system

Abstract

We provide the system. [Solution] A means of providing an interface for expert information providers to input their own knowledge and speaking style, A means for customizing an artificial intelligence model based on input data from the aforementioned expert information provider, A means of providing an online platform for the buyer to search for and acquire the customized artificial intelligence agent, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, 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 character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In a conventional dialogue system based on artificial intelligence technology, generalized information provision and responses are mainstream, and it has been difficult to provide consumers with a dialogue experience that reflects the knowledge and unique ways of speaking of individual specialized information providers. In addition, there are limited opportunities for specialized information providers to specifically utilize their knowledge and directly provide value to consumers, and the associated opportunities for monetization are not sufficient. Thus, the lack of an effective dialogue platform for both specialized information providers and consumers is an issue.

Means for Solving the Problems

[0005] This invention provides an interface that allows expert information providers to input their own knowledge and speaking style to customize an artificial intelligence (AI) model. Furthermore, by enabling consumers to search, acquire, and use the customized AI agent through an online platform, it realizes a specialized conversational experience in natural language tailored to their individual interests and needs. This system has the capability to continuously update and improve the AI ​​model by utilizing consumer feedback. As a result, expert information providers can effectively communicate their expertise, and consumers can enjoy a more personalized information experience.

[0006] A "specialized information provider" is an individual or organization that possesses knowledge and experience in a specific field and is able to provide that knowledge.

[0007] "Knowledge" refers to an understanding of and skills regarding information, facts, and theories related to a particular domain.

[0008] "Talk style" is a concept that includes the characteristics of a speaker's way of speaking, communication methods, and tone of voice.

[0009] An "artificial intelligence model" is a computational model that uses machine learning and natural language processing techniques to generate output based on input data and interact with users.

[0010] An "interface" is a user interface that provides a means for expert information providers to input knowledge and information in a conversational style into a system.

[0011] An "online platform" is a system that provides consumers with a space to search for, obtain, and use customized artificial intelligence agents from expert information providers via the internet.

[0012] A "consumer" is an individual or group that uses the provided artificial intelligence agent for the purpose of receiving information.

[0013] "Feedback" refers to the opinions and evaluations that consumers provide based on their experience using artificial intelligence agents, and is information used to improve the system. [Brief explanation of the drawing]

[0014] [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]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

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

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

[0017] In the following embodiments, a 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.

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

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

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

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

[0022] [First Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0035] To implement this invention, a professional information provider must first access the platform and create an account. Through the platform's interface, the user can input their expertise and speaking style characteristics. Based on this, the server customizes a dedicated artificial intelligence model based on the received data.

[0036] The server integrates the provided knowledge data and speech style into an artificial intelligence agent using natural language processing techniques and machine learning algorithms. This creates an agent that possesses user-specific knowledge and engages in conversations with a unique speaking style. This process includes steps such as analyzing voice samples and clustering text data.

[0037] On the other hand, general consumers (users) can access the online platform using their own devices. Consumers can search for areas of interest and expert information providers, and purchase or subscribe to customized artificial intelligence agents. After completing the purchase process, they can use the agent through a dedicated application on their device or access it via a cloud-based system.

[0038] As a concrete example, consider a scenario where a culinary expert inputs their culinary theories and teaching methods to generate an agent. The server analyzes the provided recipe and cooking technique data and builds a cooking-specific artificial intelligence agent based on that analysis. Consumers can use this agent to receive real-time answers to cooking-related questions and advice, thereby easily applying expert knowledge at home.

[0039] Furthermore, the server can collect feedback from consumers and use that feedback to continuously improve the AI ​​model. This will make the information provided by the agent even more accurate, enabling more natural and engaging conversations.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] Users (expert information providers) register an account on the platform and access a dedicated dashboard. On the dashboard, they input information about their expertise and speaking style, and upload audio samples and text data to the system as needed.

[0043] Step 2:

[0044] The server receives data provided by the user and prepares it for incorporation into the artificial intelligence model. Specifically, it divides text data, performs preprocessing for natural language processing, and extracts features of the speaking style through analysis of voice samples.

[0045] Step 3:

[0046] The server applies machine learning algorithms to pre-processed data to generate an artificial intelligence agent that reflects the user's knowledge and unique speaking style. This also includes a process of simulating conversations.

[0047] Step 4:

[0048] Users can test the generated artificial intelligence agent to verify that it performs as expected. Testing is conducted on a dashboard, and users can provide feedback and request further tuning as needed.

[0049] Step 5:

[0050] The server improves and adjusts the artificial intelligence model based on user feedback. The adjusted model is then presented to the user again for final confirmation before being finalized.

[0051] Step 6:

[0052] If a user decides to make their agent public, that agent will be listed on the platform and made accessible to consumers. Pricing and distribution terms are also set up at this stage.

[0053] Step 7:

[0054] The consumer's device searches for agents of interest through an online platform and completes the purchase process. After purchase, the consumer can download agents and use cloud-based services.

[0055] Step 8:

[0056] The device (the consumer's device) can interact with an agent and receive expert-based advice and information. Users can also input feedback during use, and this information is collected on the server.

[0057] Step 9:

[0058] The server continuously improves the model based on consumer feedback and performs data analysis to enhance the quality of the AI ​​agent. When changes are made, the user is notified again, and the agent is updated accordingly.

[0059] (Example 1)

[0060] 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."

[0061] There is a need for a system that allows individuals with specialized knowledge to efficiently deploy their knowledge and communication style on digital platforms and provide it to a diverse range of users. Furthermore, it is crucial that the generated artificial intelligence functions reflect user feedback and are always provided in the most up-to-date and optimal form.

[0062] 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.

[0063] In this invention, the server includes means for providing an information processing device for expert information providers to input their expertise and communication style, means for adjusting an artificial intelligence processing device based on the input information from the expert information provider, and means for providing an electronic information infrastructure for users to explore and obtain the adjusted artificial intelligence functions. This ensures that artificial intelligence functions based on the expert information provider's knowledge are provided to users in the most up-to-date and optimal form, and that continuous improvement is possible through user feedback.

[0064] A "specialized information provider" refers to an individual or organization that possesses specific knowledge or skills and provides that information in a way that is useful to others.

[0065] An "information processing device" refers to an electronic or software-based means that receives data, analyzes it, and outputs it in a specific format.

[0066] "Communication style" refers to the overall style of communication, including the language, expressions, and characteristics of word choice used when conveying information.

[0067] An "artificial intelligence processing device" refers to a device or software that uses technologies such as machine learning and natural language processing to analyze data and enable decision-making and dialogue.

[0068] "Electronic information infrastructure" refers to a foundational system for organizing, storing, and providing data and information to users through the internet and other electronic means.

[0069] "User" refers to an individual or organization that utilizes the information and functions provided through this system.

[0070] "Feedback" refers to information regarding the evaluation of a system or its functions, such as opinions, impressions, and suggestions for improvement provided by users.

[0071] To implement this invention, a dedicated software platform is required. An embodiment thereof is shown below.

[0072] First, users, who are professional information providers, access the online platform using a personal computer or tablet as an information processing device. There, an interface is provided for inputting their expertise and communication style. This allows users to electronically register their professional information. For example, a culinary expert can describe their recipes and cooking methods.

[0073] Next, the server receives the provided input data, analyzes it using an artificial intelligence processing unit, and builds a customized generative AI model. This process utilizes software libraries such as "TENSORFLOW®" and "SpaCy" to automatically analyze the data and optimize the model. The server ensures that the model is appropriately adjusted to take the user's communication style into consideration.

[0074] Subsequently, users with devices can access the online platform through the electronic information infrastructure, search for generated artificial intelligence functions, and use them. Users log in to the platform using their smartphones or PCs and interact with agents who are expert information providers of their interest. For example, with a cooking-focused agent, users can enter prompts such as "Tell me how to cook pasta" or "Give me some easy dinner ideas" to receive direct advice.

[0075] Furthermore, the server continuously collects feedback from users and improves the artificial intelligence model. This feedback improves the quality of the information provided over time, making it more valuable to users. Feedback can be easily submitted through the platform, quickly analyzed by the server, and the model is updated as needed.

[0076] In this way, a system is realized in which expert information providers and users collaborate with each other, enabling the efficient transfer and dissemination of knowledge.

[0077] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0078] Step 1:

[0079] Professional information providers, as users, access the online platform using a PC or tablet and create an account. They input their expertise and communication style through the interface. This input data serves to electronically record the characteristics of their expertise and style and is transmitted to the server.

[0080] Step 2:

[0081] The server analyzes the input data received from the user. This analysis utilizes natural language processing libraries such as TensorFlow and SpaCy. Specifically, the server uses these libraries to analyze text data, extracting technical terms and patterning speech styles. This process allows the generative AI model to be refined based on the features obtained from the input data.

[0082] Step 3:

[0083] The server builds an artificial intelligence agent based on a finely tuned generative AI model and registers its functions in the electronic information infrastructure. The agent includes a program to replicate the user's communication style and is ready for user access.

[0084] Step 4:

[0085] Users with a device access the online platform using their smartphone or computer. By searching for and selecting an AI agent of an expert information provider of their interest, they prepare to interact with the agent through prompts. This allows users to input specific questions, such as "How do I cook pasta?", and receive direct advice.

[0086] Step 5:

[0087] The server receives questions and feedback from users and records the agent's responses. This data is used to evaluate the performance of the AI ​​model, reanalyze the data as needed, and adjust model parameters to improve system performance. Continuously incorporating feedback enables more accurate and user-friendly responses.

[0088] (Application Example 1)

[0089] 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."

[0090] Modern consumers demand highly responsive information tailored to their diverse tastes and needs. However, general-purpose information sources cannot fully address individual needs, creating a demand for information specialized in specific areas. Furthermore, there is a need for real-time responsiveness and continuous improvement based on accurate feedback, and there is a lack of means to enhance the consumer experience in this way.

[0091] 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.

[0092] In this invention, the server includes means for providing an interface for experts to offer their knowledge and communication skills, means for generating application-relevant recommendations based on user preference information, and means for responding to user behavior in real time and guiding the process. This enables the provision of specialized information tailored to individual consumers, improving the consumer experience and providing highly satisfying services.

[0093] An "expert" is a person who possesses advanced knowledge and skills in a specific field and is able to provide that knowledge to others.

[0094] An "interface" is a point of contact or means for a user to interact with a system or device.

[0095] A "learning model" is an algorithm that has been trained to identify patterns based on data and to make predictions and judgments in response to new inputs.

[0096] An "online infrastructure" refers to services and platforms provided via the internet, providing an environment where users can access or exchange digital information.

[0097] A "user" is an individual or group that uses the system or service.

[0098] "Preference information" refers to data that indicates a user's preferences and interests, and is an element used to provide personalized services.

[0099] "Recommendations" refer to options or actions suggested based on the user's needs and preferences.

[0100] "Real-time response" refers to a process that allows for quick responses on the spot, enabling smooth communication with users.

[0101] The system for implementing this invention mainly consists of server, terminal, and user interaction.

[0102] The server receives knowledge and discourse provided by experts through the necessary interfaces and can adjust its learning model accordingly. This model is generated using natural language processing techniques and machine learning algorithms, and after adjustment, it is provided to users via an online platform. Specific software used includes OpenAI's GPT and TensorFlow. This enables the provision of precise information.

[0103] The terminal connects to an online infrastructure, providing users with access to a tailored learning agent. Users can use the terminal to input their preferences and receive recommendations based on that information. This data processing involves real-time analysis of the user's input data and calculations to provide personalized recommendations.

[0104] As a concrete example, if a user requests a recipe, they would enter a prompt message into their terminal such as, "Suggest a Japanese-style dish that doesn't use nuts." Based on this, the server would use a individually tailored agent model to recommend relevant recipes and guide the user through each step. For example, by using a prompt message like, "I want to eat Japanese food today, but I have an allergy, so please make it without nuts," the user can receive appropriate menu suggestions tailored to their preferences.

[0105] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0106] Step 1:

[0107] The server receives knowledge and speech data from experts through an interface. Based on this input data, it adjusts the learning model using natural language processing techniques and machine learning algorithms. By analyzing the data provided by experts and appropriately incorporating it into the model, it generates an AI agent that reflects individual knowledge.

[0108] Step 2:

[0109] The terminal receives preference information and prompt messages from the user. A specific prompt message might be, "Suggest a Japanese-style dish that doesn't use nuts." Based on this input, the terminal sends a request to the server.

[0110] Step 3:

[0111] The server utilizes a generative AI model based on the received preference information to generate corresponding recommendations. Data processing involves analyzing user input and extracting information from related databases. As a result, it generates a list of recommended dishes that match the user's requests and returns it to the terminal.

[0112] Step 4:

[0113] The terminal displays recommendations returned from the server to the user. The output list of recommended dishes is appropriately formatted for visual clarity and to provide an easy-to-use interface for the user to select from.

[0114] Step 5:

[0115] The user selects an individual dish from the presented list of recommended dishes. The selected information is then sent back to the server by the terminal.

[0116] Step 6:

[0117] The server generates a detailed cooking guide based on the selected dish. This process can include step-by-step instructions and time management advice. The generated guide information is then sent back to the terminal.

[0118] Step 7:

[0119] The terminal provides the user with the received cooking guide in real time. As the user progresses with the cooking, the terminal displays the next steps sequentially, supporting their progress.

[0120] 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.

[0121] This invention provides a system that combines an artificial intelligence agent that reflects the knowledge and speaking style of a professional information provider with an emotion engine that recognizes the user's emotions and appropriately adjusts the content of the conversation.

[0122] To implement this system, expert information providers must first access the platform and create an account. Users can input their expertise and speaking style characteristics through the interface and upload necessary voice samples, etc. Based on this data, the server builds an artificial intelligence model and forms a customized agent.

[0123] Furthermore, users can configure the emotion engine to determine how the agent recognizes the user's emotions and adjusts its responses accordingly. This emotion engine includes algorithms that analyze emotions from voice tone and text, and optimize responses based on the resulting emotional state.

[0124] Consumers (other users) access the platform using their own devices and search for AI agents that interest them. After purchase, consumers run the agent in the cloud or locally and begin interacting with it. The system uses an emotion engine to analyze the consumer's emotional state in real time and generate the most appropriate response for that state. For example, if the user is feeling stressed, it prioritizes providing calming tones and relaxing information.

[0125] This system allows the server to continuously improve its artificial intelligence model based on consumer feedback and the results of emotion engine analysis. As a result, it can provide consumers with a more personalized and advanced conversational experience.

[0126] The following describes the processing flow.

[0127] Step 1:

[0128] Users (expert information providers) create an account on the platform and log in. Through the interface, they input their expertise and speaking style characteristics, and upload audio samples as needed.

[0129] Step 2:

[0130] The server receives data sent by the user and preprocesses it. Text data is analyzed using natural language processing, and characteristics of the user's speech are extracted from audio samples.

[0131] Step 3:

[0132] The server customizes the artificial intelligence model based on the data obtained. It generates an AI agent that reflects the user's knowledge and speaking style, and also connects an emotion engine to the agent.

[0133] Step 4:

[0134] Users test the generated artificial intelligence agent on a dashboard. Simultaneously, they verify the operation of the emotion engine built into the agent and send feedback to the server as needed.

[0135] Step 5:

[0136] The server receives feedback from the user and adjusts the artificial intelligence model and emotion engine. The adjusted model is then presented to the user again for final confirmation.

[0137] Step 6:

[0138] The device (consumer's device) accesses the platform and searches for an AI agent of interest. After completing the purchase process, the agent becomes available.

[0139] Step 7:

[0140] The device (the consumer's device) activates the purchased artificial intelligence agent and begins a conversation. The emotion engine analyzes the consumer's voice tone and text to determine their emotions, and the agent adjusts its response based on the analysis results.

[0141] Step 8:

[0142] The device (the consumer's device) allows consumers to receive appropriate advice based on their emotional changes through an interactive experience with an agent. Consumer usage data and feedback are sent to a server.

[0143] Step 9:

[0144] The server continuously improves its artificial intelligence model and emotion engine based on feedback and sentiment analysis data collected from consumers. This improves the accuracy and naturalness of the agent's responses.

[0145] (Example 2)

[0146] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0147] The problem that this invention aims to solve is to build intelligent agents that reflect the characteristics of individuals with specialized knowledge, and to improve the accuracy and effectiveness of the personalized conversational experience that users obtain through those agents. Furthermore, it aims to provide a more sophisticated communication experience by automatically adjusting responses according to the user's emotions and through continuous model improvement.

[0148] 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.

[0149] In this invention, the server includes means for providing an interface for an individual with specialized knowledge to input their knowledge and speech patterns, means for individually adjusting an intelligent processing model based on the individual's input information, and means for setting up an emotion analysis device and generating a response that matches the user's emotional state. This makes it possible to construct a customized conversational agent that combines expertise and emotion recognition.

[0150] A "specialized individual" is someone who possesses advanced knowledge and skills in a specific field and provides information to agents in order to utilize that knowledge.

[0151] An "interface" is a means of inputting and outputting information between a user and a system, and a mechanism for facilitating smooth interaction with the system.

[0152] An "intelligent processing model" is a framework that uses artificial intelligence technology to learn from input data and generate inferences and responses.

[0153] A "space on a communication network" refers to online platforms and services that can be accessed via networks such as the internet, and is a place where information can be shared and obtained.

[0154] An "emotion analysis device" is a device or system that analyzes a user's emotional state from information such as voice and text, and provides appropriate responses or services corresponding to those emotions.

[0155] "Dialogue using language data" refers to the exchange of information using language, and is a form of communication that includes questions and answers using natural language.

[0156] "Opinions" refer to feedback and evaluation information provided by users and stakeholders, and are valuable information that can be used to improve systems and services.

[0157] To implement this invention, the server constructs a customized intelligent agent using a generative AI model based on data provided by individuals with specialized knowledge. Specifically, the user inputs their expertise and speaking style through an interface using a terminal and uploads voice samples as needed. This information is sent to the server, and an intelligent processing model is generated.

[0158] The server also analyzes the user's emotional state in real time using an emotion analysis device. This analysis may involve using software equipped with natural language processing technology. For example, it could include technology that understands the emotional state and provides an appropriate response based on voice and text data provided by the user.

[0159] A concrete example is an intelligent agent designed for psychological counseling. Users can input and configure their expertise in psychology and their calm speaking style to build an agent that suggests appropriate relaxation methods to stressed consumers.

[0160] The following are examples of prompt statements that are used.

[0161] "Like a skilled psychological counselor, provide advice on how to calm users when they are feeling stressed. Suggest specific ways for consumers to relax."

[0162] In this way, it becomes possible to provide personalized conversational experiences tailored to each individual user.

[0163] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0164] Step 1:

[0165] Users input their expertise and speaking style on their device and upload any necessary audio samples.

[0166] Input: User expertise, speaking style, voice sample

[0167] Operation: The user interacts with the interface to provide the necessary input data.

[0168] Output: Expertise and talk style data are sent to the server.

[0169] Step 2:

[0170] The server receives input data and customizes the intelligent processing model using a generative AI model.

[0171] Input: User-submitted expertise, talk style data, and audio samples.

[0172] Operation: The server utilizes a generated AI model to build an intelligent agent that reflects the user's characteristics. Specifically, it performs data analysis and model training.

[0173] Output: Customized intelligent agent model

[0174] Step 3:

[0175] The user configures the emotion analysis device on their terminal.

[0176] Input: Setting parameters related to sentiment analysis (e.g., sentiment detection threshold and response pattern)

[0177] Operation: The user selects emotion engine settings on the interface and sends those parameters to the server.

[0178] Output: The settings are saved on the server and reflected in the intelligent agent's response.

[0179] Step 4:

[0180] Consumers access the platform on their devices, search for agents, and make purchases.

[0181] Input: Consumer search queries and agent selection information

[0182] Operation: Consumers select agents of interest from a list and proceed with the purchase.

[0183] Output: Purchase completion notification and agent usage rights

[0184] Step 5:

[0185] The consumer activates an intelligent agent on their device and begins a dialogue.

[0186] Input: Consumer voice and text-based interactive input

[0187] Operation: Consumers communicate with the agent using a conversational interface. The agent performs sentiment analysis and generates appropriate responses.

[0188] Output: Personalized response from the agent

[0189] Step 6:

[0190] The server collects consumer feedback and interaction data, and continuously improves its intelligent processing model.

[0191] Input: Consumer feedback data, dialogue logs

[0192] Operation: The server analyzes feedback and interaction logs to adjust and improve the model.

[0193] Output: Updated intelligent agent model

[0194] In this way, an individualized conversational experience is provided by an agent that combines specialized knowledge and emotion recognition.

[0195] (Application Example 2)

[0196] 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".

[0197] In an aging society, the number of elderly people requiring care is increasing. Elderly people often experience feelings of loneliness and anxiety in their daily lives, which can impair their mental health. Traditional care systems are limited to simple information provision and bureaucratic interactions, lacking emotional support that considers the feelings of the elderly. Therefore, there is a need for techniques that enable flexible communication tailored to their emotional state.

[0198] 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.

[0199] In this invention, the server includes means for providing an interface for expert information providers to input their knowledge and speaking style, means for customizing an artificial intelligence model based on the input data from the expert information providers, and means for analyzing the user's emotional state via an emotion analysis engine and optimizing the response. This enables the generation of responses tailored to the emotional state of elderly individuals, providing personalized emotional support to the user.

[0200] A "specialized information provider" is an individual or organization that possesses extensive knowledge and experience in a specific field and has the ability to provide that knowledge and communication skills.

[0201] An "interface" is a means or device used by a user to input information or interact with a system.

[0202] An "artificial intelligence model" is a computational tool that makes judgments and predictions based on input data according to a specific purpose, and has the ability to automate specific tasks.

[0203] An "online platform" is a virtual environment on the internet for users to search for, select, and obtain specific services or products.

[0204] An "emotion analysis engine" is a technology or method for analyzing and understanding a user's emotional state from voice or text.

[0205] "Optimizing responses" is the process of selecting and providing the most appropriate and effective response or action based on the user's emotions and situation.

[0206] "Support for the elderly" refers to assistance and services provided to alleviate the mental and physical burdens on elderly people in their daily lives.

[0207] To realize this invention, expert information providers are required to access a server and input their knowledge and speaking style through an interface. In particular, expert information providers upload voice samples and text data so that the AI ​​model can be customized to reflect their characteristics. On the server, a generative AI model is built based on this data, and a customized artificial intelligence agent is formed. At this time, the construction of the AI ​​model uses the natural language processing library spaCy in Python, and Google's Speech-to-Text API for speech processing, among others.

[0208] Users who purchase the product can access the online platform using their own devices to search for and acquire artificial intelligence agents that interest them. The emotion analysis engine installed on the device analyzes the user's tone of voice and speech content in real time to determine the user's emotional state. Based on this analysis, the customized AI agent generates optimized responses to support the elderly. The responses are generated considering the user's psychological state, aiming to provide the user with a sense of security and satisfaction.

[0209] As a concrete example, if an elderly person feels lonely during a daily conversation, the AI ​​agent will engage in dialogue based on prompts such as, "Good morning, how are you feeling today? Is there anything that's bothering you about the recent weather?" to provide encouragement and comfort. Through such dialogue, personalized emotional support for the elderly is realized.

[0210] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0211] Step 1:

[0212] The server receives data on knowledge and speaking style from expert information providers. This data includes audio samples and text information. Using this data as input, the generative AI model is customized to reflect the characteristics of the expert information providers.

[0213] Step 2:

[0214] The server builds a generative AI model and creates a customized artificial intelligence agent. This process uses a natural language processing library (e.g., spaCy) to parse text data, and speech data is transcribed via Google's Speech-to-Text API. This results in an AI agent that reflects the speaking style of the expert informant.

[0215] Step 3:

[0216] The user accesses an online platform using their device and searches for an artificial intelligence agent that interests them. Once the user selects an agent, the server sends that agent to the user's device.

[0217] Step 4:

[0218] The sentiment analysis engine running on the device analyzes the user's voice and text input in real time. In this step, it analyzes the voice tone and selected words as input to determine the user's emotional state. The results of the sentiment analysis are output and transmitted to the AI ​​agent.

[0219] Step 5:

[0220] The AI ​​agent generates the optimal response based on the results of emotion analysis. The generated response is adjusted to support the psychological health of the elderly and output to the user as voice or text on the device. An example of a prompt used at this stage would be, "Good morning, how are you feeling today? Is there anything that the recent weather has been bothering you?"

[0221] 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.

[0222] 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.

[0223] 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.

[0224] [Second Embodiment]

[0225] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0226] 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.

[0227] 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).

[0228] 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.

[0229] 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.

[0230] 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).

[0231] 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.

[0232] 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.

[0233] 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.

[0234] 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.

[0235] 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.

[0236] 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".

[0237] To implement this invention, a professional information provider must first access the platform and create an account. Through the platform's interface, the user can input their expertise and speaking style characteristics. Based on this, the server customizes a dedicated artificial intelligence model based on the received data.

[0238] The server integrates the provided knowledge data and speech style into an artificial intelligence agent using natural language processing techniques and machine learning algorithms. This creates an agent that possesses user-specific knowledge and engages in conversations with a unique speaking style. This process includes steps such as analyzing voice samples and clustering text data.

[0239] On the other hand, general consumers (users) can access the online platform using their own devices. Consumers can search for areas of interest and expert information providers, and purchase or subscribe to customized artificial intelligence agents. After completing the purchase process, they can use the agent through a dedicated application on their device or access it via a cloud-based system.

[0240] As a concrete example, consider a scenario where a culinary expert inputs their culinary theories and teaching methods to generate an agent. The server analyzes the provided recipe and cooking technique data and builds a cooking-specific artificial intelligence agent based on that analysis. Consumers can use this agent to receive real-time answers to cooking-related questions and advice, thereby easily applying expert knowledge at home.

[0241] Furthermore, the server can collect feedback from consumers and use that feedback to continuously improve the AI ​​model. This will make the information provided by the agent even more accurate, enabling more natural and engaging conversations.

[0242] The following describes the processing flow.

[0243] Step 1:

[0244] Users (expert information providers) register an account on the platform and access a dedicated dashboard. On the dashboard, they input information about their expertise and speaking style, and upload audio samples and text data to the system as needed.

[0245] Step 2:

[0246] The server receives data provided by the user and prepares it for incorporation into the artificial intelligence model. Specifically, it divides text data, performs preprocessing for natural language processing, and extracts features of the speaking style through analysis of voice samples.

[0247] Step 3:

[0248] The server applies machine learning algorithms to pre-processed data to generate an artificial intelligence agent that reflects the user's knowledge and unique speaking style. This also includes a process of simulating conversations.

[0249] Step 4:

[0250] Users can test the generated artificial intelligence agent to verify that it performs as expected. Testing is conducted on a dashboard, and users can provide feedback and request further tuning as needed.

[0251] Step 5:

[0252] The server improves and adjusts the artificial intelligence model based on user feedback. The adjusted model is then presented to the user again for final confirmation before being finalized.

[0253] Step 6:

[0254] If a user decides to make their agent public, that agent will be listed on the platform and made accessible to consumers. Pricing and distribution terms are also set up at this stage.

[0255] Step 7:

[0256] The consumer's device searches for agents of interest through an online platform and completes the purchase process. After purchase, the consumer can download agents and use cloud-based services.

[0257] Step 8:

[0258] The device (the consumer's device) can interact with an agent and receive expert-based advice and information. Users can also input feedback during use, and this information is collected on the server.

[0259] Step 9:

[0260] The server continuously improves the model based on consumer feedback and performs data analysis to enhance the quality of the AI ​​agent. When changes are made, the user is notified again, and the agent is updated accordingly.

[0261] (Example 1)

[0262] 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."

[0263] There is a need for a system that allows individuals with specialized knowledge to efficiently deploy their knowledge and communication style on digital platforms and provide it to a diverse range of users. Furthermore, it is crucial that the generated artificial intelligence functions reflect user feedback and are always provided in the most up-to-date and optimal form.

[0264] 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.

[0265] In this invention, the server includes means for providing an information processing device for expert information providers to input their expertise and communication style, means for adjusting an artificial intelligence processing device based on the input information from the expert information provider, and means for providing an electronic information infrastructure for users to explore and obtain the adjusted artificial intelligence functions. This ensures that artificial intelligence functions based on the expert information provider's knowledge are provided to users in the most up-to-date and optimal form, and that continuous improvement is possible through user feedback.

[0266] A "specialized information provider" refers to an individual or organization that possesses specific knowledge or skills and provides that information in a way that is useful to others.

[0267] An "information processing device" refers to an electronic or software-based means that receives data, analyzes it, and outputs it in a specific format.

[0268] "Communication style" refers to the overall style of communication, including the language, expressions, and characteristics of word choice used when conveying information.

[0269] An "artificial intelligence processing device" refers to a device or software that uses technologies such as machine learning and natural language processing to analyze data and enable decision-making and dialogue.

[0270] "Electronic information infrastructure" refers to a foundational system for organizing, storing, and providing data and information to users through the internet and other electronic means.

[0271] "User" refers to an individual or organization that utilizes the information and functions provided through this system.

[0272] "Feedback" refers to information regarding the evaluation of a system or its functions, such as opinions, impressions, and suggestions for improvement provided by users.

[0273] To implement this invention, a dedicated software platform is required. An embodiment thereof is shown below.

[0274] First, users, who are professional information providers, access the online platform using a personal computer or tablet as an information processing device. There, an interface is provided for inputting their expertise and communication style. This allows users to electronically register their professional information. For example, a culinary expert can describe their recipes and cooking methods.

[0275] Next, the server receives the provided input data, analyzes it using an artificial intelligence processing unit, and builds a customized generative AI model. This process utilizes software libraries such as "TensorFlow" and "SpaCy" to automatically analyze the data and optimize the model. The server ensures that the model is appropriately adjusted, taking into account the user's communication style.

[0276] Subsequently, users with devices can access the online platform through the electronic information infrastructure, search for generated artificial intelligence functions, and use them. Users log in to the platform using their smartphones or PCs and interact with agents who are expert information providers of their interest. For example, with a cooking-focused agent, users can enter prompts such as "Tell me how to cook pasta" or "Give me some easy dinner ideas" to receive direct advice.

[0277] Furthermore, the server continuously collects feedback from users and improves the artificial intelligence model. With this feedback, the quality of the information provided improves over time and becomes more valuable to users. Feedback can be easily submitted through the platform, quickly analyzed by the server, and the model updated as needed.

[0278] In this way, by enabling specialized information providers and users to cooperate with each other, a system is realized in which the inheritance and dissemination of knowledge are efficiently carried out.

[0279] The flow of the specific process in Example 1 will be described using FIG. 11.

[0280] Step 1:

[0281] The specialized information provider, who is the user, uses a personal computer or tablet to access the online platform and create an account. Through the interface, the user inputs their specialized knowledge and communication style. This input data serves to electronically record the characteristics of the specialized knowledge and style and is sent to the server.

[0282] Step 2:

[0283] The server analyzes the input data received from the user. For this analysis, natural language processing libraries such as "TensorFlow" and "SpaCy" are used. Specifically, the server uses these libraries to analyze the text data, extract technical terms, and patternize the talk style. Through this process, the AI model generated based on the features obtained from the input data is adjusted.

[0284] Step 3:

[0285] The server constructs an artificial intelligence agent based on the adjusted generative AI model and registers its functions in the electronic information infrastructure. The agent contains a program for reproducing the user's communication style and is ready for the user to access.

[0286] Step 4:

[0287] Users with terminals access the online platform using smartphones or personal computers. By searching for and selecting the artificial intelligence agents of specialized information providers of interest, they prepare to interact with the agents through prompt sentences. Through this operation, users can input specific questions such as "Please teach me how to cook pasta" and directly receive advice.

[0288] Step 5:

[0289] The server receives questions and feedback from the user and records the agent's responses. These data are used to evaluate the performance of the AI model, re-analyze the data and adjust the model parameters as needed to improve the system performance. By continuously incorporating feedback, more accurate and user-friendly responses become possible.

[0290] (Application Example 1)

[0291] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0292] Modern consumers demand highly responsive information provision that caters to diverse tastes and needs. However, general information sources cannot fully meet individual needs, and information provision specialized in specific fields is required. Also, continuous improvement based on real-time responsiveness and accurate feedback is necessary, and there is a lack of means to improve the consumer experience.

[0293] 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.

[0294] In this invention, the server includes means for providing an interface for experts to offer their knowledge and communication skills, means for generating application-relevant recommendations based on user preference information, and means for responding to user behavior in real time and guiding the process. This enables the provision of specialized information tailored to individual consumers, improving the consumer experience and providing highly satisfying services.

[0295] An "expert" is a person who possesses advanced knowledge and skills in a specific field and is able to provide that knowledge to others.

[0296] An "interface" is a point of contact or means for a user to interact with a system or device.

[0297] A "learning model" is an algorithm that has been trained to identify patterns based on data and to make predictions and judgments in response to new inputs.

[0298] An "online infrastructure" refers to services and platforms provided via the internet, providing an environment where users can access or exchange digital information.

[0299] A "user" is an individual or group that uses the system or service.

[0300] "Preference information" refers to data that indicates a user's preferences and interests, and is an element used to provide personalized services.

[0301] "Recommendations" refer to options or actions suggested based on the user's needs and preferences.

[0302] "Real-time response" refers to a process that can quickly reply on the spot and smoothly proceed with the interaction with the user.

[0303] The system for implementing this invention is mainly composed of the interaction among the server, the terminal, and the user.

[0304] The server can receive the knowledge and speaking methods provided by experts through the necessary interfaces and adjust the learning model based on them. This model is generated using natural language processing technology and machine learning algorithms, and after adjustment, it is provided to users through an online platform. Specific software such as OpenAI's GPT and TensorFlow are used. This enables precise information provision.

[0305] The terminal connects to the online platform and provides an environment where users can access the adjusted learning agent. Users can use the terminal to input their preference information and receive recommendations generated based on it. The data processing at this time includes analyzing the user's input data in real time and performing operations to provide individual recommendations.

[0306] As a specific example, when a user requests a cooking suggestion, the user inputs a prompt sentence "Suggest a dish with a Japanese taste and no nuts" into the terminal. Based on this, the server uses an individually adjusted agent model to recommend relevant recipes and guide the user through each step. For example, by using the prompt sentence "I want to eat Japanese food today, but I have allergies, so please without nuts", it is possible to receive an appropriate menu suggestion according to the preference.

[0307] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0308] Step 1:

[0309] The server receives knowledge and speech data from experts through an interface. Based on this input data, it adjusts the learning model using natural language processing techniques and machine learning algorithms. By analyzing the data provided by experts and appropriately incorporating it into the model, it generates an AI agent that reflects individual knowledge.

[0310] Step 2:

[0311] The terminal receives preference information and prompt messages from the user. A specific prompt message might be, "Suggest a Japanese-style dish that doesn't use nuts." Based on this input, the terminal sends a request to the server.

[0312] Step 3:

[0313] The server utilizes a generative AI model based on the received preference information to generate corresponding recommendations. Data processing involves analyzing user input and extracting information from related databases. As a result, it generates a list of recommended dishes that match the user's requests and returns it to the terminal.

[0314] Step 4:

[0315] The terminal displays recommendations returned from the server to the user. The output list of recommended dishes is appropriately formatted for visual clarity and to provide an easy-to-use interface for the user to select from.

[0316] Step 5:

[0317] The user selects an individual dish from the presented list of recommended dishes. The selected information is then sent back to the server by the terminal.

[0318] Step 6:

[0319] The server generates a detailed cooking guide based on the selected dish. This process can include step-by-step instructions and time management advice. The generated guide information is then sent back to the terminal.

[0320] Step 7:

[0321] The terminal provides the user with the received cooking guide in real time. As the user progresses with the cooking, the terminal displays the next steps sequentially, supporting their progress.

[0322] 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.

[0323] This invention provides a system that combines an artificial intelligence agent that reflects the knowledge and speaking style of a professional information provider with an emotion engine that recognizes the user's emotions and appropriately adjusts the content of the conversation.

[0324] To implement this system, expert information providers must first access the platform and create an account. Users can input their expertise and speaking style characteristics through the interface and upload necessary voice samples, etc. Based on this data, the server builds an artificial intelligence model and forms a customized agent.

[0325] Furthermore, users can configure the emotion engine to determine how the agent recognizes the user's emotions and adjusts its responses accordingly. This emotion engine includes algorithms that analyze emotions from voice tone and text, and optimize responses based on the resulting emotional state.

[0326] Consumers (other users) access the platform using their own devices and search for AI agents that interest them. After purchase, consumers run the agent in the cloud or locally and begin interacting with it. The system uses an emotion engine to analyze the consumer's emotional state in real time and generate the most appropriate response for that state. For example, if the user is feeling stressed, it prioritizes providing calming tones and relaxing information.

[0327] This system allows the server to continuously improve its artificial intelligence model based on consumer feedback and the results of emotion engine analysis. As a result, it can provide consumers with a more personalized and advanced conversational experience.

[0328] The following describes the processing flow.

[0329] Step 1:

[0330] Users (expert information providers) create an account on the platform and log in. Through the interface, they input their expertise and speaking style characteristics, and upload audio samples as needed.

[0331] Step 2:

[0332] The server receives data sent by the user and preprocesses it. Text data is analyzed using natural language processing, and characteristics of the user's speech are extracted from audio samples.

[0333] Step 3:

[0334] The server customizes the artificial intelligence model based on the data obtained. It generates an AI agent that reflects the user's knowledge and speaking style, and also connects an emotion engine to the agent.

[0335] Step 4:

[0336] Users test the generated artificial intelligence agent on a dashboard. Simultaneously, they verify the operation of the emotion engine built into the agent and send feedback to the server as needed.

[0337] Step 5:

[0338] The server receives feedback from the user and adjusts the artificial intelligence model and emotion engine. The adjusted model is then presented to the user again for final confirmation.

[0339] Step 6:

[0340] The device (consumer's device) accesses the platform and searches for an AI agent of interest. After completing the purchase process, the agent becomes available.

[0341] Step 7:

[0342] The device (the consumer's device) activates the purchased artificial intelligence agent and begins a conversation. The emotion engine analyzes the consumer's voice tone and text to determine their emotions, and the agent adjusts its response based on the analysis results.

[0343] Step 8:

[0344] The device (the consumer's device) allows consumers to receive appropriate advice based on their emotional changes through an interactive experience with an agent. Consumer usage data and feedback are sent to a server.

[0345] Step 9:

[0346] The server continuously improves its artificial intelligence model and emotion engine based on feedback and sentiment analysis data collected from consumers. This improves the accuracy and naturalness of the agent's responses.

[0347] (Example 2)

[0348] 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".

[0349] The problem that this invention aims to solve is to build intelligent agents that reflect the characteristics of individuals with specialized knowledge, and to improve the accuracy and effectiveness of the personalized conversational experience that users obtain through those agents. Furthermore, it aims to provide a more sophisticated communication experience by automatically adjusting responses according to the user's emotions and through continuous model improvement.

[0350] 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.

[0351] In this invention, the server includes means for providing an interface for an individual with specialized knowledge to input their knowledge and speech patterns, means for individually adjusting an intelligent processing model based on the individual's input information, and means for setting up an emotion analysis device and generating a response that matches the user's emotional state. This makes it possible to construct a customized conversational agent that combines expertise and emotion recognition.

[0352] A "specialized individual" is someone who possesses advanced knowledge and skills in a specific field and provides information to agents in order to utilize that knowledge.

[0353] An "interface" is a means of inputting and outputting information between a user and a system, and a mechanism for facilitating smooth interaction with the system.

[0354] An "intelligent processing model" is a framework that uses artificial intelligence technology to learn from input data and generate inferences and responses.

[0355] A "space on a communication network" refers to online platforms and services that can be accessed via networks such as the internet, and is a place where information can be shared and obtained.

[0356] An "emotion analysis device" is a device or system that analyzes a user's emotional state from information such as voice and text, and provides appropriate responses or services corresponding to those emotions.

[0357] "Dialogue using language data" refers to the exchange of information using language, and is a form of communication that includes questions and answers using natural language.

[0358] "Opinions" refer to feedback and evaluation information provided by users and stakeholders, and are valuable information that can be used to improve systems and services.

[0359] To implement this invention, the server constructs a customized intelligent agent using a generative AI model based on data provided by individuals with specialized knowledge. Specifically, the user inputs their expertise and speaking style through an interface using a terminal and uploads voice samples as needed. This information is sent to the server, and an intelligent processing model is generated.

[0360] The server also analyzes the user's emotional state in real time using an emotion analysis device. This analysis may involve using software equipped with natural language processing technology. For example, it could include technology that understands the emotional state and provides an appropriate response based on voice and text data provided by the user.

[0361] A concrete example is an intelligent agent designed for psychological counseling. Users can input and configure their expertise in psychology and their calm speaking style to build an agent that suggests appropriate relaxation methods to stressed consumers.

[0362] The following are examples of prompt statements that are used.

[0363] "Like a skilled psychological counselor, provide advice on how to calm users when they are feeling stressed. Suggest specific ways for consumers to relax."

[0364] In this way, it becomes possible to provide personalized conversational experiences tailored to each individual user.

[0365] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0366] Step 1:

[0367] Users input their expertise and speaking style on their device and upload any necessary audio samples.

[0368] Input: User expertise, speaking style, voice sample

[0369] Operation: The user interacts with the interface to provide the necessary input data.

[0370] Output: Expertise and talk style data are sent to the server.

[0371] Step 2:

[0372] The server receives input data and customizes the intelligent processing model using a generative AI model.

[0373] Input: User-submitted expertise, talk style data, and audio samples.

[0374] Operation: The server utilizes a generated AI model to build an intelligent agent that reflects the user's characteristics. Specifically, it performs data analysis and model training.

[0375] Output: Customized intelligent agent model

[0376] Step 3:

[0377] The user configures the emotion analysis device on their terminal.

[0378] Input: Setting parameters related to sentiment analysis (e.g., sentiment detection threshold and response pattern)

[0379] Operation: The user selects emotion engine settings on the interface and sends those parameters to the server.

[0380] Output: The settings are saved on the server and reflected in the intelligent agent's response.

[0381] Step 4:

[0382] Consumers access the platform on their devices, search for agents, and make purchases.

[0383] Input: Consumer search queries and agent selection information

[0384] Operation: Consumers select agents of interest from a list and proceed with the purchase.

[0385] Output: Purchase completion notification and agent usage rights

[0386] Step 5:

[0387] The consumer activates an intelligent agent on their device and begins a dialogue.

[0388] Input: Consumer voice and text-based interactive input

[0389] Operation: Consumers communicate with the agent using a conversational interface. The agent performs sentiment analysis and generates appropriate responses.

[0390] Output: Personalized response from the agent

[0391] Step 6:

[0392] The server collects consumer feedback and interaction data, and continuously improves its intelligent processing model.

[0393] Input: Consumer feedback data, dialogue logs

[0394] Operation: The server analyzes feedback and interaction logs to adjust and improve the model.

[0395] Output: Updated intelligent agent model

[0396] In this way, an individualized conversational experience is provided by an agent that combines specialized knowledge and emotion recognition.

[0397] (Application Example 2)

[0398] 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."

[0399] In an aging society, the number of elderly people requiring care is increasing. Elderly people often experience feelings of loneliness and anxiety in their daily lives, which can impair their mental health. Traditional care systems are limited to simple information provision and bureaucratic interactions, lacking emotional support that considers the feelings of the elderly. Therefore, there is a need for techniques that enable flexible communication tailored to their emotional state.

[0400] 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.

[0401] In this invention, the server includes means for providing an interface for expert information providers to input their knowledge and speaking style, means for customizing an artificial intelligence model based on the input data from the expert information providers, and means for analyzing the user's emotional state via an emotion analysis engine and optimizing the response. This enables the generation of responses tailored to the emotional state of elderly individuals, providing personalized emotional support to the user.

[0402] A "specialized information provider" is an individual or organization that possesses extensive knowledge and experience in a specific field and has the ability to provide that knowledge and communication skills.

[0403] An "interface" is a means or device used by a user to input information or interact with a system.

[0404] An "artificial intelligence model" is a computational tool that makes judgments and predictions based on input data according to a specific purpose, and has the ability to automate specific tasks.

[0405] An "online platform" is a virtual environment on the internet for users to search for, select, and obtain specific services or products.

[0406] An "emotion analysis engine" is a technology or method for analyzing and understanding a user's emotional state from voice or text.

[0407] "Optimizing responses" is the process of selecting and providing the most appropriate and effective response or action based on the user's emotions and situation.

[0408] "Support for the elderly" refers to assistance and services provided to alleviate the mental and physical burdens on elderly people in their daily lives.

[0409] To realize this invention, expert information providers are required to access a server and input their knowledge and speaking style through an interface. In particular, expert information providers upload voice samples and text data so that the AI ​​model can be customized to reflect their characteristics. On the server, a generative AI model is built based on this data, and a customized artificial intelligence agent is formed. At this time, the construction of the AI ​​model uses a natural language processing library in Python, such as spaCy, and Google's Speech-to-Text API for speech processing.

[0410] Users who purchase the product can access the online platform using their own devices to search for and acquire artificial intelligence agents that interest them. The emotion analysis engine installed on the device analyzes the user's tone of voice and speech content in real time to determine the user's emotional state. Based on this analysis, the customized AI agent generates optimized responses to support the elderly. The responses are generated considering the user's psychological state, aiming to provide the user with a sense of security and satisfaction.

[0411] As a concrete example, if an elderly person feels lonely during a daily conversation, the AI ​​agent will engage in dialogue based on prompts such as, "Good morning, how are you feeling today? Is there anything that's bothering you about the recent weather?" to provide encouragement and comfort. Through such dialogue, personalized emotional support for the elderly is realized.

[0412] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0413] Step 1:

[0414] The server receives data on knowledge and speaking style from expert information providers. This data includes audio samples and text information. Using this data as input, the generative AI model is customized to reflect the characteristics of the expert information providers.

[0415] Step 2:

[0416] The server builds a generative AI model and creates a customized artificial intelligence agent. This process uses a natural language processing library (e.g., spaCy) to parse text data, and speech data is transcribed via Google's Speech-to-Text API. This results in an AI agent that reflects the speaking style of the expert informant.

[0417] Step 3:

[0418] The user accesses an online platform using their device and searches for an artificial intelligence agent that interests them. Once the user selects an agent, the server sends that agent to the user's device.

[0419] Step 4:

[0420] The sentiment analysis engine running on the device analyzes the user's voice and text input in real time. In this step, it analyzes the voice tone and selected words as input to determine the user's emotional state. The results of the sentiment analysis are output and transmitted to the AI ​​agent.

[0421] Step 5:

[0422] The AI ​​agent generates the optimal response based on the results of emotion analysis. The generated response is adjusted to support the psychological health of the elderly and output to the user as voice or text on the device. An example of a prompt used at this stage would be, "Good morning, how are you feeling today? Is there anything that the recent weather has been bothering you?"

[0423] 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.

[0424] 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.

[0425] 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.

[0426] [Third Embodiment]

[0427] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0428] 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.

[0429] 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).

[0430] 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.

[0431] 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.

[0432] 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).

[0433] 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.

[0434] 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.

[0435] 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.

[0436] 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.

[0437] 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.

[0438] 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".

[0439] To implement this invention, a professional information provider must first access the platform and create an account. Through the platform's interface, the user can input their expertise and speaking style characteristics. Based on this, the server customizes a dedicated artificial intelligence model based on the received data.

[0440] The server integrates the provided knowledge data and speech style into an artificial intelligence agent using natural language processing techniques and machine learning algorithms. This creates an agent that possesses user-specific knowledge and engages in conversations with a unique speaking style. This process includes steps such as analyzing voice samples and clustering text data.

[0441] On the other hand, general consumers (users) can access the online platform using their own devices. Consumers can search for areas of interest and expert information providers, and purchase or subscribe to customized artificial intelligence agents. After completing the purchase process, they can use the agent through a dedicated application on their device or access it via a cloud-based system.

[0442] As a concrete example, consider a scenario where a culinary expert inputs their culinary theories and teaching methods to generate an agent. The server analyzes the provided recipe and cooking technique data and builds a cooking-specific artificial intelligence agent based on that analysis. Consumers can use this agent to receive real-time answers to cooking-related questions and advice, thereby easily applying expert knowledge at home.

[0443] Furthermore, the server can collect feedback from consumers and use that feedback to continuously improve the AI ​​model. This will make the information provided by the agent even more accurate, enabling more natural and engaging conversations.

[0444] The following describes the processing flow.

[0445] Step 1:

[0446] Users (expert information providers) register an account on the platform and access a dedicated dashboard. On the dashboard, they input information about their expertise and speaking style, and upload audio samples and text data to the system as needed.

[0447] Step 2:

[0448] The server receives data provided by the user and prepares it for incorporation into the artificial intelligence model. Specifically, it divides text data, performs preprocessing for natural language processing, and extracts features of the speaking style through analysis of voice samples.

[0449] Step 3:

[0450] The server applies machine learning algorithms to pre-processed data to generate an artificial intelligence agent that reflects the user's knowledge and unique speaking style. This also includes a process of simulating conversations.

[0451] Step 4:

[0452] Users can test the generated artificial intelligence agent to verify that it performs as expected. Testing is conducted on a dashboard, and users can provide feedback and request further tuning as needed.

[0453] Step 5:

[0454] The server improves and adjusts the artificial intelligence model based on user feedback. The adjusted model is then presented to the user again for final confirmation before being finalized.

[0455] Step 6:

[0456] If a user decides to make their agent public, that agent will be listed on the platform and made accessible to consumers. Pricing and distribution terms are also set up at this stage.

[0457] Step 7:

[0458] The consumer's device searches for agents of interest through an online platform and completes the purchase process. After purchase, the consumer can download agents and use cloud-based services.

[0459] Step 8:

[0460] The device (the consumer's device) can interact with an agent and receive expert-based advice and information. Users can also input feedback during use, and this information is collected on the server.

[0461] Step 9:

[0462] The server continuously improves the model based on consumer feedback and performs data analysis to enhance the quality of the AI ​​agent. When changes are made, the user is notified again, and the agent is updated accordingly.

[0463] (Example 1)

[0464] 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."

[0465] There is a need for a system that allows individuals with specialized knowledge to efficiently deploy their knowledge and communication style on digital platforms and provide it to a diverse range of users. Furthermore, it is crucial that the generated artificial intelligence functions reflect user feedback and are always provided in the most up-to-date and optimal form.

[0466] 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.

[0467] In this invention, the server includes means for providing an information processing device for expert information providers to input their expertise and communication style, means for adjusting an artificial intelligence processing device based on the input information from the expert information provider, and means for providing an electronic information infrastructure for users to explore and obtain the adjusted artificial intelligence functions. This ensures that artificial intelligence functions based on the expert information provider's knowledge are provided to users in the most up-to-date and optimal form, and that continuous improvement is possible through user feedback.

[0468] A "specialized information provider" refers to an individual or organization that possesses specific knowledge or skills and provides that information in a way that is useful to others.

[0469] An "information processing device" refers to an electronic or software-based means that receives data, analyzes it, and outputs it in a specific format.

[0470] "Communication style" refers to the overall style of communication, including the language, expressions, and characteristics of word choice used when conveying information.

[0471] An "artificial intelligence processing device" refers to a device or software that uses technologies such as machine learning and natural language processing to analyze data and enable decision-making and dialogue.

[0472] "Electronic information infrastructure" refers to a foundational system for organizing, storing, and providing data and information to users through the internet and other electronic means.

[0473] "User" refers to an individual or organization that utilizes the information and functions provided through this system.

[0474] "Feedback" refers to information regarding the evaluation of a system or its functions, such as opinions, impressions, and suggestions for improvement provided by users.

[0475] To implement this invention, a dedicated software platform is required. An embodiment thereof is shown below.

[0476] First, users, who are professional information providers, access the online platform using a personal computer or tablet as an information processing device. There, an interface is provided for inputting their expertise and communication style. This allows users to electronically register their professional information. For example, a culinary expert can describe their recipes and cooking methods.

[0477] Next, the server receives the provided input data, analyzes it using an artificial intelligence processing unit, and builds a customized generative AI model. This process utilizes software libraries such as "TensorFlow" and "SpaCy" to automatically analyze the data and optimize the model. The server ensures that the model is appropriately adjusted, taking into account the user's communication style.

[0478] Subsequently, users with devices can access the online platform through the electronic information infrastructure, search for generated artificial intelligence functions, and use them. Users log in to the platform using their smartphones or PCs and interact with agents who are expert information providers of their interest. For example, with a cooking-focused agent, users can enter prompts such as "Tell me how to cook pasta" or "Give me some easy dinner ideas" to receive direct advice.

[0479] Furthermore, the server continuously collects feedback from users and improves the artificial intelligence model. This feedback improves the quality of the information provided over time, making it more valuable to users. Feedback can be easily submitted through the platform, quickly analyzed by the server, and the model is updated as needed.

[0480] In this way, a system is realized in which expert information providers and users collaborate with each other, enabling the efficient transfer and dissemination of knowledge.

[0481] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0482] Step 1:

[0483] Professional information providers, as users, access the online platform using a PC or tablet and create an account. They input their expertise and communication style through the interface. This input data serves to electronically record the characteristics of their expertise and style and is transmitted to the server.

[0484] Step 2:

[0485] The server analyzes the input data received from the user. This analysis utilizes natural language processing libraries such as TensorFlow and SpaCy. Specifically, the server uses these libraries to analyze text data, extracting technical terms and patterning speech styles. This process allows the generative AI model to be refined based on the features obtained from the input data.

[0486] Step 3:

[0487] The server builds an artificial intelligence agent based on a finely tuned generative AI model and registers its functions in the electronic information infrastructure. The agent includes a program to replicate the user's communication style and is ready for user access.

[0488] Step 4:

[0489] Users with a device access the online platform using their smartphone or computer. By searching for and selecting an AI agent of an expert information provider of their interest, they prepare to interact with the agent through prompts. This allows users to input specific questions, such as "How do I cook pasta?", and receive direct advice.

[0490] Step 5:

[0491] The server receives questions and feedback from users and records the agent's responses. This data is used to evaluate the performance of the AI ​​model, reanalyze the data as needed, and adjust model parameters to improve system performance. Continuously incorporating feedback enables more accurate and user-friendly responses.

[0492] (Application Example 1)

[0493] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0494] Modern consumers demand highly responsive information tailored to their diverse tastes and needs. However, general-purpose information sources cannot fully address individual needs, creating a demand for information specialized in specific areas. Furthermore, there is a need for real-time responsiveness and continuous improvement based on accurate feedback, and there is a lack of means to enhance the consumer experience in this way.

[0495] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0496] In this invention, the server includes means for providing an interface for experts to offer their knowledge and communication skills, means for generating application-relevant recommendations based on user preference information, and means for responding to user behavior in real time and guiding the process. This enables the provision of specialized information tailored to individual consumers, improving the consumer experience and providing highly satisfying services.

[0497] An "expert" is a person who possesses advanced knowledge and skills in a specific field and is able to provide that knowledge to others.

[0498] An "interface" is a point of contact or means for a user to interact with a system or device.

[0499] A "learning model" is an algorithm that has been trained to identify patterns based on data and to make predictions and judgments in response to new inputs.

[0500] An "online infrastructure" refers to services and platforms provided via the internet, providing an environment where users can access or exchange digital information.

[0501] A "user" is an individual or group that uses the system or service.

[0502] "Preference information" refers to data that indicates a user's preferences and interests, and is an element used to provide personalized services.

[0503] "Recommendations" refer to options or actions suggested based on the user's needs and preferences.

[0504] "Real-time response" refers to a process that allows for quick responses on the spot, enabling smooth communication with users.

[0505] The system for implementing this invention mainly consists of server, terminal, and user interaction.

[0506] The server receives knowledge and discourse provided by experts through the necessary interfaces and can adjust its learning model accordingly. This model is generated using natural language processing techniques and machine learning algorithms, and after adjustment, it is provided to users via an online platform. Specific software used includes OpenAI's GPT and TensorFlow. This enables the provision of precise information.

[0507] The terminal connects to an online infrastructure, providing users with access to a tailored learning agent. Users can use the terminal to input their preferences and receive recommendations based on that information. This data processing involves real-time analysis of the user's input data and calculations to provide personalized recommendations.

[0508] As a concrete example, if a user requests a recipe, they would enter a prompt message into their terminal such as, "Suggest a Japanese-style dish that doesn't use nuts." Based on this, the server would use a individually tailored agent model to recommend relevant recipes and guide the user through each step. For example, by using a prompt message like, "I want to eat Japanese food today, but I have an allergy, so please make it without nuts," the user can receive appropriate menu suggestions tailored to their preferences.

[0509] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0510] Step 1:

[0511] The server receives knowledge and speech data from experts through an interface. Based on this input data, it adjusts the learning model using natural language processing techniques and machine learning algorithms. By analyzing the data provided by experts and appropriately incorporating it into the model, it generates an AI agent that reflects individual knowledge.

[0512] Step 2:

[0513] The terminal receives preference information and prompt messages from the user. A specific prompt message might be, "Suggest a Japanese-style dish that doesn't use nuts." Based on this input, the terminal sends a request to the server.

[0514] Step 3:

[0515] The server utilizes a generative AI model based on the received preference information to generate corresponding recommendations. Data processing involves analyzing user input and extracting information from related databases. As a result, it generates a list of recommended dishes that match the user's requests and returns it to the terminal.

[0516] Step 4:

[0517] The terminal displays recommendations returned from the server to the user. The output list of recommended dishes is appropriately formatted for visual clarity and to provide an easy-to-use interface for the user to select from.

[0518] Step 5:

[0519] The user selects an individual dish from the presented list of recommended dishes. The selected information is then sent back to the server by the terminal.

[0520] Step 6:

[0521] The server generates a detailed cooking guide based on the selected dish. This process can include step-by-step instructions and time management advice. The generated guide information is then sent back to the terminal.

[0522] Step 7:

[0523] The terminal provides the user with the received cooking guide in real time. As the user progresses with the cooking, the terminal displays the next steps sequentially, supporting their progress.

[0524] 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.

[0525] This invention provides a system that combines an artificial intelligence agent that reflects the knowledge and speaking style of a professional information provider with an emotion engine that recognizes the user's emotions and appropriately adjusts the content of the conversation.

[0526] To implement this system, expert information providers must first access the platform and create an account. Users can input their expertise and speaking style characteristics through the interface and upload necessary voice samples, etc. Based on this data, the server builds an artificial intelligence model and forms a customized agent.

[0527] Furthermore, users can configure the emotion engine to determine how the agent recognizes the user's emotions and adjusts its responses accordingly. This emotion engine includes algorithms that analyze emotions from voice tone and text, and optimize responses based on the resulting emotional state.

[0528] Consumers (other users) access the platform using their own devices and search for AI agents that interest them. After purchase, consumers run the agent in the cloud or locally and begin interacting with it. The system uses an emotion engine to analyze the consumer's emotional state in real time and generate the most appropriate response for that state. For example, if the user is feeling stressed, it prioritizes providing calming tones and relaxing information.

[0529] This system allows the server to continuously improve its artificial intelligence model based on consumer feedback and the results of emotion engine analysis. As a result, it can provide consumers with a more personalized and advanced conversational experience.

[0530] The following describes the processing flow.

[0531] Step 1:

[0532] Users (expert information providers) create an account on the platform and log in. Through the interface, they input their expertise and speaking style characteristics, and upload audio samples as needed.

[0533] Step 2:

[0534] The server receives data sent by the user and preprocesses it. Text data is analyzed using natural language processing, and characteristics of the user's speech are extracted from audio samples.

[0535] Step 3:

[0536] The server customizes the artificial intelligence model based on the data obtained. It generates an AI agent that reflects the user's knowledge and speaking style, and also connects an emotion engine to the agent.

[0537] Step 4:

[0538] Users test the generated artificial intelligence agent on a dashboard. Simultaneously, they verify the operation of the emotion engine built into the agent and send feedback to the server as needed.

[0539] Step 5:

[0540] The server receives feedback from the user and adjusts the artificial intelligence model and emotion engine. The adjusted model is then presented to the user again for final confirmation.

[0541] Step 6:

[0542] The device (consumer's device) accesses the platform and searches for an AI agent of interest. After completing the purchase process, the agent becomes available.

[0543] Step 7:

[0544] The device (the consumer's device) activates the purchased artificial intelligence agent and begins a conversation. The emotion engine analyzes the consumer's voice tone and text to determine their emotions, and the agent adjusts its response based on the analysis results.

[0545] Step 8:

[0546] The device (the consumer's device) allows consumers to receive appropriate advice based on their emotional changes through an interactive experience with an agent. Consumer usage data and feedback are sent to a server.

[0547] Step 9:

[0548] The server continuously improves its artificial intelligence model and emotion engine based on feedback and sentiment analysis data collected from consumers. This improves the accuracy and naturalness of the agent's responses.

[0549] (Example 2)

[0550] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0551] The problem that this invention aims to solve is to build intelligent agents that reflect the characteristics of individuals with specialized knowledge, and to improve the accuracy and effectiveness of the personalized conversational experience that users obtain through those agents. Furthermore, it aims to provide a more sophisticated communication experience by automatically adjusting responses according to the user's emotions and through continuous model improvement.

[0552] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0553] In this invention, the server includes means for providing an interface for an individual with specialized knowledge to input their knowledge and speech patterns, means for individually adjusting an intelligent processing model based on the individual's input information, and means for setting up an emotion analysis device and generating a response that matches the user's emotional state. This makes it possible to construct a customized conversational agent that combines expertise and emotion recognition.

[0554] A "specialized individual" is someone who possesses advanced knowledge and skills in a specific field and provides information to agents in order to utilize that knowledge.

[0555] An "interface" is a means of inputting and outputting information between a user and a system, and a mechanism for facilitating smooth interaction with the system.

[0556] An "intelligent processing model" is a framework that uses artificial intelligence technology to learn from input data and generate inferences and responses.

[0557] A "space on a communication network" refers to online platforms and services that can be accessed via networks such as the internet, and is a place where information can be shared and obtained.

[0558] An "emotion analysis device" is a device or system that analyzes a user's emotional state from information such as voice and text, and provides appropriate responses or services corresponding to those emotions.

[0559] "Dialogue using language data" refers to the exchange of information using language, and is a form of communication that includes questions and answers using natural language.

[0560] "Opinions" refer to feedback and evaluation information provided by users and stakeholders, and are valuable information that can be used to improve systems and services.

[0561] To implement this invention, the server constructs a customized intelligent agent using a generative AI model based on data provided by individuals with specialized knowledge. Specifically, the user inputs their expertise and speaking style through an interface using a terminal and uploads voice samples as needed. This information is sent to the server, and an intelligent processing model is generated.

[0562] The server also analyzes the user's emotional state in real time using an emotion analysis device. This analysis may involve using software equipped with natural language processing technology. For example, it could include technology that understands the emotional state and provides an appropriate response based on voice and text data provided by the user.

[0563] A concrete example is an intelligent agent designed for psychological counseling. Users can input and configure their expertise in psychology and their calm speaking style to build an agent that suggests appropriate relaxation methods to stressed consumers.

[0564] The following are examples of prompt statements that are used.

[0565] "Like a skilled psychological counselor, provide advice on how to calm users when they are feeling stressed. Suggest specific ways for consumers to relax."

[0566] In this way, it becomes possible to provide personalized conversational experiences tailored to each individual user.

[0567] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0568] Step 1:

[0569] Users input their expertise and speaking style on their device and upload any necessary audio samples.

[0570] Input: User expertise, speaking style, voice sample

[0571] Operation: The user interacts with the interface to provide the necessary input data.

[0572] Output: Expertise and talk style data are sent to the server.

[0573] Step 2:

[0574] The server receives input data and customizes the intelligent processing model using a generative AI model.

[0575] Input: User-submitted expertise, talk style data, and audio samples.

[0576] Operation: The server utilizes a generated AI model to build an intelligent agent that reflects the user's characteristics. Specifically, it performs data analysis and model training.

[0577] Output: Customized intelligent agent model

[0578] Step 3:

[0579] The user configures the emotion analysis device on their terminal.

[0580] Input: Setting parameters related to sentiment analysis (e.g., sentiment detection threshold and response pattern)

[0581] Operation: The user selects emotion engine settings on the interface and sends those parameters to the server.

[0582] Output: The settings are saved on the server and reflected in the intelligent agent's response.

[0583] Step 4:

[0584] Consumers access the platform on their devices, search for agents, and make purchases.

[0585] Input: Consumer search queries and agent selection information

[0586] Operation: Consumers select agents of interest from a list and proceed with the purchase.

[0587] Output: Purchase completion notification and agent usage rights

[0588] Step 5:

[0589] The consumer activates an intelligent agent on their device and begins a dialogue.

[0590] Input: Consumer voice and text-based interactive input

[0591] Operation: Consumers communicate with the agent using a conversational interface. The agent performs sentiment analysis and generates appropriate responses.

[0592] Output: Personalized response from the agent

[0593] Step 6:

[0594] The server collects consumer feedback and interaction data, and continuously improves its intelligent processing model.

[0595] Input: Consumer feedback data, dialogue logs

[0596] Operation: The server analyzes feedback and interaction logs to adjust and improve the model.

[0597] Output: Updated intelligent agent model

[0598] In this way, an individualized conversational experience is provided by an agent that combines specialized knowledge and emotion recognition.

[0599] (Application Example 2)

[0600] 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."

[0601] In an aging society, the number of elderly people requiring care is increasing. Elderly people often experience feelings of loneliness and anxiety in their daily lives, which can impair their mental health. Traditional care systems are limited to simple information provision and bureaucratic interactions, lacking emotional support that considers the feelings of the elderly. Therefore, there is a need for techniques that enable flexible communication tailored to their emotional state.

[0602] 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.

[0603] In this invention, the server includes means for providing an interface for expert information providers to input their knowledge and speaking style, means for customizing an artificial intelligence model based on the input data from the expert information providers, and means for analyzing the user's emotional state via an emotion analysis engine and optimizing the response. This enables the generation of responses tailored to the emotional state of elderly individuals, providing personalized emotional support to the user.

[0604] A "specialized information provider" is an individual or organization that possesses extensive knowledge and experience in a specific field and has the ability to provide that knowledge and communication skills.

[0605] An "interface" is a means or device used by a user to input information or interact with a system.

[0606] An "artificial intelligence model" is a computational tool that makes judgments and predictions based on input data according to a specific purpose, and has the ability to automate specific tasks.

[0607] An "online platform" is a virtual environment on the internet for users to search for, select, and obtain specific services or products.

[0608] An "emotion analysis engine" is a technology or method for analyzing and understanding a user's emotional state from voice or text.

[0609] "Optimizing responses" is the process of selecting and providing the most appropriate and effective response or action based on the user's emotions and situation.

[0610] "Support for the elderly" refers to assistance and services provided to alleviate the mental and physical burdens on elderly people in their daily lives.

[0611] To realize this invention, expert information providers are required to access a server and input their knowledge and speaking style through an interface. In particular, expert information providers upload voice samples and text data so that the AI ​​model can be customized to reflect their characteristics. On the server, a generative AI model is built based on this data, and a customized artificial intelligence agent is formed. At this time, the construction of the AI ​​model uses a natural language processing library in Python, such as spaCy, and Google's Speech-to-Text API for speech processing.

[0612] Users who purchase the product can access the online platform using their own devices to search for and acquire artificial intelligence agents that interest them. The emotion analysis engine installed on the device analyzes the user's tone of voice and speech content in real time to determine the user's emotional state. Based on this analysis, the customized AI agent generates optimized responses to support the elderly. The responses are generated considering the user's psychological state, aiming to provide the user with a sense of security and satisfaction.

[0613] As a concrete example, if an elderly person feels lonely during a daily conversation, the AI ​​agent will engage in dialogue based on prompts such as, "Good morning, how are you feeling today? Is there anything that's bothering you about the recent weather?" to provide encouragement and comfort. Through such dialogue, personalized emotional support for the elderly is realized.

[0614] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0615] Step 1:

[0616] The server receives data on knowledge and speaking style from expert information providers. This data includes audio samples and text information. Using this data as input, the generative AI model is customized to reflect the characteristics of the expert information providers.

[0617] Step 2:

[0618] The server builds a generative AI model and creates a customized artificial intelligence agent. This process uses a natural language processing library (e.g., spaCy) to parse text data, and speech data is transcribed via Google's Speech-to-Text API. This results in an AI agent that reflects the speaking style of the expert informant.

[0619] Step 3:

[0620] The user accesses an online platform using their device and searches for an artificial intelligence agent that interests them. Once the user selects an agent, the server sends that agent to the user's device.

[0621] Step 4:

[0622] The sentiment analysis engine running on the device analyzes the user's voice and text input in real time. In this step, it analyzes the voice tone and selected words as input to determine the user's emotional state. The results of the sentiment analysis are output and transmitted to the AI ​​agent.

[0623] Step 5:

[0624] The AI ​​agent generates the optimal response based on the results of emotion analysis. The generated response is adjusted to support the psychological health of the elderly and output to the user as voice or text on the device. An example of a prompt used at this stage would be, "Good morning, how are you feeling today? Is there anything that the recent weather has been bothering you?"

[0625] 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.

[0626] 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.

[0627] 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.

[0628] [Fourth Embodiment]

[0629] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0630] 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.

[0631] 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).

[0632] 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.

[0633] 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.

[0634] 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).

[0635] 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.

[0636] 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.

[0637] 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.

[0638] 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.

[0639] 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.

[0640] 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.

[0641] 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".

[0642] To implement this invention, a professional information provider must first access the platform and create an account. Through the platform's interface, the user can input their expertise and speaking style characteristics. Based on this, the server customizes a dedicated artificial intelligence model based on the received data.

[0643] The server integrates the provided knowledge data and speech style into an artificial intelligence agent using natural language processing techniques and machine learning algorithms. This creates an agent that possesses user-specific knowledge and engages in conversations with a unique speaking style. This process includes steps such as analyzing voice samples and clustering text data.

[0644] On the other hand, general consumers (users) can access the online platform using their own devices. Consumers can search for areas of interest and expert information providers, and purchase or subscribe to customized artificial intelligence agents. After completing the purchase process, they can use the agent through a dedicated application on their device or access it via a cloud-based system.

[0645] As a concrete example, consider a scenario where a culinary expert inputs their culinary theories and teaching methods to generate an agent. The server analyzes the provided recipe and cooking technique data and builds a cooking-specific artificial intelligence agent based on that analysis. Consumers can use this agent to receive real-time answers to cooking-related questions and advice, thereby easily applying expert knowledge at home.

[0646] Furthermore, the server can collect feedback from consumers and use that feedback to continuously improve the AI ​​model. This will make the information provided by the agent even more accurate, enabling more natural and engaging conversations.

[0647] The following describes the processing flow.

[0648] Step 1:

[0649] Users (expert information providers) register an account on the platform and access a dedicated dashboard. On the dashboard, they input information about their expertise and speaking style, and upload audio samples and text data to the system as needed.

[0650] Step 2:

[0651] The server receives data provided by the user and prepares it for incorporation into the artificial intelligence model. Specifically, it divides text data, performs preprocessing for natural language processing, and extracts features of the speaking style through analysis of voice samples.

[0652] Step 3:

[0653] The server applies machine learning algorithms to pre-processed data to generate an artificial intelligence agent that reflects the user's knowledge and unique speaking style. This also includes a process of simulating conversations.

[0654] Step 4:

[0655] Users can test the generated artificial intelligence agent to verify that it performs as expected. Testing is conducted on a dashboard, and users can provide feedback and request further tuning as needed.

[0656] Step 5:

[0657] The server improves and adjusts the artificial intelligence model based on user feedback. The adjusted model is then presented to the user again for final confirmation before being finalized.

[0658] Step 6:

[0659] If a user decides to make their agent public, that agent will be listed on the platform and made accessible to consumers. Pricing and distribution terms are also set up at this stage.

[0660] Step 7:

[0661] The consumer's device searches for agents of interest through an online platform and completes the purchase process. After purchase, the consumer can download agents and use cloud-based services.

[0662] Step 8:

[0663] The device (the consumer's device) can interact with an agent and receive expert-based advice and information. Users can also input feedback during use, and this information is collected on the server.

[0664] Step 9:

[0665] The server continuously improves the model based on consumer feedback and performs data analysis to enhance the quality of the AI ​​agent. When changes are made, the user is notified again, and the agent is updated accordingly.

[0666] (Example 1)

[0667] 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".

[0668] There is a need for a system that allows individuals with specialized knowledge to efficiently deploy their knowledge and communication style on digital platforms and provide it to a diverse range of users. Furthermore, it is crucial that the generated artificial intelligence functions reflect user feedback and are always provided in the most up-to-date and optimal form.

[0669] 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.

[0670] In this invention, the server includes means for providing an information processing device for expert information providers to input their expertise and communication style, means for adjusting an artificial intelligence processing device based on the input information from the expert information provider, and means for providing an electronic information infrastructure for users to explore and obtain the adjusted artificial intelligence functions. This ensures that artificial intelligence functions based on the expert information provider's knowledge are provided to users in the most up-to-date and optimal form, and that continuous improvement is possible through user feedback.

[0671] A "specialized information provider" refers to an individual or organization that possesses specific knowledge or skills and provides that information in a way that is useful to others.

[0672] An "information processing device" refers to an electronic or software-based means that receives data, analyzes it, and outputs it in a specific format.

[0673] "Communication style" refers to the overall style of communication, including the language, expressions, and characteristics of word choice used when conveying information.

[0674] An "artificial intelligence processing device" refers to a device or software that uses technologies such as machine learning and natural language processing to analyze data and enable decision-making and dialogue.

[0675] "Electronic information infrastructure" refers to a foundational system for organizing, storing, and providing data and information to users through the internet and other electronic means.

[0676] "User" refers to an individual or organization that utilizes the information and functions provided through this system.

[0677] "Feedback" refers to information regarding the evaluation of a system or its functions, such as opinions, impressions, and suggestions for improvement provided by users.

[0678] To implement this invention, a dedicated software platform is required. An embodiment thereof is shown below.

[0679] First, users, who are professional information providers, access the online platform using a personal computer or tablet as an information processing device. There, an interface is provided for inputting their expertise and communication style. This allows users to electronically register their professional information. For example, a culinary expert can describe their recipes and cooking methods.

[0680] Next, the server receives the provided input data, analyzes it using an artificial intelligence processing unit, and builds a customized generative AI model. This process utilizes software libraries such as "TensorFlow" and "SpaCy" to automatically analyze the data and optimize the model. The server ensures that the model is appropriately adjusted, taking into account the user's communication style.

[0681] Subsequently, users with devices can access the online platform through the electronic information infrastructure, search for generated artificial intelligence functions, and use them. Users log in to the platform using their smartphones or PCs and interact with agents who are expert information providers of their interest. For example, with a cooking-focused agent, users can enter prompts such as "Tell me how to cook pasta" or "Give me some easy dinner ideas" to receive direct advice.

[0682] Furthermore, the server continuously collects feedback from users and improves the artificial intelligence model. This feedback improves the quality of the information provided over time, making it more valuable to users. Feedback can be easily submitted through the platform, quickly analyzed by the server, and the model is updated as needed.

[0683] In this way, a system is realized in which expert information providers and users collaborate with each other, enabling the efficient transfer and dissemination of knowledge.

[0684] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0685] Step 1:

[0686] Professional information providers, as users, access the online platform using a PC or tablet and create an account. They input their expertise and communication style through the interface. This input data serves to electronically record the characteristics of their expertise and style and is transmitted to the server.

[0687] Step 2:

[0688] The server analyzes the input data received from the user. This analysis utilizes natural language processing libraries such as TensorFlow and SpaCy. Specifically, the server uses these libraries to analyze text data, extracting technical terms and patterning speech styles. This process allows the generative AI model to be refined based on the features obtained from the input data.

[0689] Step 3:

[0690] The server builds an artificial intelligence agent based on a finely tuned generative AI model and registers its functions in the electronic information infrastructure. The agent includes a program to replicate the user's communication style and is ready for user access.

[0691] Step 4:

[0692] Users with a device access the online platform using their smartphone or computer. By searching for and selecting an AI agent of an expert information provider of their interest, they prepare to interact with the agent through prompts. This allows users to input specific questions, such as "How do I cook pasta?", and receive direct advice.

[0693] Step 5:

[0694] The server receives questions and feedback from users and records the agent's responses. This data is used to evaluate the performance of the AI ​​model, reanalyze the data as needed, and adjust model parameters to improve system performance. Continuously incorporating feedback enables more accurate and user-friendly responses.

[0695] (Application Example 1)

[0696] 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".

[0697] Modern consumers demand highly responsive information tailored to their diverse tastes and needs. However, general-purpose information sources cannot fully address individual needs, creating a demand for information specialized in specific areas. Furthermore, there is a need for real-time responsiveness and continuous improvement based on accurate feedback, and there is a lack of means to enhance the consumer experience in this way.

[0698] 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.

[0699] In this invention, the server includes means for providing an interface for experts to offer their knowledge and communication skills, means for generating application-relevant recommendations based on user preference information, and means for responding to user behavior in real time and guiding the process. This enables the provision of specialized information tailored to individual consumers, improving the consumer experience and providing highly satisfying services.

[0700] An "expert" is a person who possesses advanced knowledge and skills in a specific field and is able to provide that knowledge to others.

[0701] An "interface" is a point of contact or means for a user to interact with a system or device.

[0702] A "learning model" is an algorithm that has been trained to identify patterns based on data and to make predictions and judgments in response to new inputs.

[0703] An "online infrastructure" refers to services and platforms provided via the internet, providing an environment where users can access or exchange digital information.

[0704] A "user" is an individual or group that uses the system or service.

[0705] "Preference information" refers to data that indicates a user's preferences and interests, and is an element used to provide personalized services.

[0706] "Recommendations" refer to options or actions suggested based on the user's needs and preferences.

[0707] "Real-time response" refers to a process that allows for quick responses on the spot, enabling smooth communication with users.

[0708] The system for implementing this invention mainly consists of server, terminal, and user interaction.

[0709] The server receives knowledge and discourse provided by experts through the necessary interfaces and can adjust its learning model accordingly. This model is generated using natural language processing techniques and machine learning algorithms, and after adjustment, it is provided to users via an online platform. Specific software used includes OpenAI's GPT and TensorFlow. This enables the provision of precise information.

[0710] The terminal connects to an online infrastructure, providing users with access to a tailored learning agent. Users can use the terminal to input their preferences and receive recommendations based on that information. This data processing involves real-time analysis of the user's input data and calculations to provide personalized recommendations.

[0711] As a concrete example, if a user requests a recipe, they would enter a prompt message into their terminal such as, "Suggest a Japanese-style dish that doesn't use nuts." Based on this, the server would use a individually tailored agent model to recommend relevant recipes and guide the user through each step. For example, by using a prompt message like, "I want to eat Japanese food today, but I have an allergy, so please make it without nuts," the user can receive appropriate menu suggestions tailored to their preferences.

[0712] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0713] Step 1:

[0714] The server receives knowledge and speech data from experts through an interface. Based on this input data, it adjusts the learning model using natural language processing techniques and machine learning algorithms. By analyzing the data provided by experts and appropriately incorporating it into the model, it generates an AI agent that reflects individual knowledge.

[0715] Step 2:

[0716] The terminal receives preference information and prompt messages from the user. A specific prompt message might be, "Suggest a Japanese-style dish that doesn't use nuts." Based on this input, the terminal sends a request to the server.

[0717] Step 3:

[0718] The server utilizes a generative AI model based on the received preference information to generate corresponding recommendations. Data processing involves analyzing user input and extracting information from related databases. As a result, it generates a list of recommended dishes that match the user's requests and returns it to the terminal.

[0719] Step 4:

[0720] The terminal displays recommendations returned from the server to the user. The output list of recommended dishes is appropriately formatted for visual clarity and to provide an easy-to-use interface for the user to select from.

[0721] Step 5:

[0722] The user selects an individual dish from the presented list of recommended dishes. The selected information is then sent back to the server by the terminal.

[0723] Step 6:

[0724] The server generates a detailed cooking guide based on the selected dish. This process can include step-by-step instructions and time management advice. The generated guide information is then sent back to the terminal.

[0725] Step 7:

[0726] The terminal provides the user with the received cooking guide in real time. As the user progresses with the cooking, the terminal displays the next steps sequentially, supporting their progress.

[0727] 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.

[0728] This invention provides a system that combines an artificial intelligence agent that reflects the knowledge and speaking style of a professional information provider with an emotion engine that recognizes the user's emotions and appropriately adjusts the content of the conversation.

[0729] To implement this system, expert information providers must first access the platform and create an account. Users can input their expertise and speaking style characteristics through the interface and upload necessary voice samples, etc. Based on this data, the server builds an artificial intelligence model and forms a customized agent.

[0730] Furthermore, users can configure the emotion engine to determine how the agent recognizes the user's emotions and adjusts its responses accordingly. This emotion engine includes algorithms that analyze emotions from voice tone and text, and optimize responses based on the resulting emotional state.

[0731] Consumers (other users) access the platform using their own devices and search for AI agents that interest them. After purchase, consumers run the agent in the cloud or locally and begin interacting with it. The system uses an emotion engine to analyze the consumer's emotional state in real time and generate the most appropriate response for that state. For example, if the user is feeling stressed, it prioritizes providing calming tones and relaxing information.

[0732] This system allows the server to continuously improve its artificial intelligence model based on consumer feedback and the results of emotion engine analysis. As a result, it can provide consumers with a more personalized and advanced conversational experience.

[0733] The following describes the processing flow.

[0734] Step 1:

[0735] Users (expert information providers) create an account on the platform and log in. Through the interface, they input their expertise and speaking style characteristics, and upload audio samples as needed.

[0736] Step 2:

[0737] The server receives data sent by the user and preprocesses it. Text data is analyzed using natural language processing, and characteristics of the user's speech are extracted from audio samples.

[0738] Step 3:

[0739] The server customizes the artificial intelligence model based on the data obtained. It generates an AI agent that reflects the user's knowledge and speaking style, and also connects an emotion engine to the agent.

[0740] Step 4:

[0741] Users test the generated artificial intelligence agent on a dashboard. Simultaneously, they verify the operation of the emotion engine built into the agent and send feedback to the server as needed.

[0742] Step 5:

[0743] The server receives feedback from the user and adjusts the artificial intelligence model and emotion engine. The adjusted model is then presented to the user again for final confirmation.

[0744] Step 6:

[0745] The device (consumer's device) accesses the platform and searches for an AI agent of interest. After completing the purchase process, the agent becomes available.

[0746] Step 7:

[0747] The device (the consumer's device) activates the purchased artificial intelligence agent and begins a conversation. The emotion engine analyzes the consumer's voice tone and text to determine their emotions, and the agent adjusts its response based on the analysis results.

[0748] Step 8:

[0749] The device (the consumer's device) allows consumers to receive appropriate advice based on their emotional changes through an interactive experience with an agent. Consumer usage data and feedback are sent to a server.

[0750] Step 9:

[0751] The server continuously improves its artificial intelligence model and emotion engine based on feedback and sentiment analysis data collected from consumers. This improves the accuracy and naturalness of the agent's responses.

[0752] (Example 2)

[0753] 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".

[0754] The problem that this invention aims to solve is to build intelligent agents that reflect the characteristics of individuals with specialized knowledge, and to improve the accuracy and effectiveness of the personalized conversational experience that users obtain through those agents. Furthermore, it aims to provide a more sophisticated communication experience by automatically adjusting responses according to the user's emotions and through continuous model improvement.

[0755] 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.

[0756] In this invention, the server includes means for providing an interface for an individual with specialized knowledge to input their knowledge and speech patterns, means for individually adjusting an intelligent processing model based on the individual's input information, and means for setting up an emotion analysis device and generating a response that matches the user's emotional state. This makes it possible to construct a customized conversational agent that combines expertise and emotion recognition.

[0757] A "specialized individual" is someone who possesses advanced knowledge and skills in a specific field and provides information to agents in order to utilize that knowledge.

[0758] An "interface" is a means of inputting and outputting information between a user and a system, and a mechanism for facilitating smooth interaction with the system.

[0759] An "intelligent processing model" is a framework that uses artificial intelligence technology to learn from input data and generate inferences and responses.

[0760] A "space on a communication network" refers to online platforms and services that can be accessed via networks such as the internet, and is a place where information can be shared and obtained.

[0761] An "emotion analysis device" is a device or system that analyzes a user's emotional state from information such as voice and text, and provides appropriate responses or services corresponding to those emotions.

[0762] "Dialogue using language data" refers to the exchange of information using language, and is a form of communication that includes questions and answers using natural language.

[0763] "Opinions" refer to feedback and evaluation information provided by users and stakeholders, and are valuable information that can be used to improve systems and services.

[0764] To implement this invention, the server constructs a customized intelligent agent using a generative AI model based on data provided by individuals with specialized knowledge. Specifically, the user inputs their expertise and speaking style through an interface using a terminal and uploads voice samples as needed. This information is sent to the server, and an intelligent processing model is generated.

[0765] The server also analyzes the user's emotional state in real time using an emotion analysis device. This analysis may involve using software equipped with natural language processing technology. For example, it could include technology that understands the emotional state and provides an appropriate response based on voice and text data provided by the user.

[0766] A concrete example is an intelligent agent designed for psychological counseling. Users can input and configure their expertise in psychology and their calm speaking style to build an agent that suggests appropriate relaxation methods to stressed consumers.

[0767] The following are examples of prompt statements that are used.

[0768] "Like a skilled psychological counselor, provide advice on how to calm users when they are feeling stressed. Suggest specific ways for consumers to relax."

[0769] In this way, it becomes possible to provide personalized conversational experiences tailored to each individual user.

[0770] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0771] Step 1:

[0772] Users input their expertise and speaking style on their device and upload any necessary audio samples.

[0773] Input: User expertise, speaking style, voice sample

[0774] Operation: The user interacts with the interface to provide the necessary input data.

[0775] Output: Expertise and talk style data are sent to the server.

[0776] Step 2:

[0777] The server receives input data and customizes the intelligent processing model using a generative AI model.

[0778] Input: User-submitted expertise, talk style data, and audio samples.

[0779] Operation: The server utilizes a generated AI model to build an intelligent agent that reflects the user's characteristics. Specifically, it performs data analysis and model training.

[0780] Output: Customized intelligent agent model

[0781] Step 3:

[0782] The user configures the emotion analysis device on their terminal.

[0783] Input: Setting parameters related to sentiment analysis (e.g., sentiment detection threshold and response pattern)

[0784] Operation: The user selects emotion engine settings on the interface and sends those parameters to the server.

[0785] Output: The settings are saved on the server and reflected in the intelligent agent's response.

[0786] Step 4:

[0787] Consumers access the platform on their devices, search for agents, and make purchases.

[0788] Input: Consumer search queries and agent selection information

[0789] Operation: Consumers select agents of interest from a list and proceed with the purchase.

[0790] Output: Purchase completion notification and agent usage rights

[0791] Step 5:

[0792] The consumer activates an intelligent agent on their device and begins a dialogue.

[0793] Input: Consumer voice and text-based interactive input

[0794] Operation: Consumers communicate with the agent using a conversational interface. The agent performs sentiment analysis and generates appropriate responses.

[0795] Output: Personalized response from the agent

[0796] Step 6:

[0797] The server collects consumer feedback and interaction data, and continuously improves its intelligent processing model.

[0798] Input: Consumer feedback data, dialogue logs

[0799] Operation: The server analyzes feedback and interaction logs to adjust and improve the model.

[0800] Output: Updated intelligent agent model

[0801] In this way, an individualized conversational experience is provided by an agent that combines specialized knowledge and emotion recognition.

[0802] (Application Example 2)

[0803] 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".

[0804] In an aging society, the number of elderly people requiring care is increasing. Elderly people often experience feelings of loneliness and anxiety in their daily lives, which can impair their mental health. Traditional care systems are limited to simple information provision and bureaucratic interactions, lacking emotional support that considers the feelings of the elderly. Therefore, there is a need for techniques that enable flexible communication tailored to their emotional state.

[0805] 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.

[0806] In this invention, the server includes means for providing an interface for expert information providers to input their knowledge and speaking style, means for customizing an artificial intelligence model based on the input data from the expert information providers, and means for analyzing the user's emotional state via an emotion analysis engine and optimizing the response. This enables the generation of responses tailored to the emotional state of elderly individuals, providing personalized emotional support to the user.

[0807] A "specialized information provider" is an individual or organization that possesses extensive knowledge and experience in a specific field and has the ability to provide that knowledge and communication skills.

[0808] An "interface" is a means or device used by a user to input information or interact with a system.

[0809] An "artificial intelligence model" is a computational tool that makes judgments and predictions based on input data according to a specific purpose, and has the ability to automate specific tasks.

[0810] An "online platform" is a virtual environment on the internet for users to search for, select, and obtain specific services or products.

[0811] An "emotion analysis engine" is a technology or method for analyzing and understanding a user's emotional state from voice or text.

[0812] "Optimizing responses" is the process of selecting and providing the most appropriate and effective response or action based on the user's emotions and situation.

[0813] "Support for the elderly" refers to assistance and services provided to alleviate the mental and physical burdens on elderly people in their daily lives.

[0814] To realize this invention, expert information providers are required to access a server and input their knowledge and speaking style through an interface. In particular, expert information providers upload voice samples and text data so that the AI ​​model can be customized to reflect their characteristics. On the server, a generative AI model is built based on this data, and a customized artificial intelligence agent is formed. At this time, the construction of the AI ​​model uses a natural language processing library in Python, such as spaCy, and Google's Speech-to-Text API for speech processing.

[0815] Users who purchase the product can access the online platform using their own devices to search for and acquire artificial intelligence agents that interest them. The emotion analysis engine installed on the device analyzes the user's tone of voice and speech content in real time to determine the user's emotional state. Based on this analysis, the customized AI agent generates optimized responses to support the elderly. The responses are generated considering the user's psychological state, aiming to provide the user with a sense of security and satisfaction.

[0816] As a concrete example, if an elderly person feels lonely during a daily conversation, the AI ​​agent will engage in dialogue based on prompts such as, "Good morning, how are you feeling today? Is there anything that's bothering you about the recent weather?" to provide encouragement and comfort. Through such dialogue, personalized emotional support for the elderly is realized.

[0817] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0818] Step 1:

[0819] The server receives data on knowledge and speaking style from expert information providers. This data includes audio samples and text information. Using this data as input, the generative AI model is customized to reflect the characteristics of the expert information providers.

[0820] Step 2:

[0821] The server builds a generative AI model and creates a customized artificial intelligence agent. This process uses a natural language processing library (e.g., spaCy) to parse text data, and speech data is transcribed via Google's Speech-to-Text API. This results in an AI agent that reflects the speaking style of the expert informant.

[0822] Step 3:

[0823] The user accesses an online platform using their device and searches for an artificial intelligence agent that interests them. Once the user selects an agent, the server sends that agent to the user's device.

[0824] Step 4:

[0825] The sentiment analysis engine running on the device analyzes the user's voice and text input in real time. In this step, it analyzes the voice tone and selected words as input to determine the user's emotional state. The results of the sentiment analysis are output and transmitted to the AI ​​agent.

[0826] Step 5:

[0827] The AI ​​agent generates the optimal response based on the results of emotion analysis. The generated response is adjusted to support the psychological health of the elderly and output to the user as voice or text on the device. An example of a prompt used at this stage would be, "Good morning, how are you feeling today? Is there anything that the recent weather has been bothering you?"

[0828] 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.

[0829] 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.

[0830] 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.

[0831] 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.

[0832] 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.

[0833] 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.

[0834] 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.

[0835] 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.

[0836] 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."

[0837] 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.

[0838] 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.

[0839] 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.

[0840] 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.

[0841] 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.

[0842] 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.

[0843] 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.

[0844] 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.

[0845] 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.

[0846] 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.

[0847] 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.

[0848] 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.

[0849] The following is further disclosed regarding the embodiments described above.

[0850] (Claim 1)

[0851] A means of providing an interface for expert information providers to input their own knowledge and speaking style,

[0852] A means for customizing an artificial intelligence model based on input data from the aforementioned expert information provider,

[0853] A means of providing an online platform for the buyer to search for and acquire the customized artificial intelligence agent,

[0854] A system that includes this.

[0855] (Claim 2)

[0856] The system according to claim 1, further comprising means by which the purchaser can use the artificial intelligence agent to engage in natural language dialogue.

[0857] (Claim 3)

[0858] The system according to claim 1, further comprising means for continuously updating and improving the artificial intelligence model based on feedback from the aforementioned expert information provider.

[0859] "Example 1"

[0860] (Claim 1)

[0861] A means of providing an information processing device for expert information providers to input their expertise and communication style,

[0862] Means for adjusting the artificial intelligence processing device based on the input information of the aforementioned expert information provider,

[0863] A means for providing an electronic information infrastructure for users to explore and obtain the aforementioned adjusted artificial intelligence functions,

[0864] A means to enable artificial intelligence functions on the user's terminal through a dedicated application program,

[0865] A means for collecting user feedback and continuously correcting the artificial intelligence processing unit,

[0866] A system that includes this.

[0867] (Claim 2)

[0868] The system according to claim 1, further comprising means by which users can exchange information verbally using the artificial intelligence function.

[0869] (Claim 3)

[0870] The system according to claim 1, further comprising means for continuously modifying and improving the artificial intelligence processing device based on the user's feedback.

[0871] "Application Example 1"

[0872] (Claim 1)

[0873] A means that provides an interface for experts to offer their knowledge and communication skills,

[0874] Means for adjusting the learning model based on the data provided by the aforementioned expert,

[0875] A means for providing an online platform for users to search for and acquire the aforementioned adjusted learning agent,

[0876] A means for generating recommendations relevant to the application based on user preference information,

[0877] A means of responding to user behavior in real time and guiding the process,

[0878] A system that includes this.

[0879] (Claim 2)

[0880] The system according to claim 1, which allows users to engage in natural language dialogue using the learning agent on an online platform.

[0881] (Claim 3)

[0882] The system according to claim 1, further comprising means for progressively updating and improving the learning model based on user evaluations.

[0883] "Example 2 of combining an emotion engine"

[0884] (Claim 1)

[0885] A means of providing an interface for individuals with specialized knowledge to input their knowledge and discourse,

[0886] A means for individually adjusting an intelligent processing model based on the aforementioned input information of an individual,

[0887] Means for providing a space on a communication network for a user to search for and acquire the aforementioned coordinated intelligent agent,

[0888] A means for setting up an emotion analysis device and generating a response that matches the user's emotional state,

[0889] A system that includes this.

[0890] (Claim 2)

[0891] The system according to claim 1, further comprising means for a user to engage in dialogue using language data with the intelligent agent.

[0892] (Claim 3)

[0893] The system according to claim 1, further comprising means for continuously updating and improving an intelligent processing model based on the user's feedback.

[0894] "Application example 2 when combining with an emotional engine"

[0895] (Claim 1)

[0896] A means of providing an interface for expert information providers to input their own knowledge and speaking style,

[0897] A means for customizing an artificial intelligence model based on input data from the aforementioned expert information provider,

[0898] A means of providing an online platform for the buyer to search for and acquire the customized artificial intelligence agent,

[0899] A means for analyzing the user's emotional state via an emotion analysis engine and optimizing the response,

[0900] A means for generating dialogue content aimed at supporting the elderly,

[0901] A system that includes this.

[0902] (Claim 2)

[0903] The system according to claim 1, further comprising means by which the purchaser can use the artificial intelligence agent to engage in natural language dialogue.

[0904] (Claim 3)

[0905] The system according to claim 1, further comprising means for continuously updating and improving the artificial intelligence model based on feedback from the aforementioned expert information provider. [Explanation of Symbols]

[0906] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of providing an interface for expert information providers to input their own knowledge and speaking style, A means for customizing an artificial intelligence model based on input data from the aforementioned expert information provider, A means of providing an online platform for the buyer to search for and acquire the customized artificial intelligence agent, A system that includes this.

2. The system according to claim 1, further comprising means by which the purchaser can use the artificial intelligence agent to engage in natural language dialogue.

3. The system according to claim 1, further comprising means for continuously updating and improving the artificial intelligence model based on feedback from the aforementioned expert information provider.

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