101] For example, if a user asks a question such as, "Please tell me about the latest generative AI technologies," this system will use GPT-3 to provide a response in the format of, "Currently, image generation technology and natural language processing technology are evolving, and there are many application examples." An example of a prompt would be, "Please list the latest generative AI technologies and explain the features of each in an easy-to-understand way." In this way, users can obtain information that suits their interests and needs, and gain a deeper understanding of generative AI technology.

[0102] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0103] Step 1:

[0104] The user enters a question related to the generating AI in text format via their device. The entered text is collected by the device and sent to the server. At this point, the input is the user's question text.

[0105] Step 2:

[0106] The server analyzes the received text data using natural language processing libraries such as spaCy and NLTK. The main purpose of the analysis is to identify the user's areas of interest, and as a result, topics of interest are output. Specifically, keyword extraction and contextual analysis are performed.

[0107] Step 3:

[0108] Based on the analysis results, the server uses generative AI libraries such as OpenAI GPT-3 to generate responses tailored to the user's interests. The input in this process is the identified topic, and the output generates information and knowledge to be provided to the user. Specifically, this includes dialogue generation and information completion.

[0109] Step 4:

[0110] The server sends the generated information to the terminal. The terminal receives this information and displays it in a format that is easy for the user to understand. This display format may include text, images, and visual data. The terminal processes this information and provides guides and hints to help the user navigate the interaction smoothly.

[0111] Step 5:

[0112] The terminal receives additional questions and feedback from the user and sends them to the server. The server uses this feedback to improve the performance of the machine learning model using learning aids. The input in this step is new user feedback, and the output is an updated model that enables more accurate response generation.

[0113] 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.

[0114] This invention enables more personalized information delivery by incorporating an emotion engine into a system that supports the understanding of generative AI technology through interaction with the user. This system is implemented in a form in which a server, a terminal, and the user interact with each other.

[0115] The terminal provides an interface that accepts text or voice input from the user. When the user inputs a question about the generated AI, the data is sent to the server. The server uses a dialogue management module to analyze the input and identify the user's intended question and areas of difficulty. In this invention, an emotion engine is further used to recognize the user's emotions contained in the input data.

[0116] The server's emotion engine determines the user's emotional state by analyzing facial expressions from text, audio, or video. For example, it analyzes specific vocabulary and punctuation in the user's text input, as well as their tone and intonation, and changes in facial expressions to identify emotions such as anxiety, excitement, or confusion.

[0117] Based on the user's emotional state, the server's information module generates a response that takes this information into account. In addition to a standard response, the information module adds easing language and reassuring, simple explanations. For example, if the system analyzes that the user is confused, it will provide an explanation that includes reassuring language such as, "Don't worry, let me explain in more detail."

[0118] The generated response is sent back to the terminal and presented to the user. By adaptively responding to the user's emotions in this way, more effective information can be provided and user satisfaction can be improved. Furthermore, in order to utilize the user's feedback in the next interaction, the server continuously stores the interaction results and the user's emotion data in a learning module and updates the model.

[0119] Through these means, a system can be built that integrates emotion recognition and generative AI knowledge provision, making it possible to provide users with a more personalized experience.

[0120] The following describes the processing flow.

[0121] Step 1:

[0122] The device provides the user with an interface, prompting them to input questions or concerns about the generated AI. The user completes their input and presses the submit button.

[0123] Step 2:

[0124] The device sends the entered text or voice data to the server. This data is received by the server for later analysis.

[0125] Step 3:

[0126] The server receives user input and passes it to the dialogue management module, which uses natural language processing algorithms to analyze the text or speech. The purpose of the analysis is to identify the user's intentions, the content of their questions, and areas of difficulty.

[0127] Step 4:

[0128] The server's emotion engine further analyzes the user's input data to recognize their emotional state. Based on text vocabulary and punctuation, voice tone, and even the user's facial expressions derived from the text data, it identifies specific emotions the user is experiencing (e.g., confusion, anxiety, joy).

[0129] Step 5:

[0130] The server's information module generates the optimal response for the user based on the analyzed intent and emotional state. The response includes adjustments according to the emotional state and is composed of reassuring language and simple explanations.

[0131] Step 6:

[0132] The server sends the generated response to the terminal. The terminal displays this information to the user and, if necessary, also provides relevant visual information (e.g., images or video links).

[0133] Step 7:

[0134] Users can view the presented information and enter further questions or feedback. Any new input from the user is processed again through the process starting from step 2.

[0135] Step 8:

[0136] The server's learning module re-evaluates the data and emotional states collected during the interaction and updates the machine learning model. Through this update, the system's accuracy is improved so that it can provide more precise information and emotional responses in subsequent interactions.

[0137] (Example 2)

[0138] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0139] Conventional interactive systems have struggled to appropriately recognize users' emotions and provide information tailored to their individual needs. Furthermore, they have been unable to employ effective dialogue strategies for each user's different areas of difficulty, making it challenging to deepen user understanding. The challenge lies in resolving these issues and providing more personalized information.

[0140] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0141] In this invention, the server includes an integrated analysis means for analyzing user input and recognizing emotions, a response generation means for generating and providing relevant information based on the user's emotional state, and an analysis device for accumulating data collected through interaction with the user and using it to improve a learning model. This makes it possible to provide personalized information that is tailored to the user's emotions and individual needs.

[0142] A "communication device" is a device that receives input from a user and provides an interface for initiating a conversation.

[0143] An "integrated analysis means" is a function within the system that analyzes the user's input, performs emotion recognition, and identifies related information.

[0144] The "response generation means" is a function that generates information to be provided to the user based on the analyzed emotional state of the user.

[0145] An "analysis device" is a device that accumulates data obtained from interactions with users and uses that data to perform a process of improving the learning model.

[0146] "Emotional state" refers to a psychological state inferred from the user's input, and includes anxiety, excitement, confusion, etc.

[0147] A "learning model" is an algorithm or model that is updated using collected data to improve the accuracy and adaptability of subsequent interactions.

[0148] "Template information" is a set of predefined information based on multiple knowledge generation techniques that forms the basis for response generation.

[0149] A "dialogue strategy" refers to a method of conducting a dialogue that is optimized according to the user's areas of difficulty and emotional state.

[0150] This invention constitutes a system that provides personalized information through dialogue with the user, and aims to generate responses that take into account the user's emotional state. The following describes a specific form for implementing this system.

[0151] The server functions as an integrated analysis tool, analyzing the user's input data. This input data consists of text and audio data provided by the user via a terminal. The terminal is equipped with a microphone to convert the user's voice into a digital signal and a user interface for inputting text data. The dialogue begins when the user inputs the question, "How does the generative AI understand emotions?" into the terminal.

[0152] Input data sent from the terminal to the server is analyzed by an integrated analysis system within the server. The server uses natural language processing technology to identify the intent of the input and then uses an emotion engine to recognize the user's emotional state. Emotion recognition includes keyword extraction from text and tone analysis in the case of audio data. For example, vocabulary such as "difficult" or "I don't understand" in the user's questions, as well as emotions such as confusion or anxiety, can be detected from the intonation of the voice.

[0153] Based on the analysis results, the server's response generation mechanism generates information based on the user's emotional state. This response includes expressions designed to help the user relax. For example, if a user inputs, "The mechanism of the generating AI is complex and I don't understand it," the server will generate something like, "Don't worry. I'll explain it in an easy-to-understand way."

[0154] The generated response is sent from the server to the terminal, which then displays it as text or outputs it as audio to the user. In this way, the dialogue progresses through the provision of appropriate information based on emotion recognition, supporting a deeper understanding of the user.

[0155] Furthermore, the server stores the dialogue results in an analysis device and uses machine learning models to improve the accuracy of future dialogues. This enables the system to continuously improve and adapt, providing users with a higher level of personalized experience.

[0156] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0157] Step 1:

[0158] The device accepts text or voice input from the user. Specifically, the user enters a prompt such as, "How will the Generating AI answer my question?" This input is captured as digital data by the device and prepared to be sent to the server.

[0159] Step 2:

[0160] The terminal transmits the received input data to the server. The input data securely reaches the server's integrated analysis system via network communication. The input here is digitized text or audio data, which is transferred directly to the server.

[0161] Step 3:

[0162] The server uses integrated analysis tools to analyze the user's input data. During this process, natural language processing techniques are used to analyze the grammar and meaning of the text and identify the user's intent. The input string data is analyzed, and an output is obtained that identifies an "explanation based on the AI's response method."

[0163] Step 4:

[0164] The server uses an emotion engine to recognize the user's emotional state. It analyzes emotions from word choices in text and tone of voice. The input here is a specific part of the sentence analyzed in the previous step, and the output is a judgment such as "User's emotional state: confused, interested."

[0165] Step 5:

[0166] The server's response generation mechanism generates an appropriate response based on the identified intent and emotional state. It includes reassuring sentences to make the response easy for the user to understand. The input uses analysis results and emotional data, and the output is a response such as, "Don't worry. The generating AI has analyzed your question and will provide the most relevant information."

[0167] Step 6:

[0168] The server sends the generated response to the terminal. The response data is encoded in a digital format and sent to the terminal via network communication.

[0169] Step 7:

[0170] The terminal displays the response received from the server to the user. Specifically, it is displayed on the screen in text format or played back through the speaker using speech synthesis. This output allows the user to confirm the response from the server.

[0171] Step 8:

[0172] The server stores the results of user interactions in an analysis device and updates the learning model to utilize them in future interactions. This data accumulation and model updating form a feedback loop to improve the quality of subsequent interactions.

[0173] (Application Example 2)

[0174] 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".

[0175] In today's information society, generative AI technology is becoming mainstream, but users need specialized knowledge to fully understand and utilize these technologies. Furthermore, there is a lack of adequate support for the anxiety and confusion that users may experience when using generative AI, highlighting the need for user-friendly systems.

[0176] 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.

[0177] In this invention, the server includes emotion recognition means for analyzing the user's emotions and adjusting the response content based on the emotional state; reassurance providing means for presenting additional information to provide reassurance when the user feels anxious or confused; and dialogue control means for analyzing the user's input and identifying related information based on the generated data. As a result, the user receives responses that take emotions into consideration, deepens their understanding of the generation AI technology, and enables them to use the system with peace of mind.

[0178] A "user interface means" is an interface device that has the function of receiving input from the user and initiating a dialogue.

[0179] A "dialogue control means" is a control device that analyzes the user's input and identifies relevant information based on the generated data.

[0180] An "information provision device" is a device that provides identified information to the user and has the function of supporting understanding of the generated AI.

[0181] An "emotion recognition device" is a device equipped with the function of analyzing the user's emotions and adjusting the response content based on the emotional state.

[0182] A "learning tool" is a device that uses data collected through interaction with the user to improve the performance of a machine learning model.

[0183] A "means of providing reassurance" is a device that has the function of presenting additional information to provide reassurance when the user feels anxious or confused.

[0184] A "generative AI model" is a form of artificial intelligence used to generate responses or information for specific tasks.

[0185] A "prompt message" is a phrase that, when input into a generation AI, is used to generate appropriate responses or information.

[0186] The system for realizing this invention is a conversational AI assistant that integrates emotion recognition technology and is specifically designed for application in the field of electronic payments. The system's terminal primarily uses a smartphone as its hardware. The terminal receives voice and text input from the user and sends this data to a server for analysis.

[0187] On the server side, a program built using Python analyzes the input data. Specifically, it uses the OpenCV and PyDub libraries to analyze the tone of the audio data and the content of the text, extracting facial and audio features. Here, the user's emotional state is determined by an emotion recognition system. This emotional information then influences the response generated by the system.

[0188] The generative AI model is implemented using the Hugging Face Transformers library and generates responses based on user input and emotional state prompts. An example prompt is, "How would you reassure a user if they are surprised?" The server analyzes the generated responses and, through reassurance mechanisms, returns appropriate messages when the user feels anxious. Through this system, users can make electronic payments smoothly and with peace of mind.

[0189] For example, when a user makes a large payment, the system sends a reassuring message such as, "We have confirmed that this transaction is secure. You can cancel it at any time, so please rest assured." As a result, users can use electronic payments with greater confidence.

[0190] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0191] Step 1:

[0192] The server receives voice and text input from the user sent from the terminal. This input includes voice files and text data. This data is used as foundational information to analyze the user's emotional state and intentions.

[0193] Step 2:

[0194] The server inputs received audio and text data into an emotion recognition system to analyze the user's emotional state. Audio data undergoes tone analysis using PyDub, and facial features are analyzed using OpenCV to infer emotions such as joy or anxiety. The output is a quantitative evaluation indicating the user's emotions.

[0195] Step 3:

[0196] The server inputs text data into the dialogue control system to analyze the user's intent and requests. Using natural language processing technology, the analyzed information is constructed as prompt sentences for a generative AI model. The output is a prompt sentence that includes the user's intent.

[0197] Step 4:

[0198] The server's AI model generates the optimal response using the sentiment evaluation from step 2 and the prompt text from step 3. Using the Hugging Face Transformers library, a contextually adjusted response is generated based on the data input. The output is the final response text to be returned to the user.

[0199] Step 5:

[0200] The server adjusts the generated response message through a reassurance-providing mechanism and adds additional reassurance messages as needed. This provides information that alleviates the user's anxiety and provides a sense of security. The output is the final response message to the user.

[0201] Step 6:

[0202] The user's device receives the final response message and presents it to the user in either audio or text format. The user receives a reassuring response, allowing them to confidently proceed with electronic payments and the use of generative AI technology.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] [Second Embodiment]

[0207] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0208] 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.

[0209] 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).

[0210] 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.

[0211] 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.

[0212] 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).

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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".

[0219] This invention describes an implementation in which it is carried out as an interactive system to make it easier for the user to understand information related to the generated AI. This system consists of multiple computer programs and operates through the interaction of a server, a terminal, and the user.

[0220] The server first receives user input from the terminal. User input is text data such as questions or points of confusion regarding the generated AI. The server passes this received data to the dialogue management module, where it analyzes the input content using natural language processing. In this analysis, the module specifically identifies the user's areas of difficulty and extracts related information. In doing so, the dialogue management module considers the user's historical interactions to learn their areas of difficulty and needs.

[0221] Next, an information provision module operates within the server and generates appropriate information based on the analysis results. This information includes a basic explanation of the generating AI and answers to specific questions the user is particularly seeking. The server sends this generated information to the terminal, which then presents the information to the user. The presentation method is primarily text format, but links to images and videos are also provided as needed.

[0222] When the user views the presented information and asks a new question, that input is sent back to the server, and the dialogue management module repeats the analysis and information provision process. Through this process, the user can gradually deepen their understanding of the generative AI.

[0223] For example, if a user asks, "How do I use image generation AI?", the server's dialogue management module analyzes this question, and the information provision module generates information such as, "Image generation AI is used in many applications. For example, it can be used to convert photos into art styles or generate new images." This allows users to deepen their understanding of the technology through concrete use cases.

[0224] Furthermore, this system utilizes data collected through interaction with the user to update the machine learning model using the server's learning module. This update enables the provision of more precise information in subsequent interactions, improving the overall response quality of the system. This feedback loop ensures the learning effect of this invention.

[0225] The following describes the processing flow.

[0226] Step 1:

[0227] The device displays a screen that accepts user input. The user enters a question about the generated AI and presses the submit button.

[0228] Step 2:

[0229] The server receives user input sent from the terminal. The input is basically treated as text data.

[0230] Step 3:

[0231] The server's dialogue management module analyzes the user's input. Here, natural language processing techniques are used to identify the user's intentions and areas of difficulty from the input.

[0232] Step 4:

[0233] The server's information module generates appropriate answers to the user's questions based on the analysis results. The generated information includes relevant explanations and specific usage examples.

[0234] Step 5:

[0235] The server sends the generated information to the terminal.

[0236] Step 6:

[0237] The device displays information it has received to the user. This display may include links to relevant images and videos, as needed.

[0238] Step 7:

[0239] After the user views the presented information, they will enter their information again if they have new questions or require further information. This input will then be processed again starting from step 2.

[0240] Step 8:

[0241] The server's learning module analyzes the data collected through interactions with the user and updates the machine learning model. This update improves the accuracy of information provided in subsequent interactions.

[0242] (Example 1)

[0243] 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."

[0244] With the advancement of modern generative AI technology, it is becoming increasingly important for ordinary users to effectively understand and utilize advanced AI technologies. However, users' understanding of generative AI depends on their individual experience and knowledge, and providing information tailored to each user's specific needs is necessary to deepen that understanding. Furthermore, continuous learning and feedback are crucial to improving the accuracy of responses in such dialogue systems.

[0245] 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.

[0246] In this invention, the server includes an input device that receives user input and initiates a dialogue, a dialogue management device that analyzes the user input and identifies relevant information, and an information provision device that provides the identified information to the user and supports their understanding. This enables the provision of information tailored to the needs of individual users, promotes a deeper understanding of the generated AI by the user, and improves the overall response quality of the system through continuous model updates by the learning device.

[0247] An "input device" is a device that receives input from the user and initiates a dialogue.

[0248] A "dialogue management device" is a device that analyzes user input and identifies relevant information based on the generated data.

[0249] An "information provision device" is a device that provides identified information to the user and supports their understanding of the generated AI.

[0250] A "learning device" is a device that improves the performance of a machine learning model using data collected through interaction with the user.

[0251] A "communication device" is a device that transmits data from an input device to a server device and receives data from a server device to an input device.

[0252] "Generative AI technology" is a form of artificial intelligence technology that creates information and content for specific purposes.

[0253] "Historical dialogue data" refers to data that records a user's past interactions and is used to identify areas of difficulty and interests for the user.

[0254] To implement this invention, it is necessary to construct a system in which a server, terminal, and user work in cooperation. A specific example of such a system is shown below.

[0255] First, the server includes an input device, a dialogue management device, an information provision device, a learning device, and a communication device. Ideally, this server should be implemented on a computer with high processing power. The software implemented on the server includes libraries for natural language processing and machine learning frameworks. This allows the server to analyze prompt sentences sent by the user and generate relevant information. A concrete example of this prompt sentence is "How do I use image generation AI?". From this input, the server provides specific usage examples and basic explanations of the generation AI.

[0256] Next, the terminal is responsible for receiving user input and sending it to the server. Terminals are implemented in devices such as personal computers, smartphones, and tablets. These devices are equipped with user interfaces to facilitate interaction with the user. The terminal also presents the information received from the server to the user. The presented information is primarily in text format, but can include image and video links as needed.

[0257] The user interacts with the system through a terminal, inputting questions and points of confusion about the generative AI as prompts. This iterative process allows the user to gradually deepen their knowledge of the generative AI. Based on the user's input, the server collects the interaction data and processes it in a learning device. This allows the system to continuously improve the accuracy of its responses.

[0258] By implementing this approach, it becomes possible to provide information optimized for individual users and to realize a system that supports understanding of the generated AI.

[0259] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0260] Step 1:

[0261] The user enters questions about the image generation AI using the terminal's user interface. These questions are in text format, such as, "How do I use the image generation AI?" The entered data is then sent to the server by the terminal.

[0262] Step 2:

[0263] The terminal sends the prompt text entered by the user as digital data to the server. The input reaches the server using the HTTP protocol. Data encryption technology (SSL / TLS) is used during this communication process to ensure the security of the transmitted content.

[0264] Step 3:

[0265] The server passes the received prompt text to the dialogue management device, where it is analyzed using natural language processing. The input here is the user's question text, and the output is the analysis result identifying the user's intent and areas of interest. This analysis utilizes morphological analysis techniques to extract keywords from the text.

[0266] Step 4:

[0267] The information provider within the server generates appropriate information based on the analysis results of the dialogue management device. Specifically, it combines knowledge of usage examples and generation AI technology to generate information most relevant to the user's question. Using AI technology, it creates content such as, for example, "An example of using image generation AI is a service that transforms photos into art."

[0268] Step 5:

[0269] The server sends the generated information to the terminal. This includes a text message and related links as response data. This information is also encrypted using SSL / TLS, ensuring security when transmitted to the terminal.

[0270] Step 6:

[0271] The terminal displays information received from the server to the user. The displayed content is in text format, and relevant images and links can be displayed as needed. The information is provided intuitively and clearly through the user interface.

[0272] Step 7:

[0273] Users can review and deepen their understanding of the presented information. If they want to know more, they can enter a new prompt, and the cycle continues from step 1. This allows users to gradually deepen their understanding of the generated AI.

[0274] (Application Example 1)

[0275] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0276] Understanding the complex information and diverse technologies related to generative AI is difficult for the average user, hindering effective learning and application. Furthermore, the lack of appropriate information tailored to each user's interests and level of understanding hinders the effective use of this information.

[0277] 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.

[0278] In this invention, the server includes user input / output means, dialogue control means, knowledge provision means, learning support means, and information processing means. This enables the provision of information based on the individual needs and interests of users, and facilitates a deep understanding of and effective utilization of generative AI technology.

[0279] "User input / output means" refers to a device or system that has the function of receiving user input and initiating dialogue.

[0280] "Dialogue control means" refers to a device or system that has a control function for analyzing user input and identifying relevant knowledge based on the generated information.

[0281] A "knowledge provision means" is a device or system that has the function of providing specific knowledge to the user and supporting their understanding of generated AI.

[0282] The "learning assistance means" is a device or system having an auxiliary function for improving the performance of a machine learning model by using information collected through interaction with a user.

[0283] The "information processing means" is a device or system having a processing function for personalizing information based on a user's history and interests.

[0284] This invention realizes a system for providing a user with information related to generative AI technology by using a server and a terminal. The server receives a user's input through user input / output means and uses dialogue control means to analyze the content. This dialogue control means uses natural language processing libraries such as spaCy and NLTK to analyze the input text and identify relevant knowledge. Then, the knowledge providing means provides the generated knowledge to the user in an easy-to-understand form. At this time, a generative AI library such as OpenAI GPT-3 is utilized to dynamically generate information according to the user's interests.

[0285] Furthermore, the terminal utilizes information processing means to present personalized information to the user. This information processing means personalizes information considering the history and interests based on the user's history data, and emphasizes and provides particularly important content. Through this process, the user's understanding is deepened, and the performance of the machine learning model is improved by the learning assistance means using the collected information.

[0286] As a specific example, when a user asks a question such as "I want to know about the latest generative AI technology", this system uses GPT-3 to provide a response in a form such as "Currently, image generation technology and natural language processing technology are evolving, and there are many application examples". As an example of a prompt sentence, content such as "List up the latest generative AI technologies and explain their features in an easy-to-understand manner." is used. By doing so, the user can obtain information according to their own interests and needs, and can more deeply understand the technology of generative AI.

[0287] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0288] Step 1:

[0289] The user inputs a question related to the generative AI in text form through the terminal. The input text is collected by the terminal and sent to the server. The input at this point is the user's question text.

[0290] Step 2:

[0291] The server analyzes the received text data using natural language processing libraries such as spaCy or NLTK. The main purpose of the analysis is to identify the fields that the user is interested in or concerned about, and as a result, the topic of interest is output. Specifically, keyword extraction and context analysis are performed.

[0292] Step 3:

[0293] Based on the analysis results, the server uses a generative AI library such as OpenAI GPT-3 to generate a response according to the topic of interest. The input in this process is the identified topic, and as its output, the information and knowledge to be provided to the user are generated. Specifically, dialogue generation and information completion are included.

[0294] Step 4:

[0295] The server sends the generated information to the terminal. The terminal receives this and displays it in a form that is easy for the user to understand. The display form may include text, images, or visual data. The terminal processes this and presents guides or hints for the user to smoothly proceed with the dialogue.

[0296] Step 5:

[0297] The terminal receives additional questions and feedback from the user and sends them to the server. The server uses this feedback to improve the performance of the machine learning model using learning aids. The input in this step is new user feedback, and the output is an updated model that enables more accurate response generation.

[0298] 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.

[0299] This invention enables more personalized information delivery by incorporating an emotion engine into a system that supports the understanding of generative AI technology through interaction with the user. This system is implemented in a form in which a server, a terminal, and the user interact with each other.

[0300] The terminal provides an interface that accepts text or voice input from the user. When the user inputs a question about the generated AI, the data is sent to the server. The server uses a dialogue management module to analyze the input and identify the user's intended question and areas of difficulty. In this invention, an emotion engine is further used to recognize the user's emotions contained in the input data.

[0301] The server's emotion engine determines the user's emotional state by analyzing facial expressions from text, audio, or video. For example, it analyzes specific vocabulary and punctuation in the user's text input, as well as their tone and intonation, and changes in facial expressions to identify emotions such as anxiety, excitement, or confusion.

[0302] Based on the user's emotional state, the server's information providing module generates a response content considering that information. In addition to the standard response, the information providing module adds expressions that soothe emotions and simple explanations that give a sense of security. For example, if the user is analyzed to be confused, the system presents an explanation that includes an expression giving a sense of security such as "Please don't worry. I will explain it in more detail."

[0303] The generated response is sent back to the terminal and presented to the user. By adapting to the user's emotions in this way, it is possible to provide more effective information and improve user satisfaction. Also, in order to utilize the user's feedback in the next interaction, the server continuously accumulates the result of the interaction and the user's emotional data in the learning module and updates the model.

[0304] By such means, a system integrating emotion recognition and generative AI knowledge provision is constructed, and it is possible to provide a more personalized experience for the user.

[0305] The following explains the processing flow.

[0306] Step 1:

[0307] The terminal provides an interface to the user and prompts the user to input questions or doubts about the generative AI. The user completes the input and presses the send button.

[0308] Step 2:

[0309] The terminal sends the input text or voice data to the server. This data is received by the server for later analysis.

[0310] Step 3:

[0311] The server receives user input and passes it to the dialogue management module, which uses natural language processing algorithms to analyze the text or speech. The purpose of the analysis is to identify the user's intentions, the content of their questions, and areas of difficulty.

[0312] Step 4:

[0313] The server's emotion engine further analyzes the user's input data to recognize their emotional state. Based on text vocabulary and punctuation, voice tone, and even the user's facial expressions derived from the text data, it identifies specific emotions the user is experiencing (e.g., confusion, anxiety, joy).

[0314] Step 5:

[0315] The server's information module generates the optimal response for the user based on the analyzed intent and emotional state. The response includes adjustments according to the emotional state and is composed of reassuring language and simple explanations.

[0316] Step 6:

[0317] The server sends the generated response to the terminal. The terminal displays this information to the user and, if necessary, also provides relevant visual information (e.g., images or video links).

[0318] Step 7:

[0319] Users can view the presented information and enter further questions or feedback. Any new input from the user is processed again through the process starting from step 2.

[0320] Step 8:

[0321] The server's learning module re-evaluates the data and emotional states collected during the interaction and updates the machine learning model. Through this update, the system's accuracy is improved so that it can provide more precise information and emotional responses in subsequent interactions.

[0322] (Example 2)

[0323] 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".

[0324] Conventional interactive systems have struggled to appropriately recognize users' emotions and provide information tailored to their individual needs. Furthermore, they have been unable to employ effective dialogue strategies for each user's different areas of difficulty, making it challenging to deepen user understanding. The challenge lies in resolving these issues and providing more personalized information.

[0325] 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.

[0326] In this invention, the server includes an integrated analysis means for analyzing user input and recognizing emotions, a response generation means for generating and providing relevant information based on the user's emotional state, and an analysis device for accumulating data collected through interaction with the user and using it to improve a learning model. This makes it possible to provide personalized information that is tailored to the user's emotions and individual needs.

[0327] A "communication device" is a device that receives input from a user and provides an interface for initiating a conversation.

[0328] An "integrated analysis means" is a function within the system that analyzes the user's input, performs emotion recognition, and identifies related information.

[0329] The "response generation means" is a function that generates information to be provided to the user based on the analyzed emotional state of the user.

[0330] An "analysis device" is a device that accumulates data obtained from interactions with users and uses that data to perform a process of improving the learning model.

[0331] "Emotional state" refers to a psychological state inferred from the user's input, and includes anxiety, excitement, confusion, etc.

[0332] A "learning model" is an algorithm or model that is updated using collected data to improve the accuracy and adaptability of subsequent interactions.

[0333] "Template information" is a set of predefined information based on multiple knowledge generation techniques that forms the basis for response generation.

[0334] A "dialogue strategy" refers to a method of conducting a dialogue that is optimized according to the user's areas of difficulty and emotional state.

[0335] This invention constitutes a system that provides personalized information through dialogue with the user, and aims to generate responses that take into account the user's emotional state. The following describes a specific form for implementing this system.

[0336] The server functions as an integrated analysis tool, analyzing the user's input data. This input data consists of text and audio data provided by the user via a terminal. The terminal is equipped with a microphone to convert the user's voice into a digital signal and a user interface for inputting text data. The dialogue begins when the user inputs the question, "How does the generative AI understand emotions?" into the terminal.

[0337] Input data sent from the terminal to the server is analyzed by an integrated analysis system within the server. The server uses natural language processing technology to identify the intent of the input and then uses an emotion engine to recognize the user's emotional state. Emotion recognition includes keyword extraction from text and tone analysis in the case of audio data. For example, vocabulary such as "difficult" or "I don't understand" in the user's questions, as well as emotions such as confusion or anxiety, can be detected from the intonation of the voice.

[0338] Based on the analysis results, the server's response generation mechanism generates information based on the user's emotional state. This response includes expressions designed to help the user relax. For example, if a user inputs, "The mechanism of the generating AI is complex and I don't understand it," the server will generate something like, "Don't worry. I'll explain it in an easy-to-understand way."

[0339] The generated response is sent from the server to the terminal, which then displays it as text or outputs it as audio to the user. In this way, the dialogue progresses through the provision of appropriate information based on emotion recognition, supporting a deeper understanding of the user.

[0340] Furthermore, the server stores the dialogue results in an analysis device and uses machine learning models to improve the accuracy of future dialogues. This enables the system to continuously improve and adapt, providing users with a higher level of personalized experience.

[0341] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0342] Step 1:

[0343] The device accepts text or voice input from the user. Specifically, the user enters a prompt such as, "How will the Generating AI answer my question?" This input is captured as digital data by the device and prepared to be sent to the server.

[0344] Step 2:

[0345] The terminal transmits the received input data to the server. The input data securely reaches the server's integrated analysis system via network communication. The input here is digitized text or audio data, which is transferred directly to the server.

[0346] Step 3:

[0347] The server uses integrated analysis tools to analyze the user's input data. During this process, natural language processing techniques are used to analyze the grammar and meaning of the text and identify the user's intent. The input string data is analyzed, and an output is obtained that identifies an "explanation based on the AI's response method."

[0348] Step 4:

[0349] The server uses an emotion engine to recognize the user's emotional state. It analyzes emotions from word choices in text and tone of voice. The input here is a specific part of the sentence analyzed in the previous step, and the output is a judgment such as "User's emotional state: confused, interested."

[0350] Step 5:

[0351] The server's response generation mechanism generates an appropriate response based on the identified intent and emotional state. It includes reassuring sentences to make the response easy for the user to understand. The input uses analysis results and emotional data, and the output is a response such as, "Don't worry. The generating AI has analyzed your question and will provide the most relevant information."

[0352] Step 6:

[0353] The server sends the generated response to the terminal. The response data is encoded in a digital format and sent to the terminal via network communication.

[0354] Step 7:

[0355] The terminal displays the response received from the server to the user. Specifically, it is displayed on the screen in text format or played back through the speaker using speech synthesis. This output allows the user to confirm the response from the server.

[0356] Step 8:

[0357] The server stores the results of user interactions in an analysis device and updates the learning model to utilize them in future interactions. This data accumulation and model updating form a feedback loop to improve the quality of subsequent interactions.

[0358] (Application Example 2)

[0359] 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."

[0360] In today's information society, generative AI technology is becoming mainstream, but users need specialized knowledge to fully understand and utilize these technologies. Furthermore, there is a lack of adequate support for the anxiety and confusion that users may experience when using generative AI, highlighting the need for user-friendly systems.

[0361] 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.

[0362] In this invention, the server includes emotion recognition means for analyzing the user's emotions and adjusting the response content based on the emotional state; reassurance providing means for presenting additional information to provide reassurance when the user feels anxious or confused; and dialogue control means for analyzing the user's input and identifying related information based on the generated data. As a result, the user receives responses that take emotions into consideration, deepens their understanding of the generation AI technology, and enables them to use the system with peace of mind.

[0363] A "user interface means" is an interface device that has the function of receiving input from the user and initiating a dialogue.

[0364] A "dialogue control means" is a control device that analyzes the user's input and identifies relevant information based on the generated data.

[0365] An "information provision device" is a device that provides identified information to the user and has the function of supporting understanding of the generated AI.

[0366] An "emotion recognition device" is a device equipped with the function of analyzing the user's emotions and adjusting the response content based on the emotional state.

[0367] A "learning tool" is a device that uses data collected through interaction with the user to improve the performance of a machine learning model.

[0368] A "means of providing reassurance" is a device that has the function of presenting additional information to provide reassurance when the user feels anxious or confused.

[0369] A "generative AI model" is a form of artificial intelligence used to generate responses or information for specific tasks.

[0370] A "prompt message" is a phrase that, when input into a generation AI, is used to generate appropriate responses or information.

[0371] The system for realizing this invention is a conversational AI assistant that integrates emotion recognition technology and is specifically designed for application in the field of electronic payments. The system's terminal primarily uses a smartphone as its hardware. The terminal receives voice and text input from the user and sends this data to a server for analysis.

[0372] On the server side, a program built using Python analyzes the input data. Specifically, it uses the OpenCV and PyDub libraries to analyze the tone of the audio data and the content of the text, extracting facial and audio features. Here, the user's emotional state is determined by an emotion recognition system. This emotional information then influences the response generated by the system.

[0373] The generative AI model is implemented using the Hugging Face Transformers library and generates responses based on user input and emotional state prompts. An example prompt is, "How would you reassure a user if they are surprised?" The server analyzes the generated responses and, through reassurance mechanisms, returns appropriate messages when the user feels anxious. Through this system, users can make electronic payments smoothly and with peace of mind.

[0374] For example, when a user makes a large payment, the system sends a reassuring message such as, "We have confirmed that this transaction is secure. You can cancel it at any time, so please rest assured." As a result, users can use electronic payments with greater confidence.

[0375] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0376] Step 1:

[0377] The server receives voice and text input from the user sent from the terminal. This input includes voice files and text data. This data is used as foundational information to analyze the user's emotional state and intentions.

[0378] Step 2:

[0379] The server inputs received audio and text data into an emotion recognition system to analyze the user's emotional state. Audio data undergoes tone analysis using PyDub, and facial features are analyzed using OpenCV to infer emotions such as joy or anxiety. The output is a quantitative evaluation indicating the user's emotions.

[0380] Step 3:

[0381] The server inputs text data into the dialogue control system to analyze the user's intent and requests. Using natural language processing technology, the analyzed information is constructed as prompt sentences for a generative AI model. The output is a prompt sentence that includes the user's intent.

[0382] Step 4:

[0383] The server's AI model generates the optimal response using the sentiment evaluation from step 2 and the prompt text from step 3. Using the Hugging Face Transformers library, a contextually adjusted response is generated based on the data input. The output is the final response text to be returned to the user.

[0384] Step 5:

[0385] The server adjusts the generated response message through a reassurance-providing mechanism and adds additional reassurance messages as needed. This provides information that alleviates the user's anxiety and provides a sense of security. The output is the final response message to the user.

[0386] Step 6:

[0387] The user's device receives the final response message and presents it to the user in either audio or text format. The user receives a reassuring response, allowing them to confidently proceed with electronic payments and the use of generative AI technology.

[0388] 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.

[0389] 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.

[0390] 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.

[0391] [Third Embodiment]

[0392] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0393] 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.

[0394] 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).

[0395] 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.

[0396] 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.

[0397] 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).

[0398] 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.

[0399] 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.

[0400] 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.

[0401] 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.

[0402] 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.

[0403] 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".

[0404] This invention describes an implementation in which it is carried out as an interactive system to make it easier for the user to understand information related to the generated AI. This system consists of multiple computer programs and operates through the interaction of a server, a terminal, and the user.

[0405] The server first receives user input from the terminal. User input is text data such as questions or points of confusion regarding the generated AI. The server passes this received data to the dialogue management module, where it analyzes the input content using natural language processing. In this analysis, the module specifically identifies the user's areas of difficulty and extracts related information. In doing so, the dialogue management module considers the user's historical interactions to learn their areas of difficulty and needs.

[0406] Next, an information provision module operates within the server and generates appropriate information based on the analysis results. This information includes a basic explanation of the generating AI and answers to specific questions the user is particularly seeking. The server sends this generated information to the terminal, which then presents the information to the user. The presentation method is primarily text format, but links to images and videos are also provided as needed.

[0407] When the user views the presented information and asks a new question, that input is sent back to the server, and the dialogue management module repeats the analysis and information provision process. Through this process, the user can gradually deepen their understanding of the generative AI.

[0408] For example, if a user asks, "How do I use image generation AI?", the server's dialogue management module analyzes this question, and the information provision module generates information such as, "Image generation AI is used in many applications. For example, it can be used to convert photos into art styles or generate new images." This allows users to deepen their understanding of the technology through concrete use cases.

[0409] Furthermore, this system utilizes data collected through interaction with the user to update the machine learning model using the server's learning module. This update enables the provision of more precise information in subsequent interactions, improving the overall response quality of the system. This feedback loop ensures the learning effect of this invention.

[0410] The following describes the processing flow.

[0411] Step 1:

[0412] The device displays a screen that accepts user input. The user enters a question about the generated AI and presses the submit button.

[0413] Step 2:

[0414] The server receives user input sent from the terminal. The input is basically treated as text data.

[0415] Step 3:

[0416] The server's dialogue management module analyzes the user's input. Here, natural language processing techniques are used to identify the user's intentions and areas of difficulty from the input.

[0417] Step 4:

[0418] The server's information module generates appropriate answers to the user's questions based on the analysis results. The generated information includes relevant explanations and specific usage examples.

[0419] Step 5:

[0420] The server sends the generated information to the terminal.

[0421] Step 6:

[0422] The device displays information it has received to the user. This display may include links to relevant images and videos, as needed.

[0423] Step 7:

[0424] After the user views the presented information, they will enter their information again if they have new questions or require further information. This input will then be processed again starting from step 2.

[0425] Step 8:

[0426] The server's learning module analyzes the data collected through interactions with the user and updates the machine learning model. This update improves the accuracy of information provided in subsequent interactions.

[0427] (Example 1)

[0428] 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."

[0429] With the advancement of modern generative AI technology, it is becoming increasingly important for ordinary users to effectively understand and utilize advanced AI technologies. However, users' understanding of generative AI depends on their individual experience and knowledge, and providing information tailored to each user's specific needs is necessary to deepen that understanding. Furthermore, continuous learning and feedback are crucial to improving the accuracy of responses in such dialogue systems.

[0430] 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.

[0431] In this invention, the server includes an input device that receives user input and initiates a dialogue, a dialogue management device that analyzes the user input and identifies relevant information, and an information provision device that provides the identified information to the user and supports their understanding. This enables the provision of information tailored to the needs of individual users, promotes a deeper understanding of the generated AI by the user, and improves the overall response quality of the system through continuous model updates by the learning device.

[0432] An "input device" is a device that receives input from the user and initiates a dialogue.

[0433] A "dialogue management device" is a device that analyzes user input and identifies relevant information based on the generated data.

[0434] An "information provision device" is a device that provides identified information to the user and supports their understanding of the generated AI.

[0435] A "learning device" is a device that improves the performance of a machine learning model using data collected through interaction with the user.

[0436] A "communication device" is a device that transmits data from an input device to a server device and receives data from a server device to an input device.

[0437] "Generative AI technology" is a form of artificial intelligence technology that creates information and content for specific purposes.

[0438] "Historical dialogue data" refers to data that records a user's past interactions and is used to identify areas of difficulty and interests for the user.

[0439] To implement this invention, it is necessary to construct a system in which a server, terminal, and user work in cooperation. A specific example of such a system is shown below.

[0440] First, the server includes an input device, a dialogue management device, an information provision device, a learning device, and a communication device. Ideally, this server should be implemented on a computer with high processing power. The software implemented on the server includes libraries for natural language processing and machine learning frameworks. This allows the server to analyze prompt sentences sent by the user and generate relevant information. A concrete example of this prompt sentence is "How do I use image generation AI?". From this input, the server provides specific usage examples and basic explanations of the generation AI.

[0441] Next, the terminal is responsible for receiving user input and sending it to the server. Terminals are implemented in devices such as personal computers, smartphones, and tablets. These devices are equipped with user interfaces to facilitate interaction with the user. The terminal also presents the information received from the server to the user. The presented information is primarily in text format, but can include image and video links as needed.

[0442] The user interacts with the system through a terminal, inputting questions and points of confusion about the generative AI as prompts. This iterative process allows the user to gradually deepen their knowledge of the generative AI. Based on the user's input, the server collects the interaction data and processes it in a learning device. This allows the system to continuously improve the accuracy of its responses.

[0443] By implementing this approach, it becomes possible to provide information optimized for individual users and to realize a system that supports understanding of the generated AI.

[0444] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0445] Step 1:

[0446] The user enters questions about the image generation AI using the terminal's user interface. These questions are in text format, such as, "How do I use the image generation AI?" The entered data is then sent to the server by the terminal.

[0447] Step 2:

[0448] The terminal sends the prompt text entered by the user as digital data to the server. The input reaches the server using the HTTP protocol. Data encryption technology (SSL / TLS) is used during this communication process to ensure the security of the transmitted content.

[0449] Step 3:

[0450] The server passes the received prompt text to the dialogue management device, where it is analyzed using natural language processing. The input here is the user's question text, and the output is the analysis result identifying the user's intent and areas of interest. This analysis utilizes morphological analysis techniques to extract keywords from the text.

[0451] Step 4:

[0452] The information provider within the server generates appropriate information based on the analysis results of the dialogue management device. Specifically, it combines knowledge of usage examples and generation AI technology to generate information most relevant to the user's question. Using AI technology, it creates content such as, for example, "An example of using image generation AI is a service that transforms photos into art."

[0453] Step 5:

[0454] The server sends the generated information to the terminal. This includes a text message and related links as response data. This information is also encrypted using SSL / TLS, ensuring security when transmitted to the terminal.

[0455] Step 6:

[0456] The terminal displays information received from the server to the user. The displayed content is in text format, and relevant images and links can be displayed as needed. The information is provided intuitively and clearly through the user interface.

[0457] Step 7:

[0458] Users can review and deepen their understanding of the presented information. If they want to know more, they can enter a new prompt, and the cycle continues from step 1. This allows users to gradually deepen their understanding of the generated AI.

[0459] (Application Example 1)

[0460] 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."

[0461] Understanding the complex information and diverse technologies related to generative AI is difficult for the average user, hindering effective learning and application. Furthermore, the lack of appropriate information tailored to each user's interests and level of understanding hinders the effective use of this information.

[0462] 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.

[0463] In this invention, the server includes user input / output means, dialogue control means, knowledge provision means, learning support means, and information processing means. This enables the provision of information based on the individual needs and interests of users, and facilitates a deep understanding of and effective utilization of generative AI technology.

[0464] "User input / output means" refers to a device or system that has the function of receiving user input and initiating dialogue.

[0465] "Dialogue control means" refers to a device or system that has a control function for analyzing user input and identifying relevant knowledge based on the generated information.

[0466] A "knowledge provision means" is a device or system that has the function of providing specific knowledge to the user and supporting their understanding of generated AI.

[0467] A "learning support device" is a device or system that has auxiliary functions to improve the performance of a machine learning model using information collected through interaction with the user.

[0468] "Information processing means" refers to a device or system that has processing functions for personalizing information based on the user's history and interests.

[0469] This invention realizes a system for providing users with information related to generative AI technology using a server and a terminal. The server receives user input through user input / output means and uses dialogue control means to analyze the content of that input. This dialogue control means uses natural language processing libraries such as spaCy and NLTK to analyze the input text and identify relevant knowledge. Then, knowledge provision means provides the generated knowledge to the user in an easy-to-understand format. In this process, generative AI libraries such as OpenAI GPT-3 are utilized to dynamically generate information tailored to the user's interests.

[0470] Furthermore, the device utilizes information processing means to present personalized information to the user. This information processing means personalizes information based on the user's history and interests, taking into account their history, and highlights particularly important content. Through this process, the device deepens its understanding of the user, and the collected information is used to improve the performance of machine learning models using learning support means.

[0471] For example, if a user asks a question such as, "Please tell me about the latest generative AI technologies," this system will use GPT-3 to provide a response in the format of, "Currently, image generation technology and natural language processing technology are evolving, and there are many application examples." An example of a prompt would be, "Please list the latest generative AI technologies and explain the features of each in an easy-to-understand way." In this way, users can obtain information that suits their interests and needs, and gain a deeper understanding of generative AI technology.

[0472] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0473] Step 1:

[0474] The user enters a question related to the generating AI in text format via their device. The entered text is collected by the device and sent to the server. At this point, the input is the user's question text.

[0475] Step 2:

[0476] The server analyzes the received text data using natural language processing libraries such as spaCy and NLTK. The main purpose of the analysis is to identify the user's areas of interest, and as a result, topics of interest are output. Specifically, keyword extraction and contextual analysis are performed.

[0477] Step 3:

[0478] Based on the analysis results, the server uses generative AI libraries such as OpenAI GPT-3 to generate responses tailored to the user's interests. The input in this process is the identified topic, and the output generates information and knowledge to be provided to the user. Specifically, this includes dialogue generation and information completion.

[0479] Step 4:

[0480] The server sends the generated information to the terminal. The terminal receives this information and displays it in a format that is easy for the user to understand. This display format may include text, images, and visual data. The terminal processes this information and provides guides and hints to help the user navigate the interaction smoothly.

[0481] Step 5:

[0482] The terminal receives additional questions and feedback from the user and sends them to the server. The server uses this feedback to improve the performance of the machine learning model using learning aids. The input in this step is new user feedback, and the output is an updated model that enables more accurate response generation.

[0483] 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.

[0484] This invention enables more personalized information delivery by incorporating an emotion engine into a system that supports the understanding of generative AI technology through interaction with the user. This system is implemented in a form in which a server, a terminal, and the user interact with each other.

[0485] The terminal provides an interface that accepts text or voice input from the user. When the user inputs a question about the generated AI, the data is sent to the server. The server uses a dialogue management module to analyze the input and identify the user's intended question and areas of difficulty. In this invention, an emotion engine is further used to recognize the user's emotions contained in the input data.

[0486] The server's emotion engine determines the user's emotional state by analyzing facial expressions from text, audio, or video. For example, it analyzes specific vocabulary and punctuation in the user's text input, as well as their tone and intonation, and changes in facial expressions to identify emotions such as anxiety, excitement, or confusion.

[0487] Based on the user's emotional state, the server's information module generates a response that takes this information into account. In addition to a standard response, the information module adds easing language and reassuring, simple explanations. For example, if the system analyzes that the user is confused, it will provide an explanation that includes reassuring language such as, "Don't worry, let me explain in more detail."

[0488] The generated response is sent back to the terminal and presented to the user. By adaptively responding to the user's emotions in this way, more effective information can be provided and user satisfaction can be improved. Furthermore, in order to utilize the user's feedback in the next interaction, the server continuously stores the interaction results and the user's emotion data in a learning module and updates the model.

[0489] Through these means, a system can be built that integrates emotion recognition and generative AI knowledge provision, making it possible to provide users with a more personalized experience.

[0490] The following describes the processing flow.

[0491] Step 1:

[0492] The device provides the user with an interface, prompting them to input questions or concerns about the generated AI. The user completes their input and presses the submit button.

[0493] Step 2:

[0494] The device sends the entered text or voice data to the server. This data is received by the server for later analysis.

[0495] Step 3:

[0496] The server receives user input and passes it to the dialogue management module, which uses natural language processing algorithms to analyze the text or speech. The purpose of the analysis is to identify the user's intentions, the content of their questions, and areas of difficulty.

[0497] Step 4:

[0498] The server's emotion engine further analyzes the user's input data to recognize their emotional state. Based on text vocabulary and punctuation, voice tone, and even the user's facial expressions derived from the text data, it identifies specific emotions the user is experiencing (e.g., confusion, anxiety, joy).

[0499] Step 5:

[0500] The server's information module generates the optimal response for the user based on the analyzed intent and emotional state. The response includes adjustments according to the emotional state and is composed of reassuring language and simple explanations.

[0501] Step 6:

[0502] The server sends the generated response to the terminal. The terminal displays this information to the user and, if necessary, also provides relevant visual information (e.g., images or video links).

[0503] Step 7:

[0504] Users can view the presented information and enter further questions or feedback. Any new input from the user is processed again through the process starting from step 2.

[0505] Step 8:

[0506] The server's learning module re-evaluates the data and emotional states collected during the interaction and updates the machine learning model. Through this update, the system's accuracy is improved so that it can provide more precise information and emotional responses in subsequent interactions.

[0507] (Example 2)

[0508] 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."

[0509] Conventional interactive systems have struggled to appropriately recognize users' emotions and provide information tailored to their individual needs. Furthermore, they have been unable to employ effective dialogue strategies for each user's different areas of difficulty, making it challenging to deepen user understanding. The challenge lies in resolving these issues and providing more personalized information.

[0510] 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.

[0511] In this invention, the server includes an integrated analysis means for analyzing user input and recognizing emotions, a response generation means for generating and providing relevant information based on the user's emotional state, and an analysis device for accumulating data collected through interaction with the user and using it to improve a learning model. This makes it possible to provide personalized information that is tailored to the user's emotions and individual needs.

[0512] A "communication device" is a device that receives input from a user and provides an interface for initiating a conversation.

[0513] An "integrated analysis means" is a function within the system that analyzes the user's input, performs emotion recognition, and identifies related information.

[0514] The "response generation means" is a function that generates information to be provided to the user based on the analyzed emotional state of the user.

[0515] An "analysis device" is a device that accumulates data obtained from interactions with users and uses that data to perform a process of improving the learning model.

[0516] "Emotional state" refers to a psychological state inferred from the user's input, and includes anxiety, excitement, confusion, etc.

[0517] A "learning model" is an algorithm or model that is updated using collected data to improve the accuracy and adaptability of subsequent interactions.

[0518] "Template information" is a set of predefined information based on multiple knowledge generation techniques that forms the basis for response generation.

[0519] A "dialogue strategy" refers to a method of conducting a dialogue that is optimized according to the user's areas of difficulty and emotional state.

[0520] This invention constitutes a system that provides personalized information through dialogue with the user, and aims to generate responses that take into account the user's emotional state. The following describes a specific form for implementing this system.

[0521] The server functions as an integrated analysis tool, analyzing the user's input data. This input data consists of text and audio data provided by the user via a terminal. The terminal is equipped with a microphone to convert the user's voice into a digital signal and a user interface for inputting text data. The dialogue begins when the user inputs the question, "How does the generative AI understand emotions?" into the terminal.

[0522] Input data sent from the terminal to the server is analyzed by an integrated analysis system within the server. The server uses natural language processing technology to identify the intent of the input and then uses an emotion engine to recognize the user's emotional state. Emotion recognition includes keyword extraction from text and tone analysis in the case of audio data. For example, vocabulary such as "difficult" or "I don't understand" in the user's questions, as well as emotions such as confusion or anxiety, can be detected from the intonation of the voice.

[0523] Based on the analysis results, the server's response generation mechanism generates information based on the user's emotional state. This response includes expressions designed to help the user relax. For example, if a user inputs, "The mechanism of the generating AI is complex and I don't understand it," the server will generate something like, "Don't worry. I'll explain it in an easy-to-understand way."

[0524] The generated response is sent from the server to the terminal, which then displays it as text or outputs it as audio to the user. In this way, the dialogue progresses through the provision of appropriate information based on emotion recognition, supporting a deeper understanding of the user.

[0525] Furthermore, the server stores the dialogue results in an analysis device and uses machine learning models to improve the accuracy of future dialogues. This enables the system to continuously improve and adapt, providing users with a higher level of personalized experience.

[0526] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0527] Step 1:

[0528] The device accepts text or voice input from the user. Specifically, the user enters a prompt such as, "How will the Generating AI answer my question?" This input is captured as digital data by the device and prepared to be sent to the server.

[0529] Step 2:

[0530] The terminal transmits the received input data to the server. The input data securely reaches the server's integrated analysis system via network communication. The input here is digitized text or audio data, which is transferred directly to the server.

[0531] Step 3:

[0532] The server uses integrated analysis tools to analyze the user's input data. During this process, natural language processing techniques are used to analyze the grammar and meaning of the text and identify the user's intent. The input string data is analyzed, and an output is obtained that identifies an "explanation based on the AI's response method."

[0533] Step 4:

[0534] The server uses an emotion engine to recognize the user's emotional state. It analyzes emotions from word choices in text and tone of voice. The input here is a specific part of the sentence analyzed in the previous step, and the output is a judgment such as "User's emotional state: confused, interested."

[0535] Step 5:

[0536] The server's response generation mechanism generates an appropriate response based on the identified intent and emotional state. It includes reassuring sentences to make the response easy for the user to understand. The input uses analysis results and emotional data, and the output is a response such as, "Don't worry. The generating AI has analyzed your question and will provide the most relevant information."

[0537] Step 6:

[0538] The server sends the generated response to the terminal. The response data is encoded in a digital format and sent to the terminal via network communication.

[0539] Step 7:

[0540] The terminal displays the response received from the server to the user. Specifically, it is displayed on the screen in text format or played back through the speaker using speech synthesis. This output allows the user to confirm the response from the server.

[0541] Step 8:

[0542] The server stores the results of user interactions in an analysis device and updates the learning model to utilize them in future interactions. This data accumulation and model updating form a feedback loop to improve the quality of subsequent interactions.

[0543] (Application Example 2)

[0544] 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."

[0545] In today's information society, generative AI technology is becoming mainstream, but users need specialized knowledge to fully understand and utilize these technologies. Furthermore, there is a lack of adequate support for the anxiety and confusion that users may experience when using generative AI, highlighting the need for user-friendly systems.

[0546] 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.

[0547] In this invention, the server includes emotion recognition means for analyzing the user's emotions and adjusting the response content based on the emotional state; reassurance providing means for presenting additional information to provide reassurance when the user feels anxious or confused; and dialogue control means for analyzing the user's input and identifying related information based on the generated data. As a result, the user receives responses that take emotions into consideration, deepens their understanding of the generation AI technology, and enables them to use the system with peace of mind.

[0548] A "user interface means" is an interface device that has the function of receiving input from the user and initiating a dialogue.

[0549] A "dialogue control means" is a control device that analyzes the user's input and identifies relevant information based on the generated data.

[0550] An "information provision device" is a device that provides identified information to the user and has the function of supporting understanding of the generated AI.

[0551] An "emotion recognition device" is a device equipped with the function of analyzing the user's emotions and adjusting the response content based on the emotional state.

[0552] A "learning tool" is a device that uses data collected through interaction with the user to improve the performance of a machine learning model.

[0553] A "means of providing reassurance" is a device that has the function of presenting additional information to provide reassurance when the user feels anxious or confused.

[0554] A "generative AI model" is a form of artificial intelligence used to generate responses or information for specific tasks.

[0555] A "prompt message" is a phrase that, when input into a generation AI, is used to generate appropriate responses or information.

[0556] The system for realizing this invention is a conversational AI assistant that integrates emotion recognition technology and is specifically designed for application in the field of electronic payments. The system's terminal primarily uses a smartphone as its hardware. The terminal receives voice and text input from the user and sends this data to a server for analysis.

[0557] On the server side, a program built using Python analyzes the input data. Specifically, it uses the OpenCV and PyDub libraries to analyze the tone of the audio data and the content of the text, extracting facial and audio features. Here, the user's emotional state is determined by an emotion recognition system. This emotional information then influences the response generated by the system.

[0558] The generative AI model is implemented using the Hugging Face Transformers library and generates responses based on user input and emotional state prompts. An example prompt is, "How would you reassure a user if they are surprised?" The server analyzes the generated responses and, through reassurance mechanisms, returns appropriate messages when the user feels anxious. Through this system, users can make electronic payments smoothly and with peace of mind.

[0559] For example, when a user makes a large payment, the system sends a reassuring message such as, "We have confirmed that this transaction is secure. You can cancel it at any time, so please rest assured." As a result, users can use electronic payments with greater confidence.

[0560] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0561] Step 1:

[0562] The server receives voice and text input from the user sent from the terminal. This input includes voice files and text data. This data is used as foundational information to analyze the user's emotional state and intentions.

[0563] Step 2:

[0564] The server inputs received audio and text data into an emotion recognition system to analyze the user's emotional state. Audio data undergoes tone analysis using PyDub, and facial features are analyzed using OpenCV to infer emotions such as joy or anxiety. The output is a quantitative evaluation indicating the user's emotions.

[0565] Step 3:

[0566] The server inputs text data into the dialogue control system to analyze the user's intent and requests. Using natural language processing technology, the analyzed information is constructed as prompt sentences for a generative AI model. The output is a prompt sentence that includes the user's intent.

[0567] Step 4:

[0568] The server's AI model generates the optimal response using the sentiment evaluation from step 2 and the prompt text from step 3. Using the Hugging Face Transformers library, a contextually adjusted response is generated based on the data input. The output is the final response text to be returned to the user.

[0569] Step 5:

[0570] The server adjusts the generated response message through a reassurance-providing mechanism and adds additional reassurance messages as needed. This provides information that alleviates the user's anxiety and provides a sense of security. The output is the final response message to the user.

[0571] Step 6:

[0572] The user's device receives the final response message and presents it to the user in either audio or text format. The user receives a reassuring response, allowing them to confidently proceed with electronic payments and the use of generative AI technology.

[0573] 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.

[0574] 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.

[0575] 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.

[0576] [Fourth Embodiment]

[0577] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0578] 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.

[0579] 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).

[0580] 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.

[0581] 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.

[0582] 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).

[0583] 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.

[0584] 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.

[0585] 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.

[0586] 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.

[0587] 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.

[0588] 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.

[0589] 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".

[0590] This invention describes an implementation in which it is carried out as an interactive system to make it easier for the user to understand information related to the generated AI. This system consists of multiple computer programs and operates through the interaction of a server, a terminal, and the user.

[0591] The server first receives user input from the terminal. User input is text data such as questions or points of confusion regarding the generated AI. The server passes this received data to the dialogue management module, where it analyzes the input content using natural language processing. In this analysis, the module specifically identifies the user's areas of difficulty and extracts related information. In doing so, the dialogue management module considers the user's historical interactions to learn their areas of difficulty and needs.

[0592] Next, an information provision module operates within the server and generates appropriate information based on the analysis results. This information includes a basic explanation of the generating AI and answers to specific questions the user is particularly seeking. The server sends this generated information to the terminal, which then presents the information to the user. The presentation method is primarily text format, but links to images and videos are also provided as needed.

[0593] When the user views the presented information and asks a new question, that input is sent back to the server, and the dialogue management module repeats the analysis and information provision process. Through this process, the user can gradually deepen their understanding of the generative AI.

[0594] For example, if a user asks, "How do I use image generation AI?", the server's dialogue management module analyzes this question, and the information provision module generates information such as, "Image generation AI is used in many applications. For example, it can be used to convert photos into art styles or generate new images." This allows users to deepen their understanding of the technology through concrete use cases.

[0595] Furthermore, this system utilizes data collected through interaction with the user to update the machine learning model using the server's learning module. This update enables the provision of more precise information in subsequent interactions, improving the overall response quality of the system. This feedback loop ensures the learning effect of this invention.

[0596] The following describes the processing flow.

[0597] Step 1:

[0598] The device displays a screen that accepts user input. The user enters a question about the generated AI and presses the submit button.

[0599] Step 2:

[0600] The server receives user input sent from the terminal. The input is basically treated as text data.

[0601] Step 3:

[0602] The server's dialogue management module analyzes the user's input. Here, natural language processing techniques are used to identify the user's intentions and areas of difficulty from the input.

[0603] Step 4:

[0604] The server's information module generates appropriate answers to the user's questions based on the analysis results. The generated information includes relevant explanations and specific usage examples.

[0605] Step 5:

[0606] The server sends the generated information to the terminal.

[0607] Step 6:

[0608] The device displays information it has received to the user. This display may include links to relevant images and videos, as needed.

[0609] Step 7:

[0610] After the user views the presented information, they will enter their information again if they have new questions or require further information. This input will then be processed again starting from step 2.

[0611] Step 8:

[0612] The server's learning module analyzes the data collected through interactions with the user and updates the machine learning model. This update improves the accuracy of information provided in subsequent interactions.

[0613] (Example 1)

[0614] 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".

[0615] With the advancement of modern generative AI technology, it is becoming increasingly important for ordinary users to effectively understand and utilize advanced AI technologies. However, users' understanding of generative AI depends on their individual experience and knowledge, and providing information tailored to each user's specific needs is necessary to deepen that understanding. Furthermore, continuous learning and feedback are crucial to improving the accuracy of responses in such dialogue systems.

[0616] 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.

[0617] In this invention, the server includes an input device that receives user input and initiates a dialogue, a dialogue management device that analyzes the user input and identifies relevant information, and an information provision device that provides the identified information to the user and supports their understanding. This enables the provision of information tailored to the needs of individual users, promotes a deeper understanding of the generated AI by the user, and improves the overall response quality of the system through continuous model updates by the learning device.

[0618] An "input device" is a device that receives input from the user and initiates a dialogue.

[0619] A "dialogue management device" is a device that analyzes user input and identifies relevant information based on the generated data.

[0620] An "information provision device" is a device that provides identified information to the user and supports their understanding of the generated AI.

[0621] A "learning device" is a device that improves the performance of a machine learning model using data collected through interaction with the user.

[0622] A "communication device" is a device that transmits data from an input device to a server device and receives data from a server device to an input device.

[0623] "Generative AI technology" is a form of artificial intelligence technology that creates information and content for specific purposes.

[0624] "Historical dialogue data" refers to data that records a user's past interactions and is used to identify areas of difficulty and interests for the user.

[0625] To implement this invention, it is necessary to construct a system in which a server, terminal, and user work in cooperation. A specific example of such a system is shown below.

[0626] First, the server includes an input device, a dialogue management device, an information provision device, a learning device, and a communication device. Ideally, this server should be implemented on a computer with high processing power. The software implemented on the server includes libraries for natural language processing and machine learning frameworks. This allows the server to analyze prompt sentences sent by the user and generate relevant information. A concrete example of this prompt sentence is "How do I use image generation AI?". From this input, the server provides specific usage examples and basic explanations of the generation AI.

[0627] Next, the terminal is responsible for receiving user input and sending it to the server. Terminals are implemented in devices such as personal computers, smartphones, and tablets. These devices are equipped with user interfaces to facilitate interaction with the user. The terminal also presents the information received from the server to the user. The presented information is primarily in text format, but can include image and video links as needed.

[0628] The user interacts with the system through a terminal, inputting questions and points of confusion about the generative AI as prompts. This iterative process allows the user to gradually deepen their knowledge of the generative AI. Based on the user's input, the server collects the interaction data and processes it in a learning device. This allows the system to continuously improve the accuracy of its responses.

[0629] By implementing this approach, it becomes possible to provide information optimized for individual users and to realize a system that supports understanding of the generated AI.

[0630] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0631] Step 1:

[0632] The user enters questions about the image generation AI using the terminal's user interface. These questions are in text format, such as, "How do I use the image generation AI?" The entered data is then sent to the server by the terminal.

[0633] Step 2:

[0634] The terminal sends the prompt text entered by the user as digital data to the server. The input reaches the server using the HTTP protocol. Data encryption technology (SSL / TLS) is used during this communication process to ensure the security of the transmitted content.

[0635] Step 3:

[0636] The server passes the received prompt text to the dialogue management device, where it is analyzed using natural language processing. The input here is the user's question text, and the output is the analysis result identifying the user's intent and areas of interest. This analysis utilizes morphological analysis techniques to extract keywords from the text.

[0637] Step 4:

[0638] The information provider within the server generates appropriate information based on the analysis results of the dialogue management device. Specifically, it combines knowledge of usage examples and generation AI technology to generate information most relevant to the user's question. Using AI technology, it creates content such as, for example, "An example of using image generation AI is a service that transforms photos into art."

[0639] Step 5:

[0640] The server sends the generated information to the terminal. This includes a text message and related links as response data. This information is also encrypted using SSL / TLS, ensuring security when transmitted to the terminal.

[0641] Step 6:

[0642] The terminal displays information received from the server to the user. The displayed content is in text format, and relevant images and links can be displayed as needed. The information is provided intuitively and clearly through the user interface.

[0643] Step 7:

[0644] Users can review and deepen their understanding of the presented information. If they want to know more, they can enter a new prompt, and the cycle continues from step 1. This allows users to gradually deepen their understanding of the generated AI.

[0645] (Application Example 1)

[0646] 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".

[0647] Understanding the complex information and diverse technologies related to generative AI is difficult for the average user, hindering effective learning and application. Furthermore, the lack of appropriate information tailored to each user's interests and level of understanding hinders the effective use of this information.

[0648] 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.

[0649] In this invention, the server includes user input / output means, dialogue control means, knowledge provision means, learning support means, and information processing means. This enables the provision of information based on the individual needs and interests of users, and facilitates a deep understanding of and effective utilization of generative AI technology.

[0650] "User input / output means" refers to a device or system that has the function of receiving user input and initiating dialogue.

[0651] "Dialogue control means" refers to a device or system that has a control function for analyzing user input and identifying relevant knowledge based on the generated information.

[0652] A "knowledge provision means" is a device or system that has the function of providing specific knowledge to the user and supporting their understanding of generated AI.

[0653] A "learning support device" is a device or system that has auxiliary functions to improve the performance of a machine learning model using information collected through interaction with the user.

[0654] "Information processing means" refers to a device or system that has processing functions for personalizing information based on the user's history and interests.

[0655] This invention realizes a system for providing users with information related to generative AI technology using a server and a terminal. The server receives user input through user input / output means and uses dialogue control means to analyze the content of that input. This dialogue control means uses natural language processing libraries such as spaCy and NLTK to analyze the input text and identify relevant knowledge. Then, knowledge provision means provides the generated knowledge to the user in an easy-to-understand format. In this process, generative AI libraries such as OpenAI GPT-3 are utilized to dynamically generate information tailored to the user's interests.

[0656] Furthermore, the device utilizes information processing means to present personalized information to the user. This information processing means personalizes information based on the user's history and interests, taking into account their history, and highlights particularly important content. Through this process, the device deepens its understanding of the user, and the collected information is used to improve the performance of machine learning models using learning support means.

[0657] For example, if a user asks a question such as, "Please tell me about the latest generative AI technologies," this system will use GPT-3 to provide a response in the format of, "Currently, image generation technology and natural language processing technology are evolving, and there are many application examples." An example of a prompt would be, "Please list the latest generative AI technologies and explain the features of each in an easy-to-understand way." In this way, users can obtain information that suits their interests and needs, and gain a deeper understanding of generative AI technology.

[0658] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0659] Step 1:

[0660] The user enters a question related to the generating AI in text format via their device. The entered text is collected by the device and sent to the server. At this point, the input is the user's question text.

[0661] Step 2:

[0662] The server analyzes the received text data using natural language processing libraries such as spaCy and NLTK. The main purpose of the analysis is to identify the user's areas of interest, and as a result, topics of interest are output. Specifically, keyword extraction and contextual analysis are performed.

[0663] Step 3:

[0664] Based on the analysis results, the server uses generative AI libraries such as OpenAI GPT-3 to generate responses tailored to the user's interests. The input in this process is the identified topic, and the output generates information and knowledge to be provided to the user. Specifically, this includes dialogue generation and information completion.

[0665] Step 4:

[0666] The server sends the generated information to the terminal. The terminal receives this information and displays it in a format that is easy for the user to understand. This display format may include text, images, and visual data. The terminal processes this information and provides guides and hints to help the user navigate the interaction smoothly.

[0667] Step 5:

[0668] The terminal receives additional questions and feedback from the user and sends them to the server. The server uses this feedback to improve the performance of the machine learning model using learning aids. The input in this step is new user feedback, and the output is an updated model that enables more accurate response generation.

[0669] 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.

[0670] This invention enables more personalized information delivery by incorporating an emotion engine into a system that supports the understanding of generative AI technology through interaction with the user. This system is implemented in a form in which a server, a terminal, and the user interact with each other.

[0671] The terminal provides an interface that accepts text or voice input from the user. When the user inputs a question about the generated AI, the data is sent to the server. The server uses a dialogue management module to analyze the input and identify the user's intended question and areas of difficulty. In this invention, an emotion engine is further used to recognize the user's emotions contained in the input data.

[0672] The server's emotion engine determines the user's emotional state by analyzing facial expressions from text, audio, or video. For example, it analyzes specific vocabulary and punctuation in the user's text input, as well as their tone and intonation, and changes in facial expressions to identify emotions such as anxiety, excitement, or confusion.

[0673] Based on the user's emotional state, the server's information module generates a response that takes this information into account. In addition to a standard response, the information module adds easing language and reassuring, simple explanations. For example, if the system analyzes that the user is confused, it will provide an explanation that includes reassuring language such as, "Don't worry, let me explain in more detail."

[0674] The generated response is sent back to the terminal and presented to the user. By adaptively responding to the user's emotions in this way, more effective information can be provided and user satisfaction can be improved. Furthermore, in order to utilize the user's feedback in the next interaction, the server continuously stores the interaction results and the user's emotion data in a learning module and updates the model.

[0675] Through these means, a system can be built that integrates emotion recognition and generative AI knowledge provision, making it possible to provide users with a more personalized experience.

[0676] The following describes the processing flow.

[0677] Step 1:

[0678] The device provides the user with an interface, prompting them to input questions or concerns about the generated AI. The user completes their input and presses the submit button.

[0679] Step 2:

[0680] The device sends the entered text or voice data to the server. This data is received by the server for later analysis.

[0681] Step 3:

[0682] The server receives user input and passes it to the dialogue management module, which uses natural language processing algorithms to analyze the text or speech. The purpose of the analysis is to identify the user's intentions, the content of their questions, and areas of difficulty.

[0683] Step 4:

[0684] The server's emotion engine further analyzes the user's input data to recognize their emotional state. Based on text vocabulary and punctuation, voice tone, and even the user's facial expressions derived from the text data, it identifies specific emotions the user is experiencing (e.g., confusion, anxiety, joy).

[0685] Step 5:

[0686] The server's information module generates the optimal response for the user based on the analyzed intent and emotional state. The response includes adjustments according to the emotional state and is composed of reassuring language and simple explanations.

[0687] Step 6:

[0688] The server sends the generated response to the terminal. The terminal displays this information to the user and, if necessary, also provides relevant visual information (e.g., images or video links).

[0689] Step 7:

[0690] Users can view the presented information and enter further questions or feedback. Any new input from the user is processed again through the process starting from step 2.

[0691] Step 8:

[0692] The server's learning module re-evaluates the data and emotional states collected during the interaction and updates the machine learning model. Through this update, the system's accuracy is improved so that it can provide more precise information and emotional responses in subsequent interactions.

[0693] (Example 2)

[0694] 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".

[0695] Conventional interactive systems have struggled to appropriately recognize users' emotions and provide information tailored to their individual needs. Furthermore, they have been unable to employ effective dialogue strategies for each user's different areas of difficulty, making it challenging to deepen user understanding. The challenge lies in resolving these issues and providing more personalized information.

[0696] 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.

[0697] In this invention, the server includes an integrated analysis means for analyzing user input and recognizing emotions, a response generation means for generating and providing relevant information based on the user's emotional state, and an analysis device for accumulating data collected through interaction with the user and using it to improve a learning model. This makes it possible to provide personalized information that is tailored to the user's emotions and individual needs.

[0698] A "communication device" is a device that receives input from a user and provides an interface for initiating a conversation.

[0699] An "integrated analysis means" is a function within the system that analyzes the user's input, performs emotion recognition, and identifies related information.

[0700] The "response generation means" is a function that generates information to be provided to the user based on the analyzed emotional state of the user.

[0701] An "analysis device" is a device that accumulates data obtained from interactions with users and uses that data to perform a process of improving the learning model.

[0702] "Emotional state" refers to a psychological state inferred from the user's input, and includes anxiety, excitement, confusion, etc.

[0703] A "learning model" is an algorithm or model that is updated using collected data to improve the accuracy and adaptability of subsequent interactions.

[0704] "Template information" is a set of predefined information based on multiple knowledge generation techniques that forms the basis for response generation.

[0705] A "dialogue strategy" refers to a method of conducting a dialogue that is optimized according to the user's areas of difficulty and emotional state.

[0706] This invention constitutes a system that provides personalized information through dialogue with the user, and aims to generate responses that take into account the user's emotional state. The following describes a specific form for implementing this system.

[0707] The server functions as an integrated analysis tool, analyzing the user's input data. This input data consists of text and audio data provided by the user via a terminal. The terminal is equipped with a microphone to convert the user's voice into a digital signal and a user interface for inputting text data. The dialogue begins when the user inputs the question, "How does the generative AI understand emotions?" into the terminal.

[0708] Input data sent from the terminal to the server is analyzed by an integrated analysis system within the server. The server uses natural language processing technology to identify the intent of the input and then uses an emotion engine to recognize the user's emotional state. Emotion recognition includes keyword extraction from text and tone analysis in the case of audio data. For example, vocabulary such as "difficult" or "I don't understand" in the user's questions, as well as emotions such as confusion or anxiety, can be detected from the intonation of the voice.

[0709] Based on the analysis results, the server's response generation mechanism generates information based on the user's emotional state. This response includes expressions designed to help the user relax. For example, if a user inputs, "The mechanism of the generating AI is complex and I don't understand it," the server will generate something like, "Don't worry. I'll explain it in an easy-to-understand way."

[0710] The generated response is sent from the server to the terminal, which then displays it as text or outputs it as audio to the user. In this way, the dialogue progresses through the provision of appropriate information based on emotion recognition, supporting a deeper understanding of the user.

[0711] Furthermore, the server stores the dialogue results in an analysis device and uses machine learning models to improve the accuracy of future dialogues. This enables the system to continuously improve and adapt, providing users with a higher level of personalized experience.

[0712] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0713] Step 1:

[0714] The device accepts text or voice input from the user. Specifically, the user enters a prompt such as, "How will the Generating AI answer my question?" This input is captured as digital data by the device and prepared to be sent to the server.

[0715] Step 2:

[0716] The terminal transmits the received input data to the server. The input data securely reaches the server's integrated analysis system via network communication. The input here is digitized text or audio data, which is transferred directly to the server.

[0717] Step 3:

[0718] The server uses integrated analysis tools to analyze the user's input data. During this process, natural language processing techniques are used to analyze the grammar and meaning of the text and identify the user's intent. The input string data is analyzed, and an output is obtained that identifies an "explanation based on the AI's response method."

[0719] Step 4:

[0720] The server uses an emotion engine to recognize the user's emotional state. It analyzes emotions from word choices in text and tone of voice. The input here is a specific part of the sentence analyzed in the previous step, and the output is a judgment such as "User's emotional state: confused, interested."

[0721] Step 5:

[0722] The server's response generation mechanism generates an appropriate response based on the identified intent and emotional state. It includes reassuring sentences to make the response easy for the user to understand. The input uses analysis results and emotional data, and the output is a response such as, "Don't worry. The generating AI has analyzed your question and will provide the most relevant information."

[0723] Step 6:

[0724] The server sends the generated response to the terminal. The response data is encoded in a digital format and sent to the terminal via network communication.

[0725] Step 7:

[0726] The terminal displays the response received from the server to the user. Specifically, it is displayed on the screen in text format or played back through the speaker using speech synthesis. This output allows the user to confirm the response from the server.

[0727] Step 8:

[0728] The server stores the results of user interactions in an analysis device and updates the learning model to utilize them in future interactions. This data accumulation and model updating form a feedback loop to improve the quality of subsequent interactions.

[0729] (Application Example 2)

[0730] 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".

[0731] In today's information society, generative AI technology is becoming mainstream, but users need specialized knowledge to fully understand and utilize these technologies. Furthermore, there is a lack of adequate support for the anxiety and confusion that users may experience when using generative AI, highlighting the need for user-friendly systems.

[0732] 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.

[0733] In this invention, the server includes emotion recognition means for analyzing the user's emotions and adjusting the response content based on the emotional state; reassurance providing means for presenting additional information to provide reassurance when the user feels anxious or confused; and dialogue control means for analyzing the user's input and identifying related information based on the generated data. As a result, the user receives responses that take emotions into consideration, deepens their understanding of the generation AI technology, and enables them to use the system with peace of mind.

[0734] A "user interface means" is an interface device that has the function of receiving input from the user and initiating a dialogue.

[0735] A "dialogue control means" is a control device that analyzes the user's input and identifies relevant information based on the generated data.

[0736] An "information provision device" is a device that provides identified information to the user and has the function of supporting understanding of the generated AI.

[0737] An "emotion recognition device" is a device equipped with the function of analyzing the user's emotions and adjusting the response content based on the emotional state.

[0738] A "learning tool" is a device that uses data collected through interaction with the user to improve the performance of a machine learning model.

[0739] A "means of providing reassurance" is a device that has the function of presenting additional information to provide reassurance when the user feels anxious or confused.

[0740] A "generative AI model" is a form of artificial intelligence used to generate responses or information for specific tasks.

[0741] A "prompt message" is a phrase that, when input into a generation AI, is used to generate appropriate responses or information.

[0742] The system for realizing this invention is a conversational AI assistant that integrates emotion recognition technology and is specifically designed for application in the field of electronic payments. The system's terminal primarily uses a smartphone as its hardware. The terminal receives voice and text input from the user and sends this data to a server for analysis.

[0743] On the server side, a program built using Python analyzes the input data. Specifically, it uses the OpenCV and PyDub libraries to analyze the tone of the audio data and the content of the text, extracting facial and audio features. Here, the user's emotional state is determined by an emotion recognition system. This emotional information then influences the response generated by the system.

[0744] The generative AI model is implemented using the Hugging Face Transformers library and generates responses based on user input and emotional state prompts. An example prompt is, "How would you reassure a user if they are surprised?" The server analyzes the generated responses and, through reassurance mechanisms, returns appropriate messages when the user feels anxious. Through this system, users can make electronic payments smoothly and with peace of mind.

[0745] For example, when a user makes a large payment, the system sends a reassuring message such as, "We have confirmed that this transaction is secure. You can cancel it at any time, so please rest assured." As a result, users can use electronic payments with greater confidence.

[0746] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0747] Step 1:

[0748] The server receives voice and text input from the user sent from the terminal. This input includes voice files and text data. This data is used as foundational information to analyze the user's emotional state and intentions.

[0749] Step 2:

[0750] The server inputs received audio and text data into an emotion recognition system to analyze the user's emotional state. Audio data undergoes tone analysis using PyDub, and facial features are analyzed using OpenCV to infer emotions such as joy or anxiety. The output is a quantitative evaluation indicating the user's emotions.

[0751] Step 3:

[0752] The server inputs text data into the dialogue control system to analyze the user's intent and requests. Using natural language processing technology, the analyzed information is constructed as prompt sentences for a generative AI model. The output is a prompt sentence that includes the user's intent.

[0753] Step 4:

[0754] The server's AI model generates the optimal response using the sentiment evaluation from step 2 and the prompt text from step 3. Using the Hugging Face Transformers library, a contextually adjusted response is generated based on the data input. The output is the final response text to be returned to the user.

[0755] Step 5:

[0756] The server adjusts the generated response message through a reassurance-providing mechanism and adds additional reassurance messages as needed. This provides information that alleviates the user's anxiety and provides a sense of security. The output is the final response message to the user.

[0757] Step 6:

[0758] The user's device receives the final response message and presents it to the user in either audio or text format. The user receives a reassuring response, allowing them to confidently proceed with electronic payments and the use of generative AI technology.

[0759] 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.

[0760] 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.

[0761] 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.

[0762] 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.

[0763] 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.

[0764] 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.

[0765] 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.

[0766] 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.

[0767] 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."

[0768] 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.

[0769] 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.

[0770] 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.

[0771] 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.

[0772] 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.

[0773] 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.

[0774] 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.

[0775] 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.

[0776] 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.

[0777] 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.

[0778] 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.

[0779] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0780] The following is further disclosed regarding the embodiments described above.

[0781] (Claim 1)

[0782] A user interface means that receives user input and initiates interaction,

[0783] A dialogue management means that analyzes the user's input and identifies related information based on the generated data,

[0784] Information provision means that provides the user with the identified information and supports understanding of the generated AI,

[0785] A learning method that improves the performance of a machine learning model using data collected through the aforementioned interaction with the user,

[0786] A system that includes this.

[0787] (Claim 2)

[0788] The system according to claim 1, wherein the dialogue management means has a function to identify the user's areas of difficulty and optimize the dialogue strategy based on this.

[0789] (Claim 3)

[0790] The system according to claim 1, wherein the information providing means has template information relating to multiple generation AI technologies and has a function to adaptively select it according to the user's input.

[0791] "Example 1"

[0792] (Claim 1)

[0793] An input device that receives user input and initiates interaction,

[0794] A dialogue management device that analyzes the user's input and identifies related information based on the generated data,

[0795] An information providing device that provides the user with the identified information and supports their understanding of the generated AI,

[0796] A learning device that improves the performance of a machine learning model using data collected through the aforementioned interaction with the user,

[0797] A communication device that transmits data from an input device to a server device and receives data from a server device to an input device,

[0798] The dialogue management device identifies the user's areas of difficulty by referring to the user's historical dialogue data, and the information provision device provides relevant specific examples based on generated AI technology.

[0799] A system that includes this.

[0800] (Claim 2)

[0801] The system according to claim 1, wherein the dialogue management device has a function to identify the user's areas of difficulty and optimize the dialogue strategy based on this.

[0802] (Claim 3)

[0803] The system according to claim 1, wherein the information providing device has template information relating to multiple generation AI technologies and has a function to adaptively select it according to the user's input.

[0804] "Application Example 1"

[0805] (Claim 1)

[0806] A user input / output means that receives user input and initiates dialogue,

[0807] A dialogue control means that analyzes the user's input and identifies related knowledge based on the generated information,

[0808] A knowledge-providing means that provides the user with the identified knowledge and supports their understanding of the generated AI,

[0809] A learning support means that improves the performance of a machine learning model using information collected through the aforementioned interaction with the user,

[0810] Information processing means that personalize information based on the user's history and interests,

[0811] A system that includes this.

[0812] (Claim 2)

[0813] The system according to claim 1, wherein the dialogue control means has a function to identify the user's areas of difficulty and optimize the dialogue strategy based on this.

[0814] (Claim 3)

[0815] The system according to claim 1, wherein the knowledge-providing means has template information relating to multiple generative AI technologies and has a function to flexibly select it according to the user's input.

[0816] "Example 2 of combining an emotion engine"

[0817] (Claim 1)

[0818] A communication device that receives user input and initiates dialogue,

[0819] An integrated analysis means for analyzing the user's input and recognizing emotions,

[0820] A response generation means that generates and provides relevant information based on the emotional state of the user,

[0821] An analytical device that stores data collected through the aforementioned interaction with the user and uses it to improve the learning model,

[0822] A system that includes this.

[0823] (Claim 2)

[0824] The system according to claim 1, wherein the integrated analysis means has a function to identify the user's weak areas and optimize the dialogue strategy accordingly.

[0825] (Claim 3)

[0826] The system according to claim 1, wherein the response generation means has template information based on a plurality of knowledge generation technologies and has a function to dynamically select it in accordance with the user's input.

[0827] "Application example 2 when combining with an emotional engine"

[0828] (Claim 1)

[0829] A user interface means that receives user input and initiates interaction,

[0830] A dialogue control means that analyzes the user's input and identifies related information based on the generated data,

[0831] Information provision means that provides the user with the identified information and supports understanding of the generated AI,

[0832] An emotion recognition means that analyzes the user's emotions and adjusts the response content based on the emotional state,

[0833] A learning method that improves the performance of a machine learning model using data collected through the aforementioned interaction with the user,

[0834] A means of providing reassurance that presents additional information to give a sense of security when the user feels anxious or confused,

[0835] A system that includes this.

[0836] (Claim 2)

[0837] The system according to claim 1, wherein the dialogue control means has a function to identify the user's areas of difficulty and optimize the dialogue strategy based on this, and further adaptively changes the content of the dialogue using the emotion recognition means.

[0838] (Claim 3)

[0839] The system according to claim 1, wherein the information providing means has formal information relating to multiple generative AI technologies and has a function to adaptively select it according to the user's input and emotional state. [Explanation of symbols]

[0840] 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 user input / output means that receives user input and initiates dialogue, A dialogue control means that analyzes the user's input and identifies related knowledge based on the generated information, A knowledge-providing means that provides the user with the identified knowledge and supports their understanding of the generated AI, A learning support means that improves the performance of a machine learning model using information collected through the aforementioned interaction with the user, Information processing means that personalize information based on the user's history and interests, A system that includes this.

2. The system according to claim 1, wherein the dialogue control means has a function to identify the user's areas of difficulty and optimize the dialogue strategy based on this.

3. The system according to claim 1, wherein the knowledge-providing means has template information relating to multiple generation AI technologies and has a function to flexibly select it according to the user's input.

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