system

Abstract

We provide the system. [Solution] A system including means for generating experts on a specific theme based on user requests, means for the generated experts to provide advice to the user, and means for providing ideas in response to user requests.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds 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 system, when a user needed advice or ideas in business or work, it was difficult to find an appropriate expert. Also, it was difficult to obtain opinions from multiple experts at once.

Means for Solving the Problems

[0005] The system of the present invention generates experts along a specific theme based on a request from a user. The generated experts provide advice to the user and also provide ideas in response to the request from the user. Three experts are generated, and the generation is performed by GPT. As a result, the user can efficiently obtain the necessary advice and ideas.

Brief Description of the Drawings

[0006] [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 Embodiment 1 of Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1 of Form Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2 of Embodiment 2. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2 of Form Example 2. [Figure 15] This is a sequence diagram showing the processing flow of the data processing system in Embodiment 3 of Example 3. [Figure 16]It is a sequence diagram showing the processing flow of the data processing system in Application Example 3 of Embodiment Example 3. [Figure 17] It is a sequence diagram showing the processing flow of the data processing system in Embodiment Example 1 of Embodiment Example 1 when combined with an emotion engine. [Figure 18] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1 of Embodiment Example 1 when combined with an emotion engine. [Figure 19] It is a sequence diagram showing the processing flow of the data processing system in Embodiment Example 2 of Embodiment Example 2 when combined with an emotion engine. [Figure 20] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 of Embodiment Example 2 when combined with an emotion engine. [Figure 21] It is a sequence diagram showing the processing flow of the data processing system in Embodiment Example 3 of Embodiment Example 3 when combined with an emotion engine. [Figure 22] It is a sequence diagram showing the processing flow of the data processing system in Application Example 3 of Embodiment Example 3 when combined with an emotion engine. [Figure 23] It is a sequence diagram showing the processing flow of the data processing system in other embodiments.

Modes for Carrying Out the Invention

[0007] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0008] First, the language used in the following description will be explained.

[0009] In the following embodiments, the labeled 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), or a TPU (TENSOR PROCESSING UNIT (registered trademark)), etc.

[0010] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0011] In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0012] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), etc.

[0013] 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."

[0014] [First Embodiment]

[0015] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0016] 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.

[0017] 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).

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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.

[0022] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0023] 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.

[0024] 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.

[0025] 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.

[0026] Next, the identification process performed by the identification processing unit 290 of the data processing device 12 will be described.

[0027] "Example of form 1"

[0028] The system of the present invention includes an interface for receiving requests from users. This interface may take the form of a web page or application, for example, and may provide fields for the user to input themes or requests.

[0029] "Example of form 2"

[0030] Upon receiving a request from a user, the system uses GPT to generate three experts aligned with the specific theme. These experts possess different areas of expertise, such as business strategy, marketing, and product development, depending on the user's request.

[0031] "Example of form 3"

[0032] Each generated expert provides advice to the user. This is achieved, for example, by displaying advice to the user in text format. Furthermore, experts offer ideas in response to user requests. This is achieved, for example, if a user is seeking new business ideas, by having each expert offer ideas from a different perspective.

[0033] The following describes the processing flow for each example of the form.

[0034] "Example of form 1"

[0035] Step 1: The user enters a specific theme or request through the system's interface (e.g., a web page or application).

[0036] Step 2: The system receives input from the user and analyzes it.

[0037] Step 3: Based on the analysis results, the system uses GPT to generate three experts on a specific topic.

[0038] "Example of form 2"

[0039] Step 1: After receiving a request from the user, the system uses GPT to generate three experts on the specific topic.

[0040] Step 2: Each generated expert provides advice to the user. This is achieved, for example, by displaying the advice to the user in text format.

[0041] Step 3: Experts also provide ideas in response to user requests. This is achieved, for example, when a user is looking for new business ideas, by having each expert provide ideas from a different perspective.

[0042] "Example of form 3"

[0043] Step 1: The system receives a request from the user and analyzes it.

[0044] Step 2: Based on the analysis results, the system uses GPT to generate three experts on a specific topic.

[0045] Step 3: Each generated expert provides advice to the user. This is achieved, for example, by displaying the advice to the user in text format.

[0046] Step 4: Experts also provide ideas in response to user requests. This is achieved, for example, when a user is looking for new business ideas, by having each expert provide ideas from a different perspective.

[0047] (Example 1)

[0048] Next, we will describe Example 1 of Form 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."

[0049] Traditional systems had the problem of being inefficient, as users had to manually search multiple sources to obtain information on a specific topic. Furthermore, there was the challenge of providing appropriate and timely responses to user requests.

[0050] 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.

[0051] In this invention, the server includes means for providing an interface for receiving requests from users, means for converting themes and requests entered by the user into prompt sentences, and means for sending the generated prompt sentences to a generation AI model and receiving a response. This enables users to efficiently obtain information and quickly receive appropriate responses to their requests.

[0052] An "interface" is the means by which a user accesses a system and enters requests, and it can take the form of a web page or an application.

[0053] A "prompt message" is a sentence that converts the theme or request entered by the user into a format suitable for the generating AI model.

[0054] A "generative AI model" is a type of artificial intelligence that uses natural language processing technology to generate responses based on input prompt sentences.

[0055] A "response" is information or suggestions generated by a generative AI model based on a prompt, and provided to the user.

[0056] A "server" is a computer system that receives requests from users, generates prompt messages, and sends them to the AI ​​model.

[0057] In an embodiment of this invention, the system is configured as follows: The server provides an interface for receiving requests from users. This interface is implemented as a web page or application and includes fields for the user to input themes or requests. When a user accesses the interface and enters a specific theme or request, the server receives the input and converts it into a prompt statement.

[0058] The server uses a programming language such as Python to format user input into a format suitable for the generative AI model. This generative AI model uses a general artificial intelligence model that employs natural language processing techniques. Specifically, if the user inputs "Think of a new recipe," the server converts this into the prompt "Please suggest a new recipe."

[0059] The server sends the generated prompt to the generative AI model and receives a response. The generative AI model generates a response based on the prompt and returns it to the server. For example, the generative AI model might suggest a "simple pasta recipe using tomatoes and basil." The server returns this response to the user, who can then view the result on the interface.

[0060] In this way, users can efficiently obtain information and quickly receive appropriate responses to their requests.

[0061] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0062] Step 1:

[0063] The user accesses the interface and inputs themes and requests. The user accesses the interface through a web browser or application and inputs requests such as "come up with a new recipe." This input forms the basis for the next processing step.

[0064] Step 2:

[0065] The server receives user input and converts it into a prompt. The server uses a Python script to format the user's request into a prompt such as "Please suggest a new recipe." This conversion is to process the data into a format that the generative AI model can easily understand.

[0066] Step 3:

[0067] The server sends the generated prompt message to the generating AI model. The server sends the prompt message to the generating AI model via the API and waits for a response. In this step, the prompt message is passed to the AI ​​model as input data.

[0068] Step 4:

[0069] The generative AI model generates a response based on the prompt. Using natural language processing techniques, the generative AI model analyzes the prompt and generates a response such as "a simple pasta recipe using tomatoes and basil." This response becomes the output data.

[0070] Step 5:

[0071] The server receives a response from the generated AI model and returns it to the user. The server receives the generated response and displays it to the user through the interface. The user can gain new information and ideas based on this response.

[0072] (Application Example 1)

[0073] Next, we will describe Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server," and the smart device 14 will be referred to as a "terminal."

[0074] In today's information-saturated age, it is difficult for users to efficiently obtain information related to specific topics. Furthermore, there is a lack of means to provide customized content tailored to user interests, creating a demand for information that meets user needs.

[0075] 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.

[0076] In this invention, the server includes means for generating information related to a specific theme based on a user request, means for providing the generated information to the user, and means for generating related content based on the theme entered by the user. This enables the user to efficiently obtain customized information according to their interests.

[0077] "User requests" refer to instructions or wishes that users input when seeking specific information or services.

[0078] "Specific theme" refers to a particular topic or field that a user is interested in.

[0079] "Means of generating information" refers to methods or devices that create relevant data and content based on user requests.

[0080] "Generated information" refers to a collection of data and content created in response to user requests.

[0081] "Means of generating content" refers to methods or devices for creating new content by combining information related to a specific theme.

[0082] "Means of providing to users" refers to methods and devices for presenting generated information or content to users.

[0083] The system for implementing this invention consists of a user terminal and a server. The user terminal is a device such as a smartphone or computer, and provides an interface for the user to input a specific theme. The server receives requests from the user and is responsible for generating relevant information and content using a generative AI model. Specifically, the server is built using Python and Flask, and uses OpenAI's GPT-3® as the generative AI model.

[0084] When a user enters a specific theme through their device, that theme is sent to the server. The server then sends the received theme to GPT-3 as a prompt, generating related information and content. The generated information is provided to the user in various formats, such as news articles, video links, and music playlists.

[0085] For example, if a user enters "space exploration," the server uses GPT-3 to generate and provide the user with the latest news articles about space exploration, links to related documentary videos, and space-related music playlists. By using a prompt such as "Please tell me the latest news articles about space exploration," it is possible to efficiently obtain information that meets the user's request.

[0086] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0087] Step 1:

[0088] The user uses a device to input a specific theme. The entered theme reflects the user's interests and concerns and is sent to the server through the device's interface. The input data is in text format and is processed as a request to the server.

[0089] Step 2:

[0090] The server sends the theme received from the user as a prompt to the generative AI model. Specifically, the server processes the request using Python and Flask and sends the prompt to OpenAI's GPT-3 API. The input is the user's theme, and the output is the related information generated by the generative AI model.

[0091] Step 3:

[0092] The GPT-3 generative AI model generates relevant information and content based on the received prompt text. The generated data includes a variety of formats, such as news articles, video links, and music playlists. The input is the prompt text, and the output is the generated content.

[0093] Step 4:

[0094] The server converts the information received from the generated AI model into an appropriate format for the user. Specifically, it converts the generated content into HTML or JSON format and sends it to the user's device. The input is the generated content, and the output is data in a format that can be displayed to the user.

[0095] Step 5:

[0096] The user's device displays and provides data received from the server. Through the displayed information, the user can obtain the latest information related to a specific topic. The input is data from the server, and the output is information visually presented to the user.

[0097] (Example 2)

[0098] Next, we will describe Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 will be referred to as the "terminal".

[0099] In today's information society, users are required to quickly obtain diverse expertise. However, obtaining expert opinions on specific topics presents a challenge, requiring considerable time and effort. Furthermore, finding the right expert to meet a user's needs is not easy.

[0100] 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.

[0101] In this invention, the server includes means for analyzing user requests and identifying relevant areas of expertise, means for generating experts corresponding to the identified areas of expertise using a generative AI model, and means for the generated experts to provide advice to the user. This enables users to quickly and efficiently obtain expert opinions on specific topics.

[0102] A "user" is an entity that inputs requests to a system and receives advice from experts.

[0103] A "request" is information that a user enters into the system to ask for information or advice on a specific topic.

[0104] A "specialized area" is a specific field in which an expert possesses knowledge and experience, identified based on the user's requirements.

[0105] A "generative AI model" is an artificial intelligence technology used to generate experts in response to user requests.

[0106] A "specialist" is a virtual character generated by a generative AI model to correspond to a specific area of ​​expertise and to provide advice to the user.

[0107] "Advice" refers to the knowledge and suggestions on a specific topic that a generated expert provides to the user.

[0108] "Feedback" refers to evaluations and additional requests that users make regarding advice from experts, and this information is used to improve the system.

[0109] The following system configurations are possible as embodiments for carrying out this invention.

[0110] The server provides an interface for receiving requests from users. Users enter requests on specific topics through a terminal. For example, they might enter a request such as, "I want to know the market launch strategy for the new product."

[0111] The server analyzes the received request and identifies the relevant areas of expertise. Natural language processing techniques can be used for this analysis. As a result of the analysis, for example, from the keyword "market entry strategy," three areas of expertise are identified: business strategy, marketing, and product development.

[0112] Next, the server uses the Generative AI Model (GPT) to generate experts corresponding to the identified area of ​​expertise. The server creates prompt statements for each expert and inputs them into GPT to generate the expert's character and their expertise. As a concrete example, a prompt statement such as "As a business strategy expert, please provide advice on launching a new product to market" is used.

[0113] The generated expert information is presented to the user via the device. The user can view advice from each expert on the device. For example, advice from a business strategy expert might include information such as, "It is important to conduct a detailed analysis of the target market and clearly define points of differentiation from competitors."

[0114] This system allows users to quickly and efficiently obtain expert opinions on specific topics.

[0115] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0116] Step 1:

[0117] The user enters a request into the system via a terminal. The information entered concerns a specific topic, such as "I want to know the market launch strategy for the new product." The terminal then sends this request to the server.

[0118] Step 2:

[0119] The server analyzes requests received from users. Natural language processing techniques are used for the analysis, identifying relevant areas of expertise based on keywords and context contained in the request. For example, the keyword "market entry strategy" might identify three areas of expertise: business strategy, marketing, and product development. The analysis results are output as identified areas of expertise.

[0120] Step 3:

[0121] The server generates experts using the Generative AI Model (GPT) based on identified areas of expertise. The server creates prompt statements for each expert and inputs them into GPT. For example, it might use a prompt statement like, "As a business strategy expert, please provide advice on launching a new product to market." Based on this, GPT generates the expert's character and expertise, and outputs it as expert information.

[0122] Step 4:

[0123] The terminal displays expert information received from the server to the user. The user can view advice from each expert on the terminal. For example, advice from a business strategy expert might include information such as, "It is important to conduct a detailed analysis of the target market and clearly define points of differentiation from competitors."

[0124] Step 5:

[0125] Users can make decisions based on the expert advice presented. They can also ask additional questions as needed to obtain further information. User feedback is sent to the server and used to improve the system.

[0126] (Application Example 2)

[0127] Next, we will describe application example 2 of form example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0128] In today's information society, users are required to quickly and accurately obtain specialized information from multiple perspectives on specific subjects that interest them. However, conventional methods make it difficult to efficiently provide users with the information they need, and there is a particular challenge in obtaining information from different perspectives at once.

[0129] 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.

[0130] In this invention, the server includes means for generating experts on a specific subject based on user requests, means for the generated experts to provide advice to the user, and means for providing ideas in response to user requests. This makes it possible for users to quickly obtain expert information on subjects of interest from multiple perspectives.

[0131] A "user" is an individual or group that attempts to obtain information using the system.

[0132] A "request" is the act or content of a user asking a system for specific information or services.

[0133] A "subject" is a specific topic or theme that a user is interested in and wants to learn more about.

[0134] An "expert" is a virtual entity that possesses advanced knowledge and experience in a specific subject and provides advice and information to users.

[0135] "Advice" refers to the knowledge and suggestions that experts provide to users, supporting their decision-making.

[0136] "Invention" refers to new ideas or solutions generated based on user requirements.

[0137] A "perspective" is an expert's unique viewpoint or approach to a particular subject.

[0138] A "generative AI model" is an artificial intelligence technology used to generate experts based on user requests.

[0139] The system for carrying out this invention generates experts related to a specific subject based on user requests and provides the user with information from multiple perspectives. The system includes a terminal such as a smartphone and a server that runs the generated AI model.

[0140] Upon receiving a request from a user, the server uses a generative AI model to generate three experts on a specific topic. These experts offer advice and insights from different perspectives to the user. The generated information is displayed to the user via their device.

[0141] This system utilizes generative AI models such as the OpenAI GPT API. The device is developed using mobile app development frameworks such as React Native. When a user inputs a topic of interest, experts related to that topic are generated, each providing information from a different perspective.

[0142] For example, if a user enters "sustainable energy" as the topic, the server will generate "business strategy experts on sustainable energy," "marketing experts," and "technology development experts," and provide information from each perspective. An example of a prompt would be, "Please generate experts in business strategy, marketing, and technology development on sustainable energy."

[0143] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0144] Step 1:

[0145] The user uses their device to enter a subject they are interested in. The entered subject is then sent to the server as text data by the application on the device.

[0146] Step 2:

[0147] The server inputs the received subject as a prompt into the AI ​​model. Specifically, it generates the prompt in the format "Generate experts in business strategy, marketing, and technology development related to the entered subject."

[0148] Step 3:

[0149] The generative AI model generates three experts based on the prompt text. Each expert is configured by the generative AI model to have a different knowledge domain in order to provide information from a different perspective.

[0150] Step 4:

[0151] The server receives the information generated by the experts and processes it into a format that is easy for the user to understand. This processed information is then sent to the terminal as text data.

[0152] Step 5:

[0153] The terminal displays information received from the server to the user. Based on the displayed information, the user can gain a multifaceted perspective on a subject of interest.

[0154] (Example 3)

[0155] Next, we will describe Embodiment 3 of Embodiment Example 3. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0156] In today's information society, users are required to quickly obtain diverse information and ideas. However, traditional methods make it difficult to find individuals with specific expertise and obtain appropriate advice and ideas. Therefore, there is a need to develop a system that can efficiently provide users with the information they require.

[0157] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 3 is realized by the following means.

[0158] In this invention, the server includes means for generating a virtual expert with specialized knowledge related to a specific subject based on input information from the user, means for the generated virtual expert to provide information to the user, and means for providing new concepts in relation to the input information from the user. This enables the user to quickly and efficiently obtain the necessary information and ideas.

[0159] An "information processing device" is a device that has the function of receiving, processing, and outputting data, and includes devices such as computers and servers.

[0160] "User" refers to an individual or group that operates an information processing device and seeks information or ideas.

[0161] "Input information" refers to the data and requests that a user provides to an information processing device, and includes formats such as prompt messages.

[0162] A "specific subject" refers to a particular field or theme of interest to the user, and serves as a standard for information processing equipment to provide information related to that subject.

[0163] A "virtual expert" refers to a virtual entity possessing specialized knowledge related to a specific subject, generated by an information processing device using a generative AI model.

[0164] "Means of providing information" refers to the methods and processes for transmitting information generated by virtual experts to users.

[0165] A "new concept" refers to novel ideas or perspectives generated by a virtual expert based on user input.

[0166] This invention is a system that uses an information processing device to generate a virtual expert related to a specific subject based on input information from a user, and provides the user with information and new concepts.

[0167] The server generates virtual experts using a generative AI model. This generative AI model includes models that utilize natural language processing techniques. Specifically, it leverages widely known natural language processing technologies. The server analyzes the prompt sentences received from the user and generates appropriate virtual experts.

[0168] The terminal's role is to send prompt messages entered by the user to the server. These prompt messages specifically indicate the information or ideas the user is seeking. For example, a user might enter a prompt message such as, "Please tell me how to conduct market research for a new product."

[0169] The server receives information from the generated virtual experts and sends it to the terminal. The terminal displays the received information to the user. This allows the user to quickly and efficiently obtain the necessary information and ideas.

[0170] This system enables users to quickly obtain diverse information and ideas, and eliminates the effort required to find personnel with specific expertise. The flow of the specific processing in Example 3 will be explained using Figure 15.

[0171] Step 1:

[0172] The user enters a prompt message through the terminal. This prompt message specifically indicates the information or idea the user is seeking. For example, the user might enter a prompt message such as, "Please tell me how to conduct market research for a new product." This input forms the basis for the next process.

[0173] Step 2:

[0174] The terminal sends the prompt text entered by the user to the server. Here, the input is the prompt text, and the output is the data sent to the server. The terminal sends this data to the server via the internet.

[0175] Step 3:

[0176] The server generates virtual experts using a generative AI model based on the received prompt text. The input is the prompt text, and the output is the generated virtual expert. The server analyzes the prompt text using natural language processing techniques and generates virtual experts with appropriate expertise.

[0177] Step 4:

[0178] The server receives information from the generated virtual experts. The input is the results generated by the virtual experts, and the output is the information provided to the user. The virtual experts generate information and ideas in text format based on prompt statements.

[0179] Step 5:

[0180] The server transmits information received from the virtual expert to the terminal. The input is the information from the virtual expert, and the output is the data to be sent to the terminal. The server transmits this data to the terminal via the internet.

[0181] Step 6:

[0182] The terminal displays information received from the server to the user. The input is information from the server, and the output is what is displayed to the user. This allows the user to review information and ideas from virtual experts.

[0183] (Application Example 3)

[0184] Next, we will describe application example 3 of form example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0185] Modern consumers face the challenge of choosing products from a multitude of options, making it difficult to determine which product is best suited to their needs. Furthermore, while access to expert advice from different perspectives would facilitate better purchasing decisions, there is a lack of readily available means to obtain such information.

[0186] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means.

[0187] In this invention, the server includes means for generating experts on a specific topic based on user requests, means for the generated experts to provide advice to the user, and means for providing ideas in response to user requests. This makes it possible for users to easily obtain expert advice from different perspectives.

[0188] A "user" refers to a consumer who uses the system to select products or seek advice.

[0189] A "request" refers to a request made by a user to the system for information or advice.

[0190] A "specific theme" refers to a topic or field that serves as a basis for experts to provide advice, based on user requests.

[0191] A "specialist" refers to a virtual advisor with knowledge on a specific topic, generated using a generative AI model in response to user requests.

[0192] "Generative AI models" refer to artificial intelligence technology used to generate experts based on user requests and provide advice.

[0193] A "prompt sentence" is text data input into a generative AI model, and refers to a sentence containing instructions or information for experts to generate advice.

[0194] "Advice" refers to suggestions and advice provided by experts in response to user requests.

[0195] "Perspective" refers to a specific viewpoint or stance on which an expert provides advice.

[0196] "Text format" refers to the format of the text information displayed to the user when the generated advice is presented.

[0197] One embodiment of this invention is a system that provides users with access to expert advice when selecting products. The system generates experts on specific topics based on the user's requests, and these experts provide advice to the user.

[0198] The server processes user requests using a generative AI model and generates prompt messages. These prompt messages include instructions for experts to generate advice. The generative AI model used is one that utilizes natural language processing technology. Specifically, software such as TENSORFLOW® and Hugging Face Transformers can be used.

[0199] The user's device is a smartphone or similar, and it displays advice sent from the server in text format. This allows users to easily obtain expert advice from different perspectives.

[0200] For example, if a user enters the request "I want to buy a new smartphone," the server will generate a prompt message such as "Please provide expert advice on the product the user is considering purchasing. The product name is 'Latest Smartphone'." Based on this prompt message, the AI ​​model generates advice, which is then displayed on the user's device.

[0201] The flow of the specific processing in Application Example 3 will be explained using Figure 16.

[0202] Step 1:

[0203] The user uses a terminal to enter a request regarding product selection. The entered request includes information such as product name and category. The terminal sends this request to the server.

[0204] Step 2:

[0205] The server analyzes the received user request and generates a prompt sentence aligned with a specific theme. This prompt sentence contains instructions that are input into the generation AI model. As part of the data processing, the user request is analyzed using natural language processing techniques to construct an appropriate prompt sentence.

[0206] Step 3:

[0207] The server processes prompt text using a generative AI model and generates expert advice. It receives prompt text as input, and the generative AI model outputs advice using natural language generation technology.

[0208] Step 4:

[0209] The server sends the generated advice to the user's terminal in text format. The outputted advice is then formatted in a way that is easy for the user to understand.

[0210] Step 5:

[0211] The user's device displays the received advice on the screen. Users can review expert advice from different perspectives and use it as a reference when choosing products.

[0212] 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.

[0213] "Example of form 1"

[0214] One embodiment of the present invention incorporates an emotion engine that recognizes the user's emotions into a system that includes means for generating experts on a specific theme based on user requests, means for the generated experts to provide advice to the user, and means for providing ideas in response to user requests. This emotion engine analyzes the user's emotions from facial expressions, tone of voice, text input, etc., and feeds the results back to the system. For example, if the user is showing emotion of joy, the emotion engine conveys that information to the system, and the system generates an expert related to joy.

[0215] "Example of form 2"

[0216] Furthermore, the generated experts provide advice to the user based on feedback from the emotion engine. For example, if the user is expressing sadness, the generated expert will offer words of comfort, providing advice tailored to the user's emotions.

[0217] "Example of form 3"

[0218] Furthermore, when providing ideas in response to user requests, feedback from the emotion engine is also taken into consideration. For example, if a user expresses anger, the generated expert will offer ideas to alleviate that anger. This makes it possible to provide more appropriate ideas that respond to the user's emotions.

[0219] The following describes the processing flow for each example of the form.

[0220] "Example of form 1"

[0221] Step 1: Receive a request from the user. This request seeks advice or ideas on a specific topic.

[0222] Step 2: Analyze the user's emotions using the emotion engine. The analysis is performed based on the user's facial expressions, tone of voice, text input, etc.

[0223] Step 3: Based on the analysis results of the emotion engine, the system generates experts aligned with a specific theme. For example, if the user indicates an emotion of joy, the system will generate experts related to joy.

[0224] "Example of form 2"

[0225] Step 1: The generated expert provides advice to the user based on feedback from the emotion engine. For example, if the user is expressing sadness, the generated expert will provide comforting words and other advice tailored to the user's emotions.

[0226] "Example of form 3"

[0227] Step 1: When providing ideas in response to user requests, feedback from the emotion engine is also taken into consideration. For example, if a user expresses anger, the generated expert will provide ideas to resolve that anger. This makes it possible to provide more appropriate ideas that respond to the user's emotions.

[0228] (Example 1)

[0229] Next, we will describe Example 1 of Form 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."

[0230] Traditional systems can generate appropriate experts and provide advice in response to user requests, but they have a challenge in providing advice and ideas that take user emotions into consideration. Furthermore, while flexible responses tailored to the user's emotional state are required, there has been a lack of effective means to achieve this.

[0231] 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.

[0232] In this invention, the server includes means for providing an interface for receiving requests from users, means for generating experts on specific themes based on user requests, and means for recognizing the user's emotions and feeding the results back to the system. This makes it possible to provide advice and ideas that are tailored to the user's emotional state.

[0233] An "interface" is the means by which a user accesses a system and enters requests, and it can take the form of a web page or an application.

[0234] An "expert" is a virtual entity created based on user requests, possessing knowledge on a specific topic and providing advice.

[0235] A "generative AI model" is an artificial intelligence technology used to generate experts in response to user requests, and it includes natural language processing.

[0236] An "emotion engine" is a technology that analyzes a user's facial expressions, tone of voice, text input, etc., to recognize the user's emotional state.

[0237] "Advice" refers to the information and suggestions that the generated expert provides to the user, and includes content that is tailored to the user's needs.

[0238] An "idea" is a new concept or proposal offered in response to user requests, and is generated according to user needs.

[0239] This invention begins with a user accessing the system through a webpage or application and entering a theme or request. For example, the user might enter a request such as "I want to learn new cooking recipes."

[0240] The server receives requests from users and analyzes their content using natural language processing techniques. This analysis extracts information necessary to generate appropriate experts based on the requests. A generative AI model is used to generate experts. This model generates virtual experts with knowledge on specific topics in response to user requests.

[0241] Furthermore, the server uses an emotion engine to recognize the user's emotions. The emotion engine analyzes the user's facial expressions, tone of voice, and text input to identify the user's emotional state. This information is fed back into the system to help provide advice and ideas tailored to the user's emotional state.

[0242] The device provides users with advice and ideas through a generated expert. For example, if a user enters "I want to know how to reduce stress," the server generates a stress management expert who, taking into account the user's emotional state, suggests relaxation techniques and stress relief methods.

[0243] An example of a prompt message might be, "I've been feeling tired lately. Please give me some advice on how to relax." In this way, users can obtain information and advice tailored to their needs.

[0244] The flow of the specific processing in Example 1 will be explained using Figure 17.

[0245] Step 1:

[0246] Users input themes and requests through the interface of a web page or application. The entered requests are sent to the server as text data. For example, a user might input "I want to know new cooking recipes."

[0247] Step 2:

[0248] The server analyzes the request received from the user. Using natural language processing techniques, it understands the content of the request and extracts information to generate the appropriate expert. This analysis identifies keywords and themes related to the request. As output, the analysis results are passed to the generating AI model.

[0249] Step 3:

[0250] The server uses a generative AI model to generate experts based on user requests. The model receives analysis results as input and generates virtual experts with knowledge on specific topics. As output, a profile of the generated expert is created.

[0251] Step 4:

[0252] The server uses an emotion engine to recognize the user's emotions. It analyzes the user's facial expressions, tone of voice, and text input to identify the user's emotional state. User interaction data is used as input, and the output is an evaluation of the emotional state.

[0253] Step 5:

[0254] The device provides the user with advice and ideas through a generated expert. The content and tone of the advice are adjusted according to the user's emotional state. For example, if the user wants to relax, it will suggest a simple, relaxing cooking recipe. The output presents specific advice and ideas for the user.

[0255] (Application Example 1)

[0256] Next, we will describe Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server," and the smart device 14 will be referred to as a "terminal."

[0257] In today's information society, users find it difficult to find relevant information from the vast amount of data available. Furthermore, there is a demand for personalized information tailored to users' emotions and circumstances, but conventional systems fail to adequately consider user emotions when providing information. Therefore, a system is needed that simultaneously generates experts who meet user needs and provides content based on user emotions.

[0258] 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.

[0259] In this invention, the server includes means for generating experts on a specific theme based on user requests, means for the generated experts to provide advice to the user, and means for analyzing the user's emotions and providing content that corresponds to those emotions. This enables the generation of experts in response to user requests and the provision of personalized content based on the user's emotions.

[0260] "User requests" refer to the wishes and needs that users input into the system.

[0261] A "specialized expert" is a virtual advisor with knowledge and experience in a specific field, generated based on the user's requests.

[0262] "Means of providing advice" refers to functions that allow generated experts to offer advice and suggestions to users.

[0263] "Means of providing ideas" refers to functions that present new ideas and solutions in response to user requests.

[0264] An "emotional engine for analyzing emotions" is a system that reads and analyzes emotions from a user's facial expressions, tone of voice, and other factors.

[0265] A "generative AI model" refers to an algorithm or program that uses artificial intelligence technology to generate experts and content.

[0266] A "prompt message" is text containing instructions or questions that are input into a generative AI model.

[0267] "Means of providing content" refers to functions that provide appropriate information and entertainment in response to the user's emotions and needs.

[0268] The system for implementing this invention generates experts based on user requests and provides content tailored to the user's emotions. The system consists of the user's terminal and a server.

[0269] The user's device is a smartphone or tablet equipped with a camera and microphone. This allows for the capture of the user's facial expressions and voice tone, and the collection of data for sentiment analysis. For sentiment analysis, Google Cloud's "Cloud Natural Language API" or Microsoft Azure's "Emotion API" can be used.

[0270] The server receives requests from users and uses a generative AI model to generate experts on specific topics. OpenAI's "GPT-3" and "ChatGPT®" can be used as generative AI models. The generated experts provide advice to users and deliver personalized content based on the user's emotions.

[0271] For example, if a user enters "travel" as a theme and expresses a desire to relax, the server will suggest travel destinations and activities related to relaxation. An example of a prompt message would be, "The user wants to relax. Please suggest relaxing travel activities."

[0272] In this way, it becomes possible to generate experts and provide content that meets the user's needs and emotions, thereby improving the user experience.

[0273] The flow of a specific process in Application Example 1 will be explained using Figure 18.

[0274] Step 1:

[0275] The user uses the terminal to input a request regarding a specific theme. The input request is sent to the server as text data.

[0276] Step 2:

[0277] Use the terminal's camera and microphone to capture the user's expression and voice tone. These data are sent to the server for sentiment analysis.

[0278] Step 3:

[0279] The server performs sentiment analysis using the received user's expression and voice tone data. For sentiment analysis, "Cloud Natural Language API" of Google Cloud or "Emotion API" of Microsoft Azure is used. As a result of the analysis, the user's emotional state is output.

[0280] Step 4:

[0281] The server inputs a prompt sentence into the generative AI model based on the user's request and emotional state. The prompt sentence includes instructions for generating an expert according to the user's request and emotion.

[0282] Step 5:

[0283] The generative AI model receives the prompt sentence as input and generates an expert along a specific theme. The generated expert includes information for providing advice to the user.

[0284] Step 6:

[0285] The server provides the user with advice from the generated expert. The advice is sent to the terminal as personalized content based on the user's emotion.

[0286] Step 7:

[0287] Users receive and view advice and content provided through their devices. This ensures that information is provided that meets the user's needs.

[0288] (Example 2)

[0289] Next, we will describe Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 will be referred to as the "terminal".

[0290] While conventional systems could provide expert advice in response to user requests, they struggled to provide appropriate advice that took into account the user's emotional state. Therefore, there is a need for a system that can provide advice that considers the user's feelings.

[0291] 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.

[0292] In this invention, the server includes means for generating experts on a specific topic based on user requests, means for the generated experts to provide advice to the user, and means for analyzing the user's emotional state. This makes it possible to provide advice that is adapted to the user's emotions.

[0293] A "user" is the entity that inputs requests into the system and receives advice from experts.

[0294] A "request" is information that a user enters into the system to ask for information or advice on a specific topic.

[0295] An "expert" is a virtual entity created based on user requests, possessing knowledge on a specific topic and providing advice to the user.

[0296] The "generative AI model" is an artificial intelligence technology used to generate experts based on user requests.

[0297] The "emotional state" refers to the emotional state shown by the user to the system, which is analyzed for the system to provide appropriate advice to the user.

[0298] "Advice" is the advice and suggestions provided by the generated experts to the user, which is adjusted based on the user's requests and emotional state.

[0299] The embodiments for implementing this invention will be described.

[0300] The user inputs requests regarding a specific theme through a terminal and sends them to the server. The server uses the generative AI model to generate three experts corresponding to the user's requests. As this generative AI model, a model utilizing natural language processing technology is used. The generated experts each have different specialized fields and provide appropriate advice for the user's requests.

[0301] Furthermore, the server uses an emotion engine to analyze the user's emotional state. The emotion engine determines the user's emotion based on the user's input text and past conversation history. Based on this analysis result, the generated experts provide advice adapted to the user's emotion.

[0302] As a specific example, when the user requests "want to consider the marketing strategy of a new product", the server uses the generative AI model to generate experts in marketing, business strategy, and product development. When the emotion engine determines that the user's emotion is "uneasy", the experts provide advice such as "To relieve uneasiness, first conduct market research and formulate a data-based strategy."

[0303] Examples of prompt texts include "want to consider the marketing strategy of a new product. Want advice from experts."

[0304] The flow of the specific processing in Example 2 will be explained using Figure 19.

[0305] Step 1:

[0306] The user enters a request on a specific topic via their device and sends it to the server. The input includes the user's request. For example, the user enters "I want to think about a marketing strategy for a new product" into the input form on their device and clicks the "Submit" button. The output is the user's request data received by the server.

[0307] Step 2:

[0308] The server generates three experts using a generative AI model based on the user's request. The input includes the user's request data. The server sends a prompt to the generative AI model, giving instructions such as "Generate a business strategy expert." The output is the data of the generated experts. Specifically, the server calls the generative AI model and generates the expert characters.

[0309] Step 3:

[0310] The server uses an emotion engine to analyze the user's emotional state. Input includes user request data and past conversation history. The server instructs the emotion engine to "analyze the user's emotions from this text." The output is data indicating the user's emotional state. Specifically, the server invokes the emotion engine and determines the user's emotions as "anxiety," "joy," etc.

[0311] Step 4:

[0312] The generated expert provides advice to the user based on feedback from the emotion engine. Input includes expert data and user emotional state data. The server instructs the expert, "The user is feeling anxious, so please provide advice with words of comfort." Output is the data of the advice provided to the user. Specifically, the expert generates advice such as, "To alleviate anxiety, let's start by conducting market research," and sends it to the user.

[0313] (Application Example 2)

[0314] Next, we will describe application example 2 of form example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0315] In modern e-commerce, users often struggle to make informed decisions about products and have difficulty obtaining appropriate advice. Furthermore, there is a need to provide advice tailored to the user's emotional state, but this is difficult to achieve with conventional systems.

[0316] 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.

[0317] In this invention, the server includes means for generating experts on a specific theme based on user requests, means for the generated experts to provide advice to the user, and means for analyzing the user's emotional state and adjusting the advice based on the analysis results. This makes it possible to provide appropriate advice that responds to the user's emotions when they are unsure about which product to choose.

[0318] "User requests" refer to the wishes and questions that users have about the system.

[0319] A "specialized expert" is a virtual advisor with knowledge in a specific field, generated based on the user's requests.

[0320] A "generated expert" is a virtual advisor created by the system, whose role is to provide advice to the user.

[0321] "Means of providing advice" refers to the methods and processes by which generated experts provide advice and suggestions to users.

[0322] "Means for analyzing a user's emotional state" refers to methods and techniques for analyzing a user's emotions and understanding their emotional state.

[0323] "Means of adjusting advice based on analysis results" refers to methods and processes for changing the content and method of advice provided based on the results of the user's emotion analysis.

[0324] "Means of providing advice regarding product selection" refers to methods and processes for providing advice and suggestions to help users make appropriate choices when selecting products.

[0325] A "generative AI model" is a model that uses artificial intelligence technology to generate data and information tailored to a specific purpose.

[0326] The system for implementing this invention generates experts on specific themes based on user requests and provides advice to the users. The system includes a function to analyze the user's emotional state and adjust the advice based on the analysis results.

[0327] The server uses a generative AI model to generate experts tailored to the user's requests. These experts provide the user with advice on product selection. The user's emotional state is analyzed using a sentiment analysis API (e.g., IBM Watson® Tone Analyzer), and the advice is adjusted based on the results.

[0328] A terminal is a device such as a smartphone or tablet that provides an interface for users to access the system. Through the terminal, users can enter requests and receive advice from experts.

[0329] For example, if a user enters a request such as, "I want to buy a new smartphone, but I don't know which one to choose," the server performs sentiment analysis and detects that the user is feeling anxious. Based on this information, the generative AI model generates an expert who provides advice on how to choose a smartphone that is right for the user. The expert might say something like, "Here are some of the latest smartphones that would suit your needs. Please choose with confidence."

[0330] Examples of prompt messages include the following:

[0331] User's request: "I want to buy a new smartphone, but I don't know which one to choose." Emotion: "Anxiety." Prompt to GPT-3: "Generate an expert who can provide advice on choosing a smartphone when the user is feeling anxious."

[0332] The flow of a specific process in Application Example 2 will be explained using Figure 20.

[0333] Step 1:

[0334] The user enters a request through the device. For example, the user might enter a request in text format, such as "I want to buy a new smartphone, but I don't know which one to get." This input becomes the basic data for the next process.

[0335] Step 2:

[0336] The terminal sends the user's request to the server. The server passes the received request to a sentiment analysis API, which analyzes the user's emotional state. The sentiment analysis API parses the request text and outputs an emotion such as "anxiety." This emotional information is used to adjust advice in the next step.

[0337] Step 3:

[0338] The server uses a generative AI model to generate experts based on user requests and emotional information. Specifically, it uses the GPT-3 API to generate prompt statements and determine the expert's character and advice. The generated experts are then ready to provide advice tailored to the user's requests.

[0339] Step 4:

[0340] The server sends the expert advice generated to the device. The device then displays the advice to the user in a chat format. For example, it might say, "Here are some of the latest smartphones that might suit your needs. Please feel free to choose."

[0341] Step 5:

[0342] Users can review the advice displayed on their device and use it as a reference when selecting products. Users can also enter further questions and request advice again if needed.

[0343] (Example 3)

[0344] Next, we will describe Embodiment 3 of Embodiment Example 3. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0345] Traditional systems could only provide advice from a single perspective in response to user requests, making it difficult to respond appropriately to users' emotions. Furthermore, they lacked the flexibility to respond to diverse user needs, highlighting the need for improved user experience.

[0346] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 3 is realized by the following means.

[0347] In this invention, the server includes means for generating experts on a specific theme based on user requests, means for the generated experts to provide advice to the user, means for analyzing the user's emotions, means for adjusting the advice based on the analyzed emotions, and means for creating prompt sentences using a generation AI model. This enables the provision of advice from diverse perspectives in response to user requests and appropriate responses in response to the user's emotions.

[0348] A "user" is an entity that uses a system to input requests and receive advice and ideas.

[0349] A "request" is information that indicates the wishes or needs that a user inputs into the system.

[0350] An "expert" is a virtual entity created using a generative AI model to provide advice and ideas to users on a specific theme.

[0351] "Advice" refers to suggestions and proposals provided by experts based on user requests.

[0352] "Emotions" refer to information that indicates the psychological state of the user, analyzed from their input.

[0353] A "generative AI model" is an algorithm that uses natural language processing techniques to generate expert opinions and prompts.

[0354] A "prompt sentence" is text input into a generative AI model, serving as an instruction to encourage the generation of expert opinions and advice.

[0355] This invention is a system that generates experts and provides advice in response to user requests. The system is composed of three main components: a server, a terminal, and the user.

[0356] The server generates experts using a generative AI model. This generative AI model leverages natural language processing techniques, for example, by using common natural language processing algorithms. The server receives user requests, creates prompts based on them, and inputs them into the generative AI model. An example of a prompt might be, "The user is seeking new business ideas. Please provide advice from a marketing perspective."

[0357] The terminal functions as an interface for receiving requests from the user. The user enters requests through the terminal and receives advice from the server. The terminal displays the advice sent from the server in text format, making it easy for the user to understand.

[0358] Users access the system via a terminal and input their requests. For example, if a user wants to open a new restaurant, they input this into the terminal, and the server generates a suitable expert and provides advice.

[0359] Furthermore, the server uses an emotion engine to analyze the user's emotions. The emotion engine identifies emotions from the user's input, for example, by utilizing a common emotion analysis API. If the user indicates anger, the server generates additional advice to alleviate that emotion.

[0360] In this way, the system provides advice from various perspectives that meet the user's needs and responds appropriately to the user's emotions. The flow of a specific process in Example 3 will be explained using Figure 21.

[0361] Step 1:

[0362] Users access the system through a terminal and enter their requests. For example, they might enter a request such as, "I want new business ideas." The entered request is then sent from the terminal to the server.

[0363] Step 2:

[0364] The server analyzes the user's request and creates a prompt message to input into the generative AI model. For example, it might generate a prompt message such as, "The user is seeking new business ideas. Please provide advice from a marketing perspective." This prompt message is then input into the generative AI model.

[0365] Step 3:

[0366] The server uses a generative AI model to generate experts based on the prompt text. The generative AI model uses natural language processing techniques to generate advice tailored to the user's request. The generated advice is temporarily stored within the server.

[0367] Step 4:

[0368] The server uses an emotion engine to analyze the user's emotions. It sends the user's requests and entered text to the emotion analysis API to identify the user's emotional state. For example, if the user is showing anger, that information is returned to the server.

[0369] Step 5:

[0370] The server adjusts the advice generated based on the sentiment analysis results. Depending on the user's emotions, it modifies the content of the advice or generates additional advice. For example, it might add suggestions to alleviate anger.

[0371] Step 6:

[0372] The server sends the final advice to the terminal. The terminal displays the received advice to the user. The user can review the advice on the terminal screen and decide on their next course of action.

[0373] (Application Example 3)

[0374] Next, we will describe application example 3 of form example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0375] When users seek new business ideas, there is a challenge in providing appropriate advice and ideas that resonate with their emotions. Furthermore, ideas provided without considering the user's feelings may not meet their needs, potentially leading to decreased user satisfaction.

[0376] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means.

[0377] In this invention, the server includes means for generating experts on a specific theme based on user requests, means for the generated experts to provide advice to the user, means for providing ideas in response to user requests, means for analyzing the user's emotions, means for inputting prompt sentences into a generation AI model based on the analyzed emotions to generate appropriate ideas, and means for presenting the generated ideas to the user. This makes it possible to provide personalized advice and ideas that are tailored to the user's emotions.

[0378] A "user" is an individual or group that uses the system to seek advice or ideas.

[0379] A "request" is information that indicates the specific needs and desires that a user has for the system.

[0380] An "expert" is a virtual entity created based on a specific theme, which provides advice and ideas to users.

[0381] A "generative AI model" is an artificial intelligence technology that generates experts based on user requests and provides appropriate ideas.

[0382] A "prompt message" is text containing instructions or questions that are input into a generative AI model.

[0383] "Emotional analysis methods" are technologies used to analyze a user's emotions and identify their emotional state.

[0384] An "idea" is a new concept or proposal provided in response to user requests.

[0385] "Advice" refers to suggestions and guidance provided based on the user's requests.

[0386] The system for implementing this invention generates experts based on user requests, performs sentiment analysis, and provides appropriate advice and ideas. The system mainly consists of a server and user terminals.

[0387] The server generates experts based on user requests using a generative AI model. These generated experts are responsible for providing advice to the user. Requests from the user's terminal are sent to the server in text format. The server receives the user's request and analyzes the user's emotions using sentiment analysis tools. Machine learning libraries such as TensorFlow can be used for this analysis.

[0388] Based on the analyzed emotions, the server inputs a prompt message into the generative AI model. This prompt message takes into account the user's request and emotional state, and the generative AI model generates appropriate ideas based on this. The generated ideas are then sent to the user's device and presented to them.

[0389] For example, if a user requests "I want new cafe business ideas," the server performs sentiment analysis and detects that the user is feeling stressed. Based on this, it inputs the prompt "Please suggest business ideas that will help the user relax when they are feeling stressed" into the generative AI model. The generative AI model then generates the idea of ​​a cafe that provides a relaxing space and presents it to the user.

[0390] In this way, the system can provide personalized advice and ideas that respond to the user's emotions.

[0391] The flow of the specific processing in Application Example 3 will be explained using Figure 22.

[0392] Step 1:

[0393] The user uses a terminal to input specific requests to the system in text format. The entered requests are sent to the server.

[0394] Step 2:

[0395] The server analyzes the received user request and identifies the user's emotions using sentiment analysis techniques. The input is the user's request text, and the output is the user's emotional state. Natural language processing techniques using TensorFlow are used for sentiment analysis.

[0396] Step 3:

[0397] The server generates prompt sentences to input to the generative AI model based on the analyzed emotional state. The input is the user's request and emotional state, and the output is the prompt sentence. The prompt sentence contains instructions for generating appropriate ideas based on the user's emotions.

[0398] Step 4:

[0399] The server inputs prompt text into a generative AI model and generates ideas in response to the user's request. The input is the prompt text, and the output is the generated idea. The generative AI model generates ideas using technologies such as OpenAI GPT.

[0400] Step 5:

[0401] The server sends the generated ideas to the user's terminal. The user can review the presented ideas on their terminal and provide feedback as needed. The output is the ideas presented to the user.

[0402] (Other examples)

[0403] Next, other embodiments will be described. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0404] When users needed expert advice on a specific topic, traditional systems struggled to generate experts tailored to the user's needs or to adjust advice based on the user's emotions. This resulted in a failure to provide users with optimal information, leading to decreased satisfaction.

[0405] The identification process performed by the identification processing unit 290 of the data processing device 12 in other embodiments is realized by the following means.

[0406] In this invention, the server includes means for analyzing the user's request and generating prompts to instruct the generation of experts related to a specific theme; means for inputting the generated prompts into a generation AI model to generate experts aligned with the specific theme; and means for recognizing the user's emotions and adjusting the content of the generated experts and advice based on those emotions. This makes it possible to generate experts that meet the user's requests and provide advice that takes the user's emotions into consideration.

[0407] A "user interface" is a means by which a user inputs requests into a system and receives responses from the system, and is typically implemented as a web browser or mobile application.

[0408] A "request" is information that a user enters into the system to ask for information or advice on a specific topic.

[0409] A "prompt" is a sentence used to instruct a generative AI model to generate an expert related to a specific topic, and it is generated by analyzing the user's request.

[0410] A "generative AI model" is an artificial intelligence model that generates experts on a specific topic based on input prompts and provides advice and ideas, and is implemented using natural language processing technology.

[0411] An "expert" is a virtual entity created by a generative AI model that provides users with advice and ideas on a specific topic.

[0412] An "emotion engine" is an analytical tool that recognizes a user's emotions and adjusts the content of expert-generated advice based on those emotions.

[0413] One embodiment of this invention provides a system for users to obtain expert advice on a specific topic. Specific embodiments are described below.

[0414] Hardware and software configuration

[0415] The device receives requests from the user through a user interface. This user interface is implemented as a web browser or mobile application. The user enters a request into an input field and presses a submit button, sending the request to the device.

[0416] The server receives requests sent from terminals and parses them using natural language processing libraries (e.g., NLTK or spaCy). Based on the parsing, it generates prompts instructing the server to produce experts related to a specific topic.

[0417] The generated prompt sentences are input into a generative AI model (e.g., OpenAI's GPT-3). The generative AI model generates experts on a specific topic based on the prompt sentences.

[0418] The server receives the response from the generative AI model and completes the expert generation. To analyze the response, it again uses a natural language processing library to extract the relevant information.

[0419] The server uses an emotion analysis engine (e.g., IBM Watson Tone Analyzer) to recognize the user's emotions. It analyzes the user's emotional state and adjusts the content of expert-generated advice based on the results.

[0420] The terminal displays expert advice and ideas received from the server to the user. The display is in text format and formatted to be easily understood by the user.

[0421] Specific example

[0422] When a user requests "advice on a new marketing strategy," the server parses this request and generates a prompt message similar to the following:

[0423] Example prompt: "Generate a marketing strategy expert and provide advice."

[0424] By inputting this prompt into the AI ​​model, the model generates an expert on marketing strategy and provides specific advice. For example, it might generate a suggestion such as, "As a new marketing strategy, strengthen your social media campaigns." This suggestion is then tailored to the user's emotional state and ultimately delivered to the user through their device.

[0425] The flow of specific processing in other embodiments will be explained using Figure 23.

[0426] Step 1:

[0427] The user enters a request through the terminal's user interface and presses the submit button. The entered request seeks advice on a specific topic. For example, a request might be, "I would like advice on a new marketing strategy." The terminal then sends this request to the server.

[0428] Step 2:

[0429] The server analyzes the user's request received from the terminal. Using a natural language processing library (e.g., NLTK or spaCy), it tokenizes the request and extracts keywords. The input is the user's request, and the output is the extracted keywords. For example, the keyword "marketing strategy" might be extracted.

[0430] Step 3:

[0431] The server generates prompt sentences to input into the AI ​​model based on the extracted keywords. These prompt sentences instruct the model to generate experts related to a specific topic. The input is the extracted keywords, and the output is the generated prompt sentence. Specifically, a prompt sentence is generated that says, "Generate experts on marketing strategies and provide advice."

[0432] Step 4:

[0433] The server inputs the generated prompt sentences into a generative AI model (e.g., OpenAI's GPT-3). Based on the prompt sentences, the generative AI model generates experts on a specific topic and provides advice and ideas. The input is the prompt sentences, and the output is the response from the generative AI model. The response includes specific advice provided by the experts.

[0434] Step 5:

[0435] The server analyzes the response from the generative AI model and completes the expert generation. It then uses a natural language processing library again to extract useful information for the user from the response. The input is the response from the generative AI model, and the output is the extracted advice and ideas. For example, a suggestion might be extracted: "Strengthen social media campaigns as a new marketing strategy."

[0436] Step 6:

[0437] The server uses a sentiment analysis engine (e.g., IBM Watson Tone Analyzer) to recognize the user's emotions. It analyzes the user's emotional state and adjusts the expert's generated advice based on the results. The input is the user's request and past interactions, and the output is the analyzed emotional state. For example, if the user is feeling anxious, the advice will be adjusted to be more reassuring.

[0438] Step 7:

[0439] The terminal displays expert advice and ideas received from the server to the user. The display is in text format and formatted for easy user understanding. The input is the advice and ideas from the server, and the output is the information displayed to the user. Specifically, it displays advice on the screen, allowing the user to choose their next action.

[0440] 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.

[0441] The data generation model 58 is a form of so-called generative AI (Artificial Intelligence). One example of the data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> 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.

[0442] Other examples of generative AI include Gemini® (registered trademark) (Internet search). <url: https: gemini.google.com ?hl="ja">) are some examples.

[0443] 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.

[0444] [Second Embodiment]

[0445] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0446] 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.

[0447] 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).

[0448] 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.

[0449] 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.

[0450] 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).

[0451] 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.

[0452] 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.

[0453] 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.

[0454] 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.

[0455] 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.

[0456] Next, the identification process performed by the identification processing unit 290 of the data processing device 12 will be described.

[0457] "Example of form 1"

[0458] The system of the present invention includes an interface for receiving requests from users. This interface may take the form of a web page or application, for example, and may provide fields for the user to input themes or requests.

[0459] "Example of form 2"

[0460] Upon receiving a request from a user, the system uses GPT to generate three experts aligned with the specific theme. These experts possess different areas of expertise, such as business strategy, marketing, and product development, depending on the user's request.

[0461] "Example of form 3"

[0462] Each generated expert provides advice to the user. This is achieved, for example, by displaying advice to the user in text format. Furthermore, experts offer ideas in response to user requests. This is achieved, for example, if a user is seeking new business ideas, by having each expert offer ideas from a different perspective.

[0463] The following describes the processing flow for each example of the form.

[0464] "Example of form 1"

[0465] Step 1: The user enters a specific theme or request through the system's interface (e.g., a web page or application).

[0466] Step 2: The system receives input from the user and analyzes it.

[0467] Step 3: Based on the analysis results, the system uses GPT to generate three experts on a specific topic.

[0468] "Example of form 2"

[0469] Step 1: After receiving a request from the user, the system uses GPT to generate three experts on the specific topic.

[0470] Step 2: Each generated expert provides advice to the user. This is achieved, for example, by displaying the advice to the user in text format.

[0471] Step 3: Experts also provide ideas in response to user requests. This is achieved, for example, when a user is looking for new business ideas, by having each expert provide ideas from a different perspective.

[0472] "Example of form 3"

[0473] Step 1: The system receives a request from the user and analyzes it.

[0474] Step 2: Based on the analysis results, the system uses GPT to generate three experts on a specific topic.

[0475] Step 3: Each generated expert provides advice to the user. This is achieved, for example, by displaying the advice to the user in text format.

[0476] Step 4: Experts also provide ideas in response to user requests. This is achieved, for example, when a user is looking for new business ideas, by having each expert provide ideas from a different perspective.

[0477] (Example 1)

[0478] Next, we will describe Example 1 of Form 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".

[0479] Traditional systems had the problem of being inefficient, as users had to manually search multiple sources to obtain information on a specific topic. Furthermore, there was the challenge of providing appropriate and timely responses to user requests.

[0480] 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.

[0481] In this invention, the server includes means for providing an interface for receiving requests from users, means for converting themes and requests entered by the user into prompt sentences, and means for sending the generated prompt sentences to a generation AI model and receiving a response. This enables users to efficiently obtain information and quickly receive appropriate responses to their requests.

[0482] An "interface" is the means by which a user accesses a system and enters requests, and it can take the form of a web page or an application.

[0483] A "prompt message" is a sentence that converts the theme or request entered by the user into a format suitable for the generating AI model.

[0484] A "generative AI model" is a type of artificial intelligence that uses natural language processing technology to generate responses based on input prompt sentences.

[0485] A "response" is information or suggestions generated by a generative AI model based on a prompt, and provided to the user.

[0486] A "server" is a computer system that receives requests from users, generates prompt messages, and sends them to the AI ​​model.

[0487] In an embodiment of this invention, the system is configured as follows: The server provides an interface for receiving requests from users. This interface is implemented as a web page or application and includes fields for the user to input themes or requests. When a user accesses the interface and enters a specific theme or request, the server receives the input and converts it into a prompt statement.

[0488] The server uses a programming language such as Python to format user input into a format suitable for the generative AI model. This generative AI model uses a general artificial intelligence model that employs natural language processing techniques. Specifically, if the user inputs "Think of a new recipe," the server converts this into the prompt "Please suggest a new recipe."

[0489] The server sends the generated prompt to the generative AI model and receives a response. The generative AI model generates a response based on the prompt and returns it to the server. For example, the generative AI model might suggest a "simple pasta recipe using tomatoes and basil." The server returns this response to the user, who can then view the result on the interface.

[0490] In this way, users can efficiently obtain information and quickly receive appropriate responses to their requests.

[0491] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0492] Step 1:

[0493] The user accesses the interface and inputs themes and requests. The user accesses the interface through a web browser or application and inputs requests such as "come up with a new recipe." This input forms the basis for the next processing step.

[0494] Step 2:

[0495] The server receives user input and converts it into a prompt. The server uses a Python script to format the user's request into a prompt such as "Please suggest a new recipe." This conversion is to process the data into a format that the generative AI model can easily understand.

[0496] Step 3:

[0497] The server sends the generated prompt message to the generating AI model. The server sends the prompt message to the generating AI model via the API and waits for a response. In this step, the prompt message is passed to the AI ​​model as input data.

[0498] Step 4:

[0499] The generative AI model generates a response based on the prompt. Using natural language processing techniques, the generative AI model analyzes the prompt and generates a response such as "a simple pasta recipe using tomatoes and basil." This response becomes the output data.

[0500] Step 5:

[0501] The server receives a response from the generated AI model and returns it to the user. The server receives the generated response and displays it to the user through the interface. The user can gain new information and ideas based on this response.

[0502] (Application Example 1)

[0503] Next, we will describe Application Example 1 of Form 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."

[0504] In today's information-saturated age, it is difficult for users to efficiently obtain information related to specific topics. Furthermore, there is a lack of means to provide customized content tailored to user interests, creating a demand for information that meets user needs.

[0505] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0506] In this invention, the server includes means for generating information related to a specific theme based on a user request, means for providing the generated information to the user, and means for generating related content based on the theme entered by the user. This enables the user to efficiently obtain customized information according to their interests.

[0507] "User requests" refer to instructions or wishes that users input when seeking specific information or services.

[0508] "Specific theme" refers to a particular topic or field that a user is interested in.

[0509] "Means of generating information" refers to methods or devices that create relevant data and content based on user requests.

[0510] "Generated information" refers to a collection of data and content created in response to user requests.

[0511] "Means of generating content" refers to methods or devices for creating new content by combining information related to a specific theme.

[0512] "Means of providing to users" refers to methods and devices for presenting generated information or content to users.

[0513] The system for implementing this invention consists of a user terminal and a server. The user terminal is a device such as a smartphone or computer, and provides an interface for the user to input a specific theme. The server is responsible for receiving requests from the user and generating relevant information and content using a generative AI model. Specifically, the server is built using Python and Flask, and uses OpenAI's GPT-3 as the generative AI model.

[0514] When a user enters a specific theme through their device, that theme is sent to the server. The server then sends the received theme to GPT-3 as a prompt, generating related information and content. The generated information is provided to the user in various formats, such as news articles, video links, and music playlists.

[0515] For example, if a user enters "space exploration," the server uses GPT-3 to generate and provide the user with the latest news articles about space exploration, links to related documentary videos, and space-related music playlists. By using a prompt such as "Please tell me the latest news articles about space exploration," it is possible to efficiently obtain information that meets the user's request.

[0516] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0517] Step 1:

[0518] The user uses a device to input a specific theme. The entered theme reflects the user's interests and concerns and is sent to the server through the device's interface. The input data is in text format and is processed as a request to the server.

[0519] Step 2:

[0520] The server sends the theme received from the user as a prompt to the generative AI model. Specifically, the server processes the request using Python and Flask and sends the prompt to OpenAI's GPT-3 API. The input is the user's theme, and the output is the related information generated by the generative AI model.

[0521] Step 3:

[0522] The GPT-3 generative AI model generates relevant information and content based on the received prompt text. The generated data includes a variety of formats, such as news articles, video links, and music playlists. The input is the prompt text, and the output is the generated content.

[0523] Step 4:

[0524] The server converts the information received from the generated AI model into an appropriate format for the user. Specifically, it converts the generated content into HTML or JSON format and sends it to the user's device. The input is the generated content, and the output is data in a format that can be displayed to the user.

[0525] Step 5:

[0526] The user's device displays and provides data received from the server. Through the displayed information, the user can obtain the latest information related to a specific topic. The input is data from the server, and the output is information visually presented to the user.

[0527] (Example 2)

[0528] Next, we will describe Example 2 of Form 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".

[0529] In today's information society, users are required to quickly obtain diverse expertise. However, obtaining expert opinions on specific topics presents a challenge, requiring considerable time and effort. Furthermore, finding the right expert to meet a user's needs is not easy.

[0530] 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.

[0531] In this invention, the server includes means for analyzing user requests and identifying relevant areas of expertise, means for generating experts corresponding to the identified areas of expertise using a generative AI model, and means for the generated experts to provide advice to the user. This enables users to quickly and efficiently obtain expert opinions on specific topics.

[0532] A "user" is an entity that inputs requests to a system and receives advice from experts.

[0533] A "request" is information that a user enters into the system to ask for information or advice on a specific topic.

[0534] A "specialized area" is a specific field in which an expert possesses knowledge and experience, identified based on the user's requirements.

[0535] A "generative AI model" is an artificial intelligence technology used to generate experts in response to user requests.

[0536] A "specialist" is a virtual character generated by a generative AI model to correspond to a specific area of ​​expertise and to provide advice to the user.

[0537] "Advice" refers to the knowledge and suggestions on a specific topic that a generated expert provides to the user.

[0538] "Feedback" refers to evaluations and additional requests that users make regarding advice from experts, and this information is used to improve the system.

[0539] The following system configurations are possible as embodiments for carrying out this invention.

[0540] The server provides an interface for receiving requests from users. Users enter requests on specific topics through a terminal. For example, they might enter a request such as, "I want to know the market launch strategy for the new product."

[0541] The server analyzes the received request and identifies the relevant areas of expertise. Natural language processing techniques can be used for this analysis. As a result of the analysis, for example, from the keyword "market entry strategy," three areas of expertise are identified: business strategy, marketing, and product development.

[0542] Next, the server uses the Generative AI Model (GPT) to generate experts corresponding to the identified area of ​​expertise. The server creates prompt statements for each expert and inputs them into GPT to generate the expert's character and their expertise. As a concrete example, a prompt statement such as "As a business strategy expert, please provide advice on launching a new product to market" is used.

[0543] The generated expert information is presented to the user via the device. The user can view advice from each expert on the device. For example, advice from a business strategy expert might include information such as, "It is important to conduct a detailed analysis of the target market and clearly define points of differentiation from competitors."

[0544] This system allows users to quickly and efficiently obtain expert opinions on specific topics.

[0545] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0546] Step 1:

[0547] The user enters a request into the system via a terminal. The information entered concerns a specific topic, such as "I want to know the market launch strategy for the new product." The terminal then sends this request to the server.

[0548] Step 2:

[0549] The server analyzes requests received from users. Natural language processing techniques are used for the analysis, identifying relevant areas of expertise based on keywords and context contained in the request. For example, the keyword "market entry strategy" might identify three areas of expertise: business strategy, marketing, and product development. The analysis results are output as identified areas of expertise.

[0550] Step 3:

[0551] The server generates experts using the Generative AI Model (GPT) based on identified areas of expertise. The server creates prompt statements for each expert and inputs them into GPT. For example, it might use a prompt statement like, "As a business strategy expert, please provide advice on launching a new product to market." Based on this, GPT generates the expert's character and expertise, and outputs it as expert information.

[0552] Step 4:

[0553] The terminal displays expert information received from the server to the user. The user can view advice from each expert on the terminal. For example, advice from a business strategy expert might include information such as, "It is important to conduct a detailed analysis of the target market and clearly define points of differentiation from competitors."

[0554] Step 5:

[0555] Users can make decisions based on the expert advice presented. They can also ask additional questions as needed to obtain further information. User feedback is sent to the server and used to improve the system.

[0556] (Application Example 2)

[0557] Next, we will describe application example 2 of form example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0558] In today's information society, users are required to quickly and accurately obtain specialized information from multiple perspectives on specific subjects that interest them. However, conventional methods make it difficult to efficiently provide users with the information they need, and there is a particular challenge in obtaining information from different perspectives at once.

[0559] 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.

[0560] In this invention, the server includes means for generating experts on a specific subject based on user requests, means for the generated experts to provide advice to the user, and means for providing ideas in response to user requests. This makes it possible for users to quickly obtain expert information on subjects of interest from multiple perspectives.

[0561] A "user" is an individual or group that attempts to obtain information using the system.

[0562] A "request" is the act or content of a user asking a system for specific information or services.

[0563] A "subject" is a specific topic or theme that a user is interested in and wants to learn more about.

[0564] An "expert" is a virtual entity that possesses advanced knowledge and experience in a specific subject and provides advice and information to users.

[0565] "Advice" refers to the knowledge and suggestions that experts provide to users, supporting their decision-making.

[0566] "Invention" refers to new ideas or solutions generated based on user requirements.

[0567] A "perspective" is an expert's unique viewpoint or approach to a particular subject.

[0568] A "generative AI model" is an artificial intelligence technology used to generate experts based on user requests.

[0569] The system for carrying out this invention generates experts related to a specific subject based on user requests and provides the user with information from multiple perspectives. The system includes a terminal such as a smartphone and a server that runs the generated AI model.

[0570] Upon receiving a request from a user, the server uses a generative AI model to generate three experts on a specific topic. These experts offer advice and insights from different perspectives to the user. The generated information is displayed to the user via their device.

[0571] This system utilizes generative AI models such as the OpenAI GPT API. The device is developed using mobile app development frameworks such as React Native. When a user inputs a topic of interest, experts related to that topic are generated, each providing information from a different perspective.

[0572] For example, if a user enters "sustainable energy" as the topic, the server will generate "business strategy experts on sustainable energy," "marketing experts," and "technology development experts," and provide information from each perspective. An example of a prompt would be, "Please generate experts in business strategy, marketing, and technology development on sustainable energy."

[0573] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0574] Step 1:

[0575] The user uses their device to enter a subject they are interested in. The entered subject is then sent to the server as text data by the application on the device.

[0576] Step 2:

[0577] The server inputs the received subject as a prompt into the AI ​​model. Specifically, it generates the prompt in the format "Generate experts in business strategy, marketing, and technology development related to the entered subject."

[0578] Step 3:

[0579] The generative AI model generates three experts based on the prompt text. Each expert is configured by the generative AI model to have a different knowledge domain in order to provide information from a different perspective.

[0580] Step 4:

[0581] The server receives the information generated by the experts and processes it into a format that is easy for the user to understand. This processed information is then sent to the terminal as text data.

[0582] Step 5:

[0583] The terminal displays information received from the server to the user. Based on the displayed information, the user can gain a multifaceted perspective on a subject of interest.

[0584] (Example 3)

[0585] Next, we will describe Embodiment 3 of Embodiment Example 3. 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".

[0586] In today's information society, users are required to quickly obtain diverse information and ideas. However, traditional methods make it difficult to find individuals with specific expertise and obtain appropriate advice and ideas. Therefore, there is a need to develop a system that can efficiently provide users with the information they require.

[0587] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 3 is realized by the following means.

[0588] In this invention, the server includes means for generating a virtual expert with specialized knowledge related to a specific subject based on input information from the user, means for the generated virtual expert to provide information to the user, and means for providing new concepts in relation to the input information from the user. This enables the user to quickly and efficiently obtain the necessary information and ideas.

[0589] An "information processing device" is a device that has the function of receiving, processing, and outputting data, and includes devices such as computers and servers.

[0590] "User" refers to an individual or group that operates an information processing device and seeks information or ideas.

[0591] "Input information" refers to the data and requests that a user provides to an information processing device, and includes formats such as prompt messages.

[0592] A "specific subject" refers to a particular field or theme of interest to the user, and serves as a standard for information processing equipment to provide information related to that subject.

[0593] A "virtual expert" refers to a virtual entity possessing specialized knowledge related to a specific subject, generated by an information processing device using a generative AI model.

[0594] "Means of providing information" refers to the methods and processes for transmitting information generated by virtual experts to users.

[0595] A "new concept" refers to novel ideas or perspectives generated by a virtual expert based on user input.

[0596] This invention is a system that uses an information processing device to generate a virtual expert related to a specific subject based on input information from a user, and provides the user with information and new concepts.

[0597] The server generates virtual experts using a generative AI model. This generative AI model includes models that utilize natural language processing techniques. Specifically, it leverages widely known natural language processing technologies. The server analyzes the prompt sentences received from the user and generates appropriate virtual experts.

[0598] The terminal's role is to send prompt messages entered by the user to the server. These prompt messages specifically indicate the information or ideas the user is seeking. For example, a user might enter a prompt message such as, "Please tell me how to conduct market research for a new product."

[0599] The server receives information from the generated virtual experts and sends it to the terminal. The terminal displays the received information to the user. This allows the user to quickly and efficiently obtain the necessary information and ideas.

[0600] This system enables users to quickly obtain diverse information and ideas, and eliminates the effort required to find personnel with specific expertise. The flow of the specific processing in Example 3 will be explained using Figure 15.

[0601] Step 1:

[0602] The user enters a prompt message through the terminal. This prompt message specifically indicates the information or idea the user is seeking. For example, the user might enter a prompt message such as, "Please tell me how to conduct market research for a new product." This input forms the basis for the next process.

[0603] Step 2:

[0604] The terminal sends the prompt text entered by the user to the server. Here, the input is the prompt text, and the output is the data sent to the server. The terminal sends this data to the server via the internet.

[0605] Step 3:

[0606] The server generates virtual experts using a generative AI model based on the received prompt text. The input is the prompt text, and the output is the generated virtual expert. The server analyzes the prompt text using natural language processing techniques and generates virtual experts with appropriate expertise.

[0607] Step 4:

[0608] The server receives information from the generated virtual experts. The input is the results generated by the virtual experts, and the output is the information provided to the user. The virtual experts generate information and ideas in text format based on prompt statements.

[0609] Step 5:

[0610] The server transmits information received from the virtual expert to the terminal. The input is the information from the virtual expert, and the output is the data to be sent to the terminal. The server transmits this data to the terminal via the internet.

[0611] Step 6:

[0612] The terminal displays information received from the server to the user. The input is information from the server, and the output is what is displayed to the user. This allows the user to review information and ideas from virtual experts.

[0613] (Application Example 3)

[0614] Next, we will describe application example 3 of form example 3. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0615] Modern consumers face the challenge of choosing products from a multitude of options, making it difficult to determine which product is best suited to their needs. Furthermore, while access to expert advice from different perspectives would facilitate better purchasing decisions, there is a lack of readily available means to obtain such information.

[0616] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means.

[0617] In this invention, the server includes means for generating experts on a specific topic based on user requests, means for the generated experts to provide advice to the user, and means for providing ideas in response to user requests. This makes it possible for users to easily obtain expert advice from different perspectives.

[0618] A "user" refers to a consumer who uses the system to select products or seek advice.

[0619] A "request" refers to a request made by a user to the system for information or advice.

[0620] A "specific theme" refers to a topic or field that serves as a basis for experts to provide advice, based on user requests.

[0621] A "specialist" refers to a virtual advisor with knowledge on a specific topic, generated using a generative AI model in response to user requests.

[0622] "Generative AI models" refer to artificial intelligence technology used to generate experts based on user requests and provide advice.

[0623] A "prompt sentence" is text data input into a generative AI model, and refers to a sentence containing instructions or information for experts to generate advice.

[0624] "Advice" refers to suggestions and advice provided by experts in response to user requests.

[0625] "Perspective" refers to a specific viewpoint or stance on which an expert provides advice.

[0626] "Text format" refers to the format of the text information displayed to the user when the generated advice is presented.

[0627] One embodiment of this invention is a system that provides users with access to expert advice when selecting products. The system generates experts on specific topics based on the user's requests, and these experts provide advice to the user.

[0628] The server processes user requests using a generative AI model and generates prompt messages. These prompt messages contain instructions for experts to generate advice. The generative AI model used is one that leverages natural language processing techniques. Specifically, software such as TensorFlow or Hugging Face Transformers can be used.

[0629] The user's device is a smartphone or similar, and it displays advice sent from the server in text format. This allows users to easily obtain expert advice from different perspectives.

[0630] For example, if a user enters the request "I want to buy a new smartphone," the server will generate a prompt message such as "Please provide expert advice on the product the user is considering purchasing. The product name is 'Latest Smartphone'." Based on this prompt message, the AI ​​model generates advice, which is then displayed on the user's device.

[0631] The flow of the specific processing in Application Example 3 will be explained using Figure 16.

[0632] Step 1:

[0633] The user uses a terminal to enter a request regarding product selection. The entered request includes information such as product name and category. The terminal sends this request to the server.

[0634] Step 2:

[0635] The server analyzes the received user request and generates a prompt sentence aligned with a specific theme. This prompt sentence contains instructions that are input into the generation AI model. As part of the data processing, the user request is analyzed using natural language processing techniques to construct an appropriate prompt sentence.

[0636] Step 3:

[0637] The server processes prompt text using a generative AI model and generates expert advice. It receives prompt text as input, and the generative AI model outputs advice using natural language generation technology.

[0638] Step 4:

[0639] The server sends the generated advice to the user's terminal in text format. The outputted advice is then formatted in a way that is easy for the user to understand.

[0640] Step 5:

[0641] The user's device displays the received advice on the screen. Users can review expert advice from different perspectives and use it as a reference when choosing products.

[0642] 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.

[0643] "Example of form 1"

[0644] One embodiment of the present invention incorporates an emotion engine that recognizes the user's emotions into a system that includes means for generating experts on a specific theme based on user requests, means for the generated experts to provide advice to the user, and means for providing ideas in response to user requests. This emotion engine analyzes the user's emotions from facial expressions, tone of voice, text input, etc., and feeds the results back to the system. For example, if the user is showing emotion of joy, the emotion engine conveys that information to the system, and the system generates an expert related to joy.

[0645] "Example of form 2"

[0646] Furthermore, the generated experts provide advice to the user based on feedback from the emotion engine. For example, if the user is expressing sadness, the generated expert will offer words of comfort, providing advice tailored to the user's emotions.

[0647] "Example of form 3"

[0648] Furthermore, when providing ideas in response to user requests, feedback from the emotion engine is also taken into consideration. For example, if a user expresses anger, the generated expert will offer ideas to alleviate that anger. This makes it possible to provide more appropriate ideas that respond to the user's emotions.

[0649] The following describes the processing flow for each example of the form.

[0650] "Example of form 1"

[0651] Step 1: Receive a request from the user. This request seeks advice or ideas on a specific topic.

[0652] Step 2: Analyze the user's emotions using the emotion engine. The analysis is performed based on the user's facial expressions, tone of voice, text input, etc.

[0653] Step 3: Based on the analysis results of the emotion engine, the system generates experts aligned with a specific theme. For example, if the user indicates an emotion of joy, the system will generate experts related to joy.

[0654] "Example of form 2"

[0655] Step 1: The generated expert provides advice to the user based on feedback from the emotion engine. For example, if the user is expressing sadness, the generated expert will provide comforting words and other advice tailored to the user's emotions.

[0656] "Example of form 3"

[0657] Step 1: When providing ideas in response to user requests, feedback from the emotion engine is also taken into consideration. For example, if a user expresses anger, the generated expert will provide ideas to resolve that anger. This makes it possible to provide more appropriate ideas that respond to the user's emotions.

[0658] (Example 1)

[0659] Next, we will describe Example 1 of Form 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".

[0660] Traditional systems can generate appropriate experts and provide advice in response to user requests, but they have a challenge in providing advice and ideas that take user emotions into consideration. Furthermore, while flexible responses tailored to the user's emotional state are required, there has been a lack of effective means to achieve this.

[0661] 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.

[0662] In this invention, the server includes means for providing an interface for receiving requests from users, means for generating experts on specific themes based on user requests, and means for recognizing the user's emotions and feeding the results back to the system. This makes it possible to provide advice and ideas that are tailored to the user's emotional state.

[0663] An "interface" is the means by which a user accesses a system and enters requests, and it can take the form of a web page or an application.

[0664] An "expert" is a virtual entity created based on user requests, possessing knowledge on a specific topic and providing advice.

[0665] A "generative AI model" is an artificial intelligence technology used to generate experts in response to user requests, and it includes natural language processing.

[0666] An "emotion engine" is a technology that analyzes a user's facial expressions, tone of voice, text input, etc., to recognize the user's emotional state.

[0667] "Advice" refers to the information and suggestions that the generated expert provides to the user, and includes content that is tailored to the user's needs.

[0668] An "idea" is a new concept or proposal offered in response to user requests, and is generated according to user needs.

[0669] This invention begins with a user accessing the system through a webpage or application and entering a theme or request. For example, the user might enter a request such as "I want to learn new cooking recipes."

[0670] The server receives requests from users and analyzes their content using natural language processing techniques. This analysis extracts information necessary to generate appropriate experts based on the requests. A generative AI model is used to generate experts. This model generates virtual experts with knowledge on specific topics in response to user requests.

[0671] Furthermore, the server uses an emotion engine to recognize the user's emotions. The emotion engine analyzes the user's facial expressions, tone of voice, and text input to identify the user's emotional state. This information is fed back into the system to help provide advice and ideas tailored to the user's emotional state.

[0672] The device provides users with advice and ideas through a generated expert. For example, if a user enters "I want to know how to reduce stress," the server generates a stress management expert who, taking into account the user's emotional state, suggests relaxation techniques and stress relief methods.

[0673] An example of a prompt message might be, "I've been feeling tired lately. Please give me some advice on how to relax." In this way, users can obtain information and advice tailored to their needs.

[0674] The flow of the specific processing in Example 1 will be explained using Figure 17.

[0675] Step 1:

[0676] Users input themes and requests through the interface of a web page or application. The entered requests are sent to the server as text data. For example, a user might input "I want to know new cooking recipes."

[0677] Step 2:

[0678] The server analyzes the request received from the user. Using natural language processing techniques, it understands the content of the request and extracts information to generate the appropriate expert. This analysis identifies keywords and themes related to the request. As output, the analysis results are passed to the generating AI model.

[0679] Step 3:

[0680] The server uses a generative AI model to generate experts based on user requests. The model receives analysis results as input and generates virtual experts with knowledge on specific topics. As output, a profile of the generated expert is created.

[0681] Step 4:

[0682] The server uses an emotion engine to recognize the user's emotions. It analyzes the user's facial expressions, tone of voice, and text input to identify the user's emotional state. User interaction data is used as input, and the output is an evaluation of the emotional state.

[0683] Step 5:

[0684] The device provides the user with advice and ideas through a generated expert. The content and tone of the advice are adjusted according to the user's emotional state. For example, if the user wants to relax, it will suggest a simple, relaxing cooking recipe. The output presents specific advice and ideas for the user.

[0685] (Application Example 1)

[0686] Next, we will describe Application Example 1 of Form 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."

[0687] In today's information society, users find it difficult to find relevant information from the vast amount of data available. Furthermore, there is a demand for personalized information tailored to users' emotions and circumstances, but conventional systems fail to adequately consider user emotions when providing information. Therefore, a system is needed that simultaneously generates experts who meet user needs and provides content based on user emotions.

[0688] 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.

[0689] In this invention, the server includes means for generating experts on a specific theme based on user requests, means for the generated experts to provide advice to the user, and means for analyzing the user's emotions and providing content that corresponds to those emotions. This enables the generation of experts in response to user requests and the provision of personalized content based on the user's emotions.

[0690] "User requests" refer to the wishes and needs that users input into the system.

[0691] A "specialized expert" is a virtual advisor with knowledge and experience in a specific field, generated based on the user's requests.

[0692] "Means of providing advice" refers to functions that allow generated experts to offer advice and suggestions to users.

[0693] "Means of providing ideas" refers to functions that present new ideas and solutions in response to user requests.

[0694] An "emotional engine for analyzing emotions" is a system that reads and analyzes emotions from a user's facial expressions, tone of voice, and other factors.

[0695] A "generative AI model" refers to an algorithm or program that uses artificial intelligence technology to generate experts and content.

[0696] A "prompt message" is text containing instructions or questions that are input into a generative AI model.

[0697] "Means of providing content" refers to functions that provide appropriate information and entertainment in response to the user's emotions and needs.

[0698] The system for implementing this invention generates experts based on user requests and provides content tailored to the user's emotions. The system consists of the user's terminal and a server.

[0699] The user's device is a smartphone or tablet equipped with a camera and microphone. This allows for the capture of the user's facial expressions and voice tone, collecting data for sentiment analysis. For sentiment analysis, Google Cloud's "Cloud Natural Language API" or Microsoft Azure's "Emotion API" can be used.

[0700] The server receives requests from users and uses a generative AI model to generate experts on specific topics. OpenAI's "GPT-3" and "ChatGPT" can be used as generative AI models. The generated experts provide advice to users and deliver personalized content based on the user's emotions.

[0701] For example, if a user enters "travel" as a theme and expresses a desire to relax, the server will suggest travel destinations and activities related to relaxation. An example of a prompt message would be, "The user wants to relax. Please suggest relaxing travel activities."

[0702] In this way, it becomes possible to generate experts and provide content that meets the user's needs and emotions, thereby improving the user experience.

[0703] The flow of a specific process in Application Example 1 will be explained using Figure 18.

[0704] Step 1:

[0705] The user uses a terminal to enter a request related to a specific theme. The entered request is sent to the server as text data.

[0706] Step 2:

[0707] The device's camera and microphone are used to capture the user's facial expressions and voice tone. This data is then sent to a server for sentiment analysis.

[0708] Step 3:

[0709] The server performs emotion analysis using the received user's facial expression and voice tone data. For emotion analysis, it uses Google Cloud's "Cloud Natural Language API" or Microsoft Azure's "Emotion API." The analysis results output the user's emotional state.

[0710] Step 4:

[0711] The server inputs prompt statements into the generative AI model based on the user's requests and emotional state. These prompt statements contain instructions for generating an expert that responds to the user's requests and emotions.

[0712] Step 5:

[0713] The generative AI model takes a prompt as input and generates an expert on a specific topic. The generated expert includes information to provide advice to the user.

[0714] Step 6:

[0715] The server provides the user with expert advice that has been generated. The advice is sent to the device as personalized content based on the user's emotions.

[0716] Step 7:

[0717] Users receive and view advice and content provided through their devices. This ensures that information is provided that meets the user's needs.

[0718] (Example 2)

[0719] Next, we will describe Example 2 of Form 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".

[0720] While conventional systems could provide expert advice in response to user requests, they struggled to provide appropriate advice that took into account the user's emotional state. Therefore, there is a need for a system that can provide advice that considers the user's feelings.

[0721] 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.

[0722] In this invention, the server includes means for generating experts on a specific topic based on user requests, means for the generated experts to provide advice to the user, and means for analyzing the user's emotional state. This makes it possible to provide advice that is adapted to the user's emotions.

[0723] A "user" is the entity that inputs requests into the system and receives advice from experts.

[0724] A "request" is information that a user enters into the system to ask for information or advice on a specific topic.

[0725] An "expert" is a virtual entity created based on user requests, possessing knowledge on a specific topic and providing advice to the user.

[0726] A "generative AI model" is an artificial intelligence technology used to generate experts based on user requests.

[0727] "Emotional state" refers to the emotional state a user expresses towards the system, and is analyzed by the system to provide appropriate advice to the user.

[0728] "Advice" refers to suggestions and recommendations provided by a generated expert to the user, which are tailored based on the user's needs and emotional state.

[0729] A description of embodiments for carrying out this invention will be given.

[0730] The user inputs a request on a specific topic via a terminal and sends it to the server. The server uses a generative AI model to generate three experts who respond to the user's request. This generative AI model utilizes natural language processing technology. Each of the generated experts has a different area of ​​expertise and provides appropriate advice to the user's request.

[0731] Furthermore, the server uses an emotion engine to analyze the user's emotional state. The emotion engine determines the user's emotions based on the user's input text and past conversation history. Based on this analysis, the generated expert provides advice tailored to the user's emotions.

[0732] For example, if a user requests to "develop a marketing strategy for a new product," the server uses a generative AI model to generate experts in marketing, business strategy, and product development. If the emotion engine determines the user's emotion is "anxiety," the expert will provide advice such as, "To alleviate your anxiety, let's start by conducting market research and developing a data-driven strategy."

[0733] An example of a prompt message would be, "I want to develop a marketing strategy for a new product. I would like expert advice."

[0734] The flow of the specific processing in Example 2 will be explained using Figure 19.

[0735] Step 1:

[0736] The user enters a request on a specific topic via their device and sends it to the server. The input includes the user's request. For example, the user enters "I want to think about a marketing strategy for a new product" into the input form on their device and clicks the "Submit" button. The output is the user's request data received by the server.

[0737] Step 2:

[0738] The server generates three experts using a generative AI model based on the user's request. The input includes the user's request data. The server sends a prompt to the generative AI model, giving instructions such as "Generate a business strategy expert." The output is the data of the generated experts. Specifically, the server calls the generative AI model and generates the expert characters.

[0739] Step 3:

[0740] The server uses an emotion engine to analyze the user's emotional state. Input includes user request data and past conversation history. The server instructs the emotion engine to "analyze the user's emotions from this text." The output is data indicating the user's emotional state. Specifically, the server invokes the emotion engine and determines the user's emotions as "anxiety," "joy," etc.

[0741] Step 4:

[0742] The generated expert provides advice to the user based on feedback from the emotion engine. Input includes expert data and user emotional state data. The server instructs the expert, "The user is feeling anxious, so please provide advice with words of comfort." Output is the data of the advice provided to the user. Specifically, the expert generates advice such as, "To alleviate anxiety, let's start by conducting market research," and sends it to the user.

[0743] (Application Example 2)

[0744] Next, we will describe application example 2 of form example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0745] In modern e-commerce, users often struggle to make informed decisions about products and have difficulty obtaining appropriate advice. Furthermore, there is a need to provide advice tailored to the user's emotional state, but this is difficult to achieve with conventional systems.

[0746] 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.

[0747] In this invention, the server includes means for generating experts on a specific theme based on user requests, means for the generated experts to provide advice to the user, and means for analyzing the user's emotional state and adjusting the advice based on the analysis results. This makes it possible to provide appropriate advice that responds to the user's emotions when they are unsure about which product to choose.

[0748] "User requests" refer to the wishes and questions that users have about the system.

[0749] A "specialized expert" is a virtual advisor with knowledge in a specific field, generated based on the user's requests.

[0750] A "generated expert" is a virtual advisor created by the system, whose role is to provide advice to the user.

[0751] "Means of providing advice" refers to the methods and processes by which generated experts provide advice and suggestions to users.

[0752] "Means for analyzing a user's emotional state" refers to methods and techniques for analyzing a user's emotions and understanding their emotional state.

[0753] "Means of adjusting advice based on analysis results" refers to methods and processes for changing the content and method of advice provided based on the results of the user's emotion analysis.

[0754] "Means of providing advice regarding product selection" refers to methods and processes for providing advice and suggestions to help users make appropriate choices when selecting products.

[0755] A "generative AI model" is a model that uses artificial intelligence technology to generate data and information tailored to a specific purpose.

[0756] The system for implementing this invention generates experts on specific themes based on user requests and provides advice to the users. The system includes a function to analyze the user's emotional state and adjust the advice based on the analysis results.

[0757] The server uses a generative AI model to generate experts tailored to the user's requests. These experts provide the user with advice on product selection. The user's emotional state is analyzed using a sentiment analysis API (e.g., IBM Watson Tone Analyzer), and the advice is adjusted based on the results.

[0758] A terminal is a device such as a smartphone or tablet that provides an interface for users to access the system. Through the terminal, users can enter requests and receive advice from experts.

[0759] For example, if a user enters a request such as, "I want to buy a new smartphone, but I don't know which one to choose," the server performs sentiment analysis and detects that the user is feeling anxious. Based on this information, the generative AI model generates an expert who provides advice on how to choose a smartphone that is right for the user. The expert might say something like, "Here are some of the latest smartphones that would suit your needs. Please choose with confidence."

[0760] Examples of prompt messages include the following:

[0761] User's request: "I want to buy a new smartphone, but I don't know which one to choose." Emotion: "Anxiety." Prompt to GPT-3: "Generate an expert who can provide advice on choosing a smartphone when the user is feeling anxious."

[0762] The flow of a specific process in Application Example 2 will be explained using Figure 20.

[0763] Step 1:

[0764] The user enters a request through the device. For example, the user might enter a request in text format, such as "I want to buy a new smartphone, but I don't know which one to get." This input becomes the basic data for the next process.

[0765] Step 2:

[0766] The terminal sends the user's request to the server. The server passes the received request to a sentiment analysis API, which analyzes the user's emotional state. The sentiment analysis API parses the request text and outputs an emotion such as "anxiety." This emotional information is used to adjust advice in the next step.

[0767] Step 3:

[0768] The server uses a generative AI model to generate experts based on user requests and emotional information. Specifically, it uses the GPT-3 API to generate prompt statements and determine the expert's character and advice. The generated experts are then ready to provide advice tailored to the user's requests.

[0769] Step 4:

[0770] The server sends the expert advice generated to the device. The device then displays the advice to the user in a chat format. For example, it might say, "Here are some of the latest smartphones that might suit your needs. Please feel free to choose."

[0771] Step 5:

[0772] Users can review the advice displayed on their device and use it as a reference when selecting products. Users can also enter further questions and request advice again if needed.

[0773] (Example 3)

[0774] Next, we will describe Embodiment 3 of Embodiment Example 3. 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".

[0775] Traditional systems could only provide advice from a single perspective in response to user requests, making it difficult to respond appropriately to users' emotions. Furthermore, they lacked the flexibility to respond to diverse user needs, highlighting the need for improved user experience.

[0776] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 3 is realized by the following means.

[0777] In this invention, the server includes means for generating experts on a specific theme based on user requests, means for the generated experts to provide advice to the user, means for analyzing the user's emotions, means for adjusting the advice based on the analyzed emotions, and means for creating prompt sentences using a generation AI model. This enables the provision of advice from diverse perspectives in response to user requests and appropriate responses in response to the user's emotions.

[0778] A "user" is an entity that uses a system to input requests and receive advice and ideas.

[0779] A "request" is information that indicates the wishes or needs that a user inputs into the system.

[0780] An "expert" is a virtual entity created using a generative AI model to provide advice and ideas to users on a specific theme.

[0781] "Advice" refers to suggestions and proposals provided by experts based on user requests.

[0782] "Emotions" refer to information that indicates the psychological state of the user, analyzed from their input.

[0783] A "generative AI model" is an algorithm that uses natural language processing techniques to generate expert opinions and prompts.

[0784] A "prompt sentence" is text input into a generative AI model, serving as an instruction to encourage the generation of expert opinions and advice.

[0785] This invention is a system that generates experts and provides advice in response to user requests. The system is composed of three main components: a server, a terminal, and the user.

[0786] The server generates experts using a generative AI model. This generative AI model leverages natural language processing techniques, for example, by using common natural language processing algorithms. The server receives user requests, creates prompts based on them, and inputs them into the generative AI model. An example of a prompt might be, "The user is seeking new business ideas. Please provide advice from a marketing perspective."

[0787] The terminal functions as an interface for receiving requests from the user. The user enters requests through the terminal and receives advice from the server. The terminal displays the advice sent from the server in text format, making it easy for the user to understand.

[0788] Users access the system via a terminal and input their requests. For example, if a user wants to open a new restaurant, they input this into the terminal, and the server generates a suitable expert and provides advice.

[0789] Furthermore, the server uses an emotion engine to analyze the user's emotions. The emotion engine identifies emotions from the user's input, for example, by utilizing a common emotion analysis API. If the user indicates anger, the server generates additional advice to alleviate that emotion.

[0790] In this way, the system provides advice from various perspectives that meet the user's needs and responds appropriately to the user's emotions. The flow of a specific process in Example 3 will be explained using Figure 21.

[0791] Step 1:

[0792] Users access the system through a terminal and enter their requests. For example, they might enter a request such as, "I want new business ideas." The entered request is then sent from the terminal to the server.

[0793] Step 2:

[0794] The server analyzes the user's request and creates a prompt message to input into the generative AI model. For example, it might generate a prompt message such as, "The user is seeking new business ideas. Please provide advice from a marketing perspective." This prompt message is then input into the generative AI model.

[0795] Step 3:

[0796] The server uses a generative AI model to generate experts based on the prompt text. The generative AI model uses natural language processing techniques to generate advice tailored to the user's request. The generated advice is temporarily stored within the server.

[0797] Step 4:

[0798] The server uses an emotion engine to analyze the user's emotions. It sends the user's requests and entered text to the emotion analysis API to identify the user's emotional state. For example, if the user is showing anger, that information is returned to the server.

[0799] Step 5:

[0800] The server adjusts the advice generated based on the sentiment analysis results. Depending on the user's emotions, it modifies the content of the advice or generates additional advice. For example, it might add suggestions to alleviate anger.

[0801] Step 6:

[0802] The server sends the final advice to the terminal. The terminal displays the received advice to the user. The user can review the advice on the terminal screen and decide on their next course of action.

[0803] (Application Example 3)

[0804] Next, we will describe application example 3 of form example 3. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0805] When users seek new business ideas, there is a challenge in providing appropriate advice and ideas that resonate with their emotions. Furthermore, ideas provided without considering the user's feelings may not meet their needs, potentially leading to decreased user satisfaction.

[0806] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means.

[0807] In this invention, the server includes means for generating experts on a specific theme based on user requests, means for the generated experts to provide advice to the user, means for providing ideas in response to user requests, means for analyzing the user's emotions, means for inputting prompt sentences into a generation AI model based on the analyzed emotions to generate appropriate ideas, and means for presenting the generated ideas to the user. This makes it possible to provide personalized advice and ideas that are tailored to the user's emotions.

[0808] A "user" is an individual or group that uses the system to seek advice or ideas.

[0809] A "request" is information that indicates the specific needs and desires that a user has for the system.

[0810] An "expert" is a virtual entity created based on a specific theme, which provides advice and ideas to users.

[0811] A "generative AI model" is an artificial intelligence technology that generates experts based on user requests and provides appropriate ideas.

[0812] A "prompt message" is text containing instructions or questions that are input into a generative AI model.

[0813] "Emotional analysis methods" are technologies used to analyze a user's emotions and identify their emotional state.

[0814] An "idea" is a new concept or proposal provided in response to user requests.

[0815] "Advice" refers to suggestions and guidance provided based on the user's requests.

[0816] The system for implementing this invention generates experts based on user requests, performs sentiment analysis, and provides appropriate advice and ideas. The system mainly consists of a server and user terminals.

[0817] The server generates experts based on user requests using a generative AI model. These generated experts are responsible for providing advice to the user. Requests from the user's terminal are sent to the server in text format. The server receives the user's request and analyzes the user's emotions using sentiment analysis tools. Machine learning libraries such as TensorFlow can be used for this analysis.

[0818] Based on the analyzed emotions, the server inputs a prompt message into the generative AI model. This prompt message takes into account the user's request and emotional state, and the generative AI model generates appropriate ideas based on this. The generated ideas are then sent to the user's device and presented to them.

[0819] For example, if a user requests "I want new cafe business ideas," the server performs sentiment analysis and detects that the user is feeling stressed. Based on this, it inputs the prompt "Please suggest business ideas that will help the user relax when they are feeling stressed" into the generative AI model. The generative AI model then generates the idea of ​​a cafe that provides a relaxing space and presents it to the user.

[0820] In this way, the system can provide personalized advice and ideas that respond to the user's emotions.

[0821] The flow of the specific processing in Application Example 3 will be explained using Figure 22.

[0822] Step 1:

[0823] The user uses a terminal to input specific requests to the system in text format. The entered requests are sent to the server.

[0824] Step 2:

[0825] The server analyzes the received user request and identifies the user's emotions using sentiment analysis techniques. The input is the user's request text, and the output is the user's emotional state. Natural language processing techniques using TensorFlow are used for sentiment analysis.

[0826] Step 3:

[0827] The server generates prompt sentences to input to the generative AI model based on the analyzed emotional state. The input is the user's request and emotional state, and the output is the prompt sentence. The prompt sentence contains instructions for generating appropriate ideas based on the user's emotions.

[0828] Step 4:

[0829] The server inputs prompt text into a generative AI model and generates ideas in response to the user's request. The input is the prompt text, and the output is the generated idea. The generative AI model generates ideas using technologies such as OpenAI GPT.

[0830] Step 5:

[0831] The server sends the generated ideas to the user's terminal. The user can review the presented ideas on their terminal and provide feedback as needed. The output is the ideas presented to the user.

[0832] (Other examples)

[0833] Since this is the same as the specific processing described in the other embodiments of the first embodiment above, the explanation will be omitted.

[0834] 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.

[0835] The data generation model 58 is a form of so-called generative AI (Artificial Intelligence). One example of the data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> 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.

[0836] Other examples of generative AI include Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) are some examples.

[0837] 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.

[0838] [Third Embodiment]

[0839] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0840] 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.

[0841] 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).

[0842] 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.

[0843] 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.

[0844] 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).

[0845] 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.

[0846] 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.

[0847] 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.

[0848] 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.

[0849] 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.

[0850] Next, the identification process performed by the identification processing unit 290 of the data processing device 12 will be described.

[0851] "Example of form 1"

[0852] The system of the present invention includes an interface for receiving requests from users. This interface may take the form of a web page or application, for example, and may provide fields for the user to input themes or requests.

[0853] "Example of form 2"

[0854] Upon receiving a request from a user, the system uses GPT to generate three experts aligned with the specific theme. These experts possess different areas of expertise, such as business strategy, marketing, and product development, depending on the user's request.

[0855] "Example of form 3"

[0856] Each generated expert provides advice to the user. This is achieved, for example, by displaying advice to the user in text format. Furthermore, experts offer ideas in response to user requests. This is achieved, for example, if a user is seeking new business ideas, by having each expert offer ideas from a different perspective.

[0857] The following describes the processing flow for each example of the form.

[0858] "Example of form 1"

[0859] Step 1: The user enters a specific theme or request through the system's interface (e.g., a web page or application).

[0860] Step 2: The system receives input from the user and analyzes it.

[0861] Step 3: Based on the analysis results, the system uses GPT to generate three experts on a specific topic.

[0862] "Example of form 2"

[0863] Step 1: After receiving a request from the user, the system uses GPT to generate three experts on the specific topic.

[0864] Step 2: Each generated expert provides advice to the user. This is achieved, for example, by displaying the advice to the user in text format.

[0865] Step 3: Experts also provide ideas in response to user requests. This is achieved, for example, when a user is looking for new business ideas, by having each expert provide ideas from a different perspective.

[0866] "Example of form 3"

[0867] Step 1: The system receives a request from the user and analyzes it.

[0868] Step 2: Based on the analysis results, the system uses GPT to generate three experts on a specific topic.

[0869] Step 3: Each generated expert provides advice to the user. This is achieved, for example, by displaying the advice to the user in text format.

[0870] Step 4: Experts also provide ideas in response to user requests. This is achieved, for example, when a user is looking for new business ideas, by having each expert provide ideas from a different perspective.

[0871] (Example 1)

[0872] Next, we will describe Embodiment 1 of 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."

[0873] Traditional systems had the problem of being inefficient, as users had to manually search multiple sources to obtain information on a specific topic. Furthermore, there was the challenge of providing appropriate and timely responses to user requests.

[0874] 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.

[0875] In this invention, the server includes means for providing an interface for receiving requests from users, means for converting themes and requests entered by the user into prompt sentences, and means for sending the generated prompt sentences to a generation AI model and receiving a response. This enables users to efficiently obtain information and quickly receive appropriate responses to their requests.

[0876] An "interface" is the means by which a user accesses a system and enters requests, and it can take the form of a web page or an application.

[0877] A "prompt message" is a sentence that converts the theme or request entered by the user into a format suitable for the generating AI model.

[0878] A "generative AI model" is a type of artificial intelligence that uses natural language processing technology to generate responses based on input prompt sentences.

[0879] A "response" is information or suggestions generated by a generative AI model based on a prompt, and provided to the user.

[0880] A "server" is a computer system that receives requests from users, generates prompt messages, and sends them to the AI ​​model.

[0881] In an embodiment of this invention, the system is configured as follows: The server provides an interface for receiving requests from users. This interface is implemented as a web page or application and includes fields for the user to input themes or requests. When a user accesses the interface and enters a specific theme or request, the server receives the input and converts it into a prompt statement.

[0882] The server uses a programming language such as Python to format user input into a format suitable for the generative AI model. This generative AI model uses a general artificial intelligence model that employs natural language processing techniques. Specifically, if the user inputs "Think of a new recipe," the server converts this into the prompt "Please suggest a new recipe."

[0883] The server sends the generated prompt to the generative AI model and receives a response. The generative AI model generates a response based on the prompt and returns it to the server. For example, the generative AI model might suggest a "simple pasta recipe using tomatoes and basil." The server returns this response to the user, who can then view the result on the interface.

[0884] In this way, users can efficiently obtain information and quickly receive appropriate responses to their requests.

[0885] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0886] Step 1:

[0887] The user accesses the interface and inputs themes and requests. The user accesses the interface through a web browser or application and inputs requests such as "come up with a new recipe." This input forms the basis for the next processing step.

[0888] Step 2:

[0889] The server receives user input and converts it into a prompt. The server uses a Python script to format the user's request into a prompt such as "Please suggest a new recipe." This conversion is to process the data into a format that the generative AI model can easily understand.

[0890] Step 3:

[0891] The server sends the generated prompt message to the generating AI model. The server sends the prompt message to the generating AI model via the API and waits for a response. In this step, the prompt message is passed to the AI ​​model as input data.

[0892] Step 4:

[0893] The generative AI model generates a response based on the prompt. Using natural language processing techniques, the generative AI model analyzes the prompt and generates a response such as "a simple pasta recipe using tomatoes and basil." This response becomes the output data.

[0894] Step 5:

[0895] The server receives a response from the generated AI model and returns it to the user. The server receives the generated response and displays it to the user through the interface. The user can gain new information and ideas based on this response.

[0896] (Application Example 1)

[0897] Next, we will describe Application Example 1 of Form 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."

[0898] In today's information-saturated age, it is difficult for users to efficiently obtain information related to specific topics. Furthermore, there is a lack of means to provide customized content tailored to user interests, creating a demand for information that meets user needs.

[0899] 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.

[0900] In this invention, the server includes means for generating information related to a specific theme based on a user request, means for providing the generated information to the user, and means for generating related content based on the theme entered by the user. This enables the user to efficiently obtain customized information according to their interests.

[0901] "User requests" refer to instructions or wishes that users input when seeking specific information or services.

[0902] "Specific theme" refers to a particular topic or field that a user is interested in.

[0903] "Means of generating information" refers to methods or devices that create relevant data and content based on user requests.

[0904] "Generated information" refers to a collection of data and content created in response to user requests.

[0905] "Means of generating content" refers to methods or devices for creating new content by combining information related to a specific theme.

[0906] "Means of providing to users" refers to methods and devices for presenting generated information or content to users.

[0907] The system for implementing this invention consists of a user terminal and a server. The user terminal is a device such as a smartphone or computer, and provides an interface for the user to input a specific theme. The server is responsible for receiving requests from the user and generating relevant information and content using a generative AI model. Specifically, the server is built using Python and Flask, and uses OpenAI's GPT-3 as the generative AI model.

[0908] When a user enters a specific theme through their device, that theme is sent to the server. The server then sends the received theme to GPT-3 as a prompt, generating related information and content. The generated information is provided to the user in various formats, such as news articles, video links, and music playlists.

[0909] For example, if a user enters "space exploration," the server uses GPT-3 to generate and provide the user with the latest news articles about space exploration, links to related documentary videos, and space-related music playlists. By using a prompt such as "Please tell me the latest news articles about space exploration," it is possible to efficiently obtain information that meets the user's request.

[0910] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0911] Step 1:

[0912] The user uses a device to input a specific theme. The entered theme reflects the user's interests and concerns and is sent to the server through the device's interface. The input data is in text format and is processed as a request to the server.

[0913] Step 2:

[0914] The server sends the theme received from the user as a prompt to the generative AI model. Specifically, the server processes the request using Python and Flask and sends the prompt to OpenAI's GPT-3 API. The input is the user's theme, and the output is the related information generated by the generative AI model.

[0915] Step 3:

[0916] The GPT-3 generative AI model generates relevant information and content based on the received prompt text. The generated data includes a variety of formats, such as news articles, video links, and music playlists. The input is the prompt text, and the output is the generated content.

[0917] Step 4:

[0918] The server converts the information received from the generated AI model into an appropriate format for the user. Specifically, it converts the generated content into HTML or JSON format and sends it to the user's device. The input is the generated content, and the output is data in a format that can be displayed to the user.

[0919] Step 5:

[0920] The user's device displays and provides data received from the server. Through the displayed information, the user can obtain the latest information related to a specific topic. The input is data from the server, and the output is information visually presented to the user.

[0921] (Example 2)

[0922] Next, we will describe Example 2 of the Form 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."

[0923] In today's information society, users are required to quickly obtain diverse expertise. However, obtaining expert opinions on specific topics presents a challenge, requiring considerable time and effort. Furthermore, finding the right expert to meet a user's needs is not easy.

[0924] 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.

[0925] In this invention, the server includes means for analyzing user requests and identifying relevant areas of expertise, means for generating experts corresponding to the identified areas of expertise using a generative AI model, and means for the generated experts to provide advice to the user. This enables users to quickly and efficiently obtain expert opinions on specific topics.

[0926] A "user" is an entity that inputs requests to a system and receives advice from experts.

[0927] A "request" is information that a user enters into the system to ask for information or advice on a specific topic.

[0928] A "specialized area" is a specific field in which an expert possesses knowledge and experience, identified based on the user's requirements.

[0929] A "generative AI model" is an artificial intelligence technology used to generate experts in response to user requests.

[0930] A "specialist" is a virtual character generated by a generative AI model to correspond to a specific area of ​​expertise and to provide advice to the user.

[0931] "Advice" refers to the knowledge and suggestions on a specific topic that a generated expert provides to the user.

[0932] "Feedback" refers to evaluations and additional requests that users make regarding advice from experts, and this information is used to improve the system.

[0933] The following system configurations are possible as embodiments for carrying out this invention.

[0934] The server provides an interface for receiving requests from users. Users enter requests on specific topics through a terminal. For example, they might enter a request such as, "I want to know the market launch strategy for the new product."

[0935] The server analyzes the received request and identifies the relevant areas of expertise. Natural language processing techniques can be used for this analysis. As a result of the analysis, for example, from the keyword "market entry strategy," three areas of expertise are identified: business strategy, marketing, and product development.

[0936] Next, the server uses the Generative AI Model (GPT) to generate experts corresponding to the identified area of ​​expertise. The server creates prompt statements for each expert and inputs them into GPT to generate the expert's character and their expertise. As a concrete example, a prompt statement such as "As a business strategy expert, please provide advice on launching a new product to market" is used.

[0937] The generated expert information is presented to the user via the device. The user can view advice from each expert on the device. For example, advice from a business strategy expert might include information such as, "It is important to conduct a detailed analysis of the target market and clearly define points of differentiation from competitors."

[0938] This system allows users to quickly and efficiently obtain expert opinions on specific topics.

[0939] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0940] Step 1:

[0941] The user enters a request into the system via a terminal. The information entered concerns a specific topic, such as "I want to know the market launch strategy for the new product." The terminal then sends this request to the server.

[0942] Step 2:

[0943] The server analyzes requests received from users. Natural language processing techniques are used for the analysis, identifying relevant areas of expertise based on keywords and context contained in the request. For example, the keyword "market entry strategy" might identify three areas of expertise: business strategy, marketing, and product development. The analysis results are output as identified areas of expertise.

[0944] Step 3:

[0945] The server generates experts using the Generative AI Model (GPT) based on identified areas of expertise. The server creates prompt statements for each expert and inputs them into GPT. For example, it might use a prompt statement like, "As a business strategy expert, please provide advice on launching a new product to market." Based on this, GPT generates the expert's character and expertise, and outputs it as expert information.

[0946] Step 4:

[0947] The terminal displays expert information received from the server to the user. The user can view advice from each expert on the terminal. For example, advice from a business strategy expert might include information such as, "It is important to conduct a detailed analysis of the target market and clearly define points of differentiation from competitors."

[0948] Step 5:

[0949] Users can make decisions based on the expert advice presented. They can also ask additional questions as needed to obtain further information. User feedback is sent to the server and used to improve the system.

[0950] (Application Example 2)

[0951] Next, we will describe application example 2 of form example 2. In the following description, the data processing device 12 will be referred to as a "server," and the headset-type terminal 314 will be referred to as a "terminal."

[0952] In today's information society, users are required to quickly and accurately obtain specialized information from multiple perspectives on specific subjects that interest them. However, conventional methods make it difficult to efficiently provide users with the information they need, and there is a particular challenge in obtaining information from different perspectives at once.

[0953] 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.

[0954] In this invention, the server includes means for generating experts on a specific subject based on user requests, means for the generated experts to provide advice to the user, and means for providing ideas in response to user requests. This makes it possible for users to quickly obtain expert information on subjects of interest from multiple perspectives.

[0955] A "user" is an individual or group that attempts to obtain information using the system.

[0956] A "request" is the act or content of a user asking a system for specific information or services.

[0957] A "subject" is a specific topic or theme that a user is interested in and wants to learn more about.

[0958] An "expert" is a virtual entity that possesses advanced knowledge and experience in a specific subject and provides advice and information to users.

[0959] "Advice" refers to the knowledge and suggestions that experts provide to users, supporting their decision-making.

[0960] "Invention" refers to new ideas or solutions generated based on user requirements.

[0961] A "perspective" is an expert's unique viewpoint or approach to a particular subject.

[0962] A "generative AI model" is an artificial intelligence technology used to generate experts based on user requests.

[0963] The system for carrying out this invention generates experts related to a specific subject based on user requests and provides the user with information from multiple perspectives. The system includes a terminal such as a smartphone and a server that runs the generated AI model.

[0964] Upon receiving a request from a user, the server uses a generative AI model to generate three experts on a specific topic. These experts offer advice and insights from different perspectives to the user. The generated information is displayed to the user via their device.

[0965] This system utilizes generative AI models such as the OpenAI GPT API. The device is developed using mobile app development frameworks such as React Native. When a user inputs a topic of interest, experts related to that topic are generated, each providing information from a different perspective.

[0966] For example, if a user enters "sustainable energy" as the topic, the server will generate "business strategy experts on sustainable energy," "marketing experts," and "technology development experts," and provide information from each perspective. An example of a prompt would be, "Please generate experts in business strategy, marketing, and technology development on sustainable energy."

[0967] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0968] Step 1:

[0969] The user uses their device to enter a subject they are interested in. The entered subject is then sent to the server as text data by the application on the device.

[0970] Step 2:

[0971] The server inputs the received subject as a prompt into the AI ​​model. Specifically, it generates the prompt in the format "Generate experts in business strategy, marketing, and technology development related to the entered subject."

[0972] Step 3:

[0973] The generative AI model generates three experts based on the prompt text. Each expert is configured by the generative AI model to have a different knowledge domain in order to provide information from a different perspective.

[0974] Step 4:

[0975] The server receives the information generated by the experts and processes it into a format that is easy for the user to understand. This processed information is then sent to the terminal as text data.

[0976] Step 5:

[0977] The terminal displays information received from the server to the user. Based on the displayed information, the user can gain a multifaceted perspective on a subject of interest.

[0978] (Example 3)

[0979] Next, we will describe Embodiment 3 of Embodiment Example 3. 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."

[0980] In today's information society, users are required to quickly obtain diverse information and ideas. However, traditional methods make it difficult to find individuals with specific expertise and obtain appropriate advice and ideas. Therefore, there is a need to develop a system that can efficiently provide users with the information they require.

[0981] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 3 is realized by the following means.

[0982] In this invention, the server includes means for generating a virtual expert with specialized knowledge related to a specific subject based on input information from the user, means for the generated virtual expert to provide information to the user, and means for providing new concepts in relation to the input information from the user. This enables the user to quickly and efficiently obtain the necessary information and ideas.

[0983] An "information processing device" is a device that has the function of receiving, processing, and outputting data, and includes devices such as computers and servers.

[0984] "User" refers to an individual or group that operates an information processing device and seeks information or ideas.

[0985] "Input information" refers to the data and requests that a user provides to an information processing device, and includes formats such as prompt messages.

[0986] A "specific subject" refers to a particular field or theme of interest to the user, and serves as a standard for information processing equipment to provide information related to that subject.

[0987] A "virtual expert" refers to a virtual entity possessing specialized knowledge related to a specific subject, generated by an information processing device using a generative AI model.

[0988] "Means of providing information" refers to the methods and processes for transmitting information generated by virtual experts to users.

[0989] A "new concept" refers to novel ideas or perspectives generated by a virtual expert based on user input.

[0990] This invention is a system that uses an information processing device to generate a virtual expert related to a specific subject based on input information from a user, and provides the user with information and new concepts.

[0991] The server generates virtual experts using a generative AI model. This generative AI model includes models that utilize natural language processing techniques. Specifically, it leverages widely known natural language processing technologies. The server analyzes the prompt sentences received from the user and generates appropriate virtual experts.

[0992] The terminal's role is to send prompt messages entered by the user to the server. These prompt messages specifically indicate the information or ideas the user is seeking. For example, a user might enter a prompt message such as, "Please tell me how to conduct market research for a new product."

[0993] The server receives information from the generated virtual experts and sends it to the terminal. The terminal displays the received information to the user. This allows the user to quickly and efficiently obtain the necessary information and ideas.

[0994] This system enables users to quickly obtain diverse information and ideas, and eliminates the effort required to find personnel with specific expertise. The flow of the specific processing in Example 3 will be explained using Figure 15.

[0995] Step 1:

[0996] The user enters a prompt message through the terminal. This prompt message specifically indicates the information or idea the user is seeking. For example, the user might enter a prompt message such as, "Please tell me how to conduct market research for a new product." This input forms the basis for the next process.

[0997] Step 2:

[0998] The terminal sends the prompt text entered by the user to the server. Here, the input is the prompt text, and the output is the data sent to the server. The terminal sends this data to the server via the internet.

[0999] Step 3:

[1000] The server generates virtual experts using a generative AI model based on the received prompt text. The input is the prompt text, and the output is the generated virtual expert. The server analyzes the prompt text using natural language processing techniques and generates virtual experts with appropriate expertise.

[1001] Step 4:

[1002] The server receives information from the generated virtual experts. The input is the results generated by the virtual experts, and the output is the information provided to the user. The virtual experts generate information and ideas in text format based on prompt statements.

[1003] Step 5:

[1004] The server transmits information received from the virtual expert to the terminal. The input is the information from the virtual expert, and the output is the data to be sent to the terminal. The server transmits this data to the terminal via the internet.

[1005] Step 6:

[1006] The terminal displays information received from the server to the user. The input is information from the server, and the output is what is displayed to the user. This allows the user to review information and ideas from virtual experts.

[1007] (Application Example 3)

[1008] Next, we will describe application example 3 of form example 3. 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."

[1009] Modern consumers face the challenge of choosing products from a multitude of options, making it difficult to determine which product is best suited to their needs. Furthermore, while access to expert advice from different perspectives would facilitate better purchasing decisions, there is a lack of readily available means to obtain such information.

[1010] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means.

[1011] In this invention, the server includes means for generating experts on a specific topic based on user requests, means for the generated experts to provide advice to the user, and means for providing ideas in response to user requests. This makes it possible for users to easily obtain expert advice from different perspectives.

[1012] A "user" refers to a consumer who uses the system to select products or seek advice.

[1013] A "request" refers to a request made by a user to the system for information or advice.

[1014] A "specific theme" refers to a topic or field that serves as a basis for experts to provide advice, based on user requests.

[1015] A "specialist" refers to a virtual advisor with knowledge on a specific topic, generated using a generative AI model in response to user requests.

[1016] "Generative AI models" refer to artificial intelligence technology used to generate experts based on user requests and provide advice.

[1017] A "prompt sentence" is text data input into a generative AI model, and refers to a sentence containing instructions or information for experts to generate advice.

[1018] "Advice" refers to suggestions and advice provided by experts in response to user requests.

[1019] "Perspective" refers to a specific viewpoint or stance on which an expert provides advice.

[1020] "Text format" refers to the format of the text information displayed to the user when the generated advice is presented.

[1021] One embodiment of this invention is a system that provides users with access to expert advice when selecting products. The system generates experts on specific topics based on the user's requests, and these experts provide advice to the user.

[1022] The server processes user requests using a generative AI model and generates prompt messages. These prompt messages contain instructions for experts to generate advice. The generative AI model used is one that leverages natural language processing techniques. Specifically, software such as TensorFlow or Hugging Face Transformers can be used.

[1023] The user's device is a smartphone or similar, and it displays advice sent from the server in text format. This allows users to easily obtain expert advice from different perspectives.

[1024] For example, if a user enters the request "I want to buy a new smartphone," the server will generate a prompt message such as "Please provide expert advice on the product the user is considering purchasing. The product name is 'Latest Smartphone'." Based on this prompt message, the AI ​​model generates advice, which is then displayed on the user's device.

[1025] The flow of the specific processing in Application Example 3 will be explained using Figure 16.

[1026] Step 1:

[1027] The user uses a terminal to enter a request regarding product selection. The entered request includes information such as product name and category. The terminal sends this request to the server.

[1028] Step 2:

[1029] The server analyzes the received user request and generates a prompt sentence aligned with a specific theme. This prompt sentence contains instructions that are input into the generation AI model. As part of the data processing, the user request is analyzed using natural language processing techniques to construct an appropriate prompt sentence.

[1030] Step 3:

[1031] The server processes prompt text using a generative AI model and generates expert advice. It receives prompt text as input, and the generative AI model outputs advice using natural language generation technology.

[1032] Step 4:

[1033] The server sends the generated advice to the user's terminal in text format. The outputted advice is then formatted in a way that is easy for the user to understand.

[1034] Step 5:

[1035] The user's device displays the received advice on the screen. Users can review expert advice from different perspectives and use it as a reference when choosing products.

[1036] 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.

[1037] "Example of form 1"

[1038] One embodiment of the present invention incorporates an emotion engine that recognizes the user's emotions into a system that includes means for generating experts on a specific theme based on user requests, means for the generated experts to provide advice to the user, and means for providing ideas in response to user requests. This emotion engine analyzes the user's emotions from facial expressions, tone of voice, text input, etc., and feeds the results back to the system. For example, if the user is showing emotion of joy, the emotion engine conveys that information to the system, and the system generates an expert related to joy.

[1039] "Example of form 2"

[1040] Furthermore, the generated experts provide advice to the user based on feedback from the emotion engine. For example, if the user is expressing sadness, the generated expert will offer words of comfort, providing advice tailored to the user's emotions.

[1041] "Example of form 3"

[1042] Furthermore, when providing ideas in response to user requests, feedback from the emotion engine is also taken into consideration. For example, if a user expresses anger, the generated expert will offer ideas to alleviate that anger. This makes it possible to provide more appropriate ideas that respond to the user's emotions.

[1043] The following describes the processing flow for each example of the form.

[1044] "Example of form 1"

[1045] Step 1: Receive a request from the user. This request seeks advice or ideas on a specific topic.

[1046] Step 2: Analyze the user's emotions using the emotion engine. The analysis is performed based on the user's facial expressions, tone of voice, text input, etc.

[1047] Step 3: Based on the analysis results of the emotion engine, the system generates experts aligned with a specific theme. For example, if the user indicates an emotion of joy, the system will generate experts related to joy.

[1048] "Example of form 2"

[1049] Step 1: The generated expert provides advice to the user based on feedback from the emotion engine. For example, if the user is expressing sadness, the generated expert will provide comforting words and other advice tailored to the user's emotions.

[1050] "Example of form 3"

[1051] Step 1: When providing ideas in response to user requests, feedback from the emotion engine is also taken into consideration. For example, if a user expresses anger, the generated expert will provide ideas to resolve that anger. This makes it possible to provide more appropriate ideas that respond to the user's emotions.

[1052] (Example 1)

[1053] Next, we will describe Embodiment 1 of 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."

[1054] Traditional systems can generate appropriate experts and provide advice in response to user requests, but they have a challenge in providing advice and ideas that take user emotions into consideration. Furthermore, while flexible responses tailored to the user's emotional state are required, there has been a lack of effective means to achieve this.

[1055] 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.

[1056] In this invention, the server includes means for providing an interface for receiving requests from users, means for generating experts on specific themes based on user requests, and means for recognizing the user's emotions and feeding the results back to the system. This makes it possible to provide advice and ideas that are tailored to the user's emotional state.

[1057] An "interface" is the means by which a user accesses a system and enters requests, and it can take the form of a web page or an application.

[1058] An "expert" is a virtual entity created based on user requests, possessing knowledge on a specific topic and providing advice.

[1059] A "generative AI model" is an artificial intelligence technology used to generate experts in response to user requests, and it includes natural language processing.

[1060] An "emotion engine" is a technology that analyzes a user's facial expressions, tone of voice, text input, etc., to recognize the user's emotional state.

[1061] "Advice" refers to the information and suggestions that the generated expert provides to the user, and includes content that is tailored to the user's needs.

[1062] An "idea" is a new concept or proposal offered in response to user requests, and is generated according to user needs.

[1063] This invention begins with a user accessing the system through a webpage or application and entering a theme or request. For example, the user might enter a request such as "I want to learn new cooking recipes."

[1064] The server receives requests from users and analyzes their content using natural language processing techniques. This analysis extracts information necessary to generate appropriate experts based on the requests. A generative AI model is used to generate experts. This model generates virtual experts with knowledge on specific topics in response to user requests.

[1065] Furthermore, the server uses an emotion engine to recognize the user's emotions. The emotion engine analyzes the user's facial expressions, tone of voice, and text input to identify the user's emotional state. This information is fed back into the system to help provide advice and ideas tailored to the user's emotional state.

[1066] The device provides users with advice and ideas through a generated expert. For example, if a user enters "I want to know how to reduce stress," the server generates a stress management expert who, taking into account the user's emotional state, suggests relaxation techniques and stress relief methods.

[1067] An example of a prompt message might be, "I've been feeling tired lately. Please give me some advice on how to relax." In this way, users can obtain information and advice tailored to their needs.

[1068] The flow of the specific processing in Example 1 will be explained using Figure 17.

[1069] Step 1:

[1070] Users input themes and requests through the interface of a web page or application. The entered requests are sent to the server as text data. For example, a user might input "I want to know new cooking recipes."

[1071] Step 2:

[1072] The server analyzes the request received from the user. Using natural language processing techniques, it understands the content of the request and extracts information to generate the appropriate expert. This analysis identifies keywords and themes related to the request. As output, the analysis results are passed to the generating AI model.

[1073] Step 3:

[1074] The server uses a generative AI model to generate experts based on user requests. The model receives analysis results as input and generates virtual experts with knowledge on specific topics. As output, a profile of the generated expert is created.

[1075] Step 4:

[1076] The server uses an emotion engine to recognize the user's emotions. It analyzes the user's facial expressions, tone of voice, and text input to identify the user's emotional state. User interaction data is used as input, and the output is an evaluation of the emotional state.

[1077] Step 5:

[1078] The device provides the user with advice and ideas through a generated expert. The content and tone of the advice are adjusted according to the user's emotional state. For example, if the user wants to relax, it will suggest a simple, relaxing cooking recipe. The output presents specific advice and ideas for the user.

[1079] (Application Example 1)

[1080] Next, we will describe Application Example 1 of Form 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."

[1081] In today's information society, users find it difficult to find relevant information from the vast amount of data available. Furthermore, there is a demand for personalized information tailored to users' emotions and circumstances, but conventional systems fail to adequately consider user emotions when providing information. Therefore, a system is needed that simultaneously generates experts who meet user needs and provides content based on user emotions.

[1082] 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.

[1083] In this invention, the server includes means for generating experts on a specific theme based on user requests, means for the generated experts to provide advice to the user, and means for analyzing the user's emotions and providing content that corresponds to those emotions. This enables the generation of experts in response to user requests and the provision of personalized content based on the user's emotions.

[1084] "User requests" refer to the wishes and needs that users input into the system.

[1085] A "specialized expert" is a virtual advisor with knowledge and experience in a specific field, generated based on the user's requests.

[1086] "Means of providing advice" refers to functions that allow generated experts to offer advice and suggestions to users.

[1087] "Means of providing ideas" refers to functions that present new ideas and solutions in response to user requests.

[1088] An "emotional engine for analyzing emotions" is a system that reads and analyzes emotions from a user's facial expressions, tone of voice, and other factors.

[1089] A "generative AI model" refers to an algorithm or program that uses artificial intelligence technology to generate experts and content.

[1090] A "prompt message" is text containing instructions or questions that are input into a generative AI model.

[1091] "Means of providing content" refers to functions that provide appropriate information and entertainment in response to the user's emotions and needs.

[1092] The system for implementing this invention generates experts based on user requests and provides content tailored to the user's emotions. The system consists of the user's terminal and a server.

[1093] The user's device is a smartphone or tablet equipped with a camera and microphone. This allows for the capture of the user's facial expressions and voice tone, collecting data for sentiment analysis. For sentiment analysis, Google Cloud's "Cloud Natural Language API" or Microsoft Azure's "Emotion API" can be used.

[1094] The server receives requests from users and uses a generative AI model to generate experts on specific topics. OpenAI's "GPT-3" and "ChatGPT" can be used as generative AI models. The generated experts provide advice to users and deliver personalized content based on the user's emotions.

[1095] For example, if a user enters "travel" as a theme and expresses a desire to relax, the server will suggest travel destinations and activities related to relaxation. An example of a prompt message would be, "The user wants to relax. Please suggest relaxing travel activities."

[1096] In this way, it becomes possible to generate experts and provide content that meets the user's needs and emotions, thereby improving the user experience.

[1097] The flow of a specific process in Application Example 1 will be explained using Figure 18.

[1098] Step 1:

[1099] The user uses a terminal to enter a request related to a specific theme. The entered request is sent to the server as text data.

[1100] Step 2:

[1101] The device's camera and microphone are used to capture the user's facial expressions and voice tone. This data is then sent to a server for sentiment analysis.

[1102] Step 3:

[1103] The server performs emotion analysis using the received user's facial expression and voice tone data. For emotion analysis, it uses Google Cloud's "Cloud Natural Language API" or Microsoft Azure's "Emotion API." The analysis results output the user's emotional state.

[1104] Step 4:

[1105] The server inputs prompt statements into the generative AI model based on the user's requests and emotional state. These prompt statements contain instructions for generating an expert that responds to the user's requests and emotions.

[1106] Step 5:

[1107] The generative AI model takes a prompt as input and generates an expert on a specific topic. The generated expert includes information to provide advice to the user.

[1108] Step 6:

[1109] The server provides the user with expert advice that has been generated. The advice is sent to the device as personalized content based on the user's emotions.

[1110] Step 7:

[1111] Users receive and view advice and content provided through their devices. This ensures that information is provided that meets the user's needs.

[1112] (Example 2)

[1113] Next, we will describe Example 2 of the Form 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."

[1114] While conventional systems could provide expert advice in response to user requests, they struggled to provide appropriate advice that took into account the user's emotional state. Therefore, there is a need for a system that can provide advice that considers the user's feelings.

[1115] 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.

[1116] In this invention, the server includes means for generating experts on a specific topic based on user requests, means for the generated experts to provide advice to the user, and means for analyzing the user's emotional state. This makes it possible to provide advice that is adapted to the user's emotions.

[1117] A "user" is the entity that inputs requests into the system and receives advice from experts.

[1118] A "request" is information that a user enters into the system to ask for information or advice on a specific topic.

[1119] An "expert" is a virtual entity created based on user requests, possessing knowledge on a specific topic and providing advice to the user.

[1120] A "generative AI model" is an artificial intelligence technology used to generate experts based on user requests.

[1121] "Emotional state" refers to the emotional state a user expresses towards the system, and is analyzed by the system to provide appropriate advice to the user.

[1122] "Advice" refers to suggestions and recommendations provided by a generated expert to the user, which are tailored based on the user's needs and emotional state.

[1123] A description of embodiments for carrying out this invention will be given.

[1124] The user inputs a request on a specific topic via a terminal and sends it to the server. The server uses a generative AI model to generate three experts who respond to the user's request. This generative AI model utilizes natural language processing technology. Each of the generated experts has a different area of ​​expertise and provides appropriate advice to the user's request.

[1125] Furthermore, the server uses an emotion engine to analyze the user's emotional state. The emotion engine determines the user's emotions based on the user's input text and past conversation history. Based on this analysis, the generated expert provides advice tailored to the user's emotions.

[1126] For example, if a user requests to "develop a marketing strategy for a new product," the server uses a generative AI model to generate experts in marketing, business strategy, and product development. If the emotion engine determines the user's emotion is "anxiety," the expert will provide advice such as, "To alleviate your anxiety, let's start by conducting market research and developing a data-driven strategy."

[1127] An example of a prompt message would be, "I want to develop a marketing strategy for a new product. I would like expert advice."

[1128] The flow of the specific processing in Example 2 will be explained using Figure 19.

[1129] Step 1:

[1130] The user enters a request on a specific topic via their device and sends it to the server. The input includes the user's request. For example, the user enters "I want to think about a marketing strategy for a new product" into the input form on their device and clicks the "Submit" button. The output is the user's request data received by the server.

[1131] Step 2:

[1132] The server generates three experts using a generative AI model based on the user's request. The input includes the user's request data. The server sends a prompt to the generative AI model, giving instructions such as "Generate a business strategy expert." The output is the data of the generated experts. Specifically, the server calls the generative AI model and generates the expert characters.

[1133] Step 3:

[1134] The server uses an emotion engine to analyze the user's emotional state. Input includes user request data and past conversation history. The server instructs the emotion engine to "analyze the user's emotions from this text." The output is data indicating the user's emotional state. Specifically, the server invokes the emotion engine and determines the user's emotions as "anxiety," "joy," etc.

[1135] Step 4:

[1136] The generated expert provides advice to the user based on feedback from the emotion engine. Input includes expert data and user emotional state data. The server instructs the expert, "The user is feeling anxious, so please provide advice with words of comfort." Output is the data of the advice provided to the user. Specifically, the expert generates advice such as, "To alleviate anxiety, let's start by conducting market research," and sends it to the user.

[1137] (Application Example 2)

[1138] Next, we will describe application example 2 of form example 2. In the following description, the data processing device 12 will be referred to as a "server," and the headset-type terminal 314 will be referred to as a "terminal."

[1139] In modern e-commerce, users often struggle to make informed decisions about products and have difficulty obtaining appropriate advice. Furthermore, there is a need to provide advice tailored to the user's emotional state, but this is difficult to achieve with conventional systems.

[1140] 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.

[1141] In this invention, the server includes means for generating experts on a specific theme based on user requests, means for the generated experts to provide advice to the user, and means for analyzing the user's emotional state and adjusting the advice based on the analysis results. This makes it possible to provide appropriate advice that responds to the user's emotions when they are unsure about which product to choose.

[1142] "User requests" refer to the wishes and questions that users have about the system.

[1143] A "specialized expert" is a virtual advisor with knowledge in a specific field, generated based on the user's requests.

[1144] A "generated expert" is a virtual advisor created by the system, whose role is to provide advice to the user.

[1145] "Means of providing advice" refers to the methods and processes by which generated experts provide advice and suggestions to users.

[1146] "Means for analyzing a user's emotional state" refers to methods and techniques for analyzing a user's emotions and understanding their emotional state.

[1147] "Means of adjusting advice based on analysis results" refers to methods and processes for changing the content and method of advice provided based on the results of the user's emotion analysis.

[1148] "Means of providing advice regarding product selection" refers to methods and processes for providing advice and suggestions to help users make appropriate choices when selecting products.

[1149] A "generative AI model" is a model that uses artificial intelligence technology to generate data and information tailored to a specific purpose.

[1150] The system for implementing this invention generates experts on specific themes based on user requests and provides advice to the users. The system includes a function to analyze the user's emotional state and adjust the advice based on the analysis results.

[1151] The server uses a generative AI model to generate experts tailored to the user's requests. These experts provide the user with advice on product selection. The user's emotional state is analyzed using a sentiment analysis API (e.g., IBM Watson Tone Analyzer), and the advice is adjusted based on the results.

[1152] A terminal is a device such as a smartphone or tablet that provides an interface for users to access the system. Through the terminal, users can enter requests and receive advice from experts.

[1153] For example, if a user enters a request such as, "I want to buy a new smartphone, but I don't know which one to choose," the server performs sentiment analysis and detects that the user is feeling anxious. Based on this information, the generative AI model generates an expert who provides advice on how to choose a smartphone that is right for the user. The expert might say something like, "Here are some of the latest smartphones that would suit your needs. Please choose with confidence."

[1154] Examples of prompt messages include the following:

[1155] User's request: "I want to buy a new smartphone, but I don't know which one to choose." Emotion: "Anxiety." Prompt to GPT-3: "Generate an expert who can provide advice on choosing a smartphone when the user is feeling anxious."

[1156] The flow of a specific process in Application Example 2 will be explained using Figure 20.

[1157] Step 1:

[1158] The user enters a request through the device. For example, the user might enter a request in text format, such as "I want to buy a new smartphone, but I don't know which one to get." This input becomes the basic data for the next process.

[1159] Step 2:

[1160] The terminal sends the user's request to the server. The server passes the received request to a sentiment analysis API, which analyzes the user's emotional state. The sentiment analysis API parses the request text and outputs an emotion such as "anxiety." This emotional information is used to adjust advice in the next step.

[1161] Step 3:

[1162] The server uses a generative AI model to generate experts based on user requests and emotional information. Specifically, it uses the GPT-3 API to generate prompt statements and determine the expert's character and advice. The generated experts are then ready to provide advice tailored to the user's requests.

[1163] Step 4:

[1164] The server sends the expert advice generated to the device. The device then displays the advice to the user in a chat format. For example, it might say, "Here are some of the latest smartphones that might suit your needs. Please feel free to choose."

[1165] Step 5:

[1166] Users can review the advice displayed on their device and use it as a reference when selecting products. Users can also enter further questions and request advice again if needed.

[1167] (Example 3)

[1168] Next, we will describe Embodiment 3 of Embodiment Example 3. 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."

[1169] Traditional systems could only provide advice from a single perspective in response to user requests, making it difficult to respond appropriately to users' emotions. Furthermore, they lacked the flexibility to respond to diverse user needs, highlighting the need for improved user experience.

[1170] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 3 is realized by the following means.

[1171] In this invention, the server includes means for generating experts on a specific theme based on user requests, means for the generated experts to provide advice to the user, means for analyzing the user's emotions, means for adjusting the advice based on the analyzed emotions, and means for creating prompt sentences using a generation AI model. This enables the provision of advice from diverse perspectives in response to user requests and appropriate responses in response to the user's emotions.

[1172] A "user" is an entity that uses a system to input requests and receive advice and ideas.

[1173] A "request" is information that indicates the wishes or needs that a user inputs into the system.

[1174] An "expert" is a virtual entity created using a generative AI model to provide advice and ideas to users on a specific theme.

[1175] "Advice" refers to suggestions and proposals provided by experts based on user requests.

[1176] "Emotions" refer to information that indicates the psychological state of the user, analyzed from their input.

[1177] A "generative AI model" is an algorithm that uses natural language processing techniques to generate expert opinions and prompts.

[1178] A "prompt sentence" is text input into a generative AI model, serving as an instruction to encourage the generation of expert opinions and advice.

[1179] This invention is a system that generates experts and provides advice in response to user requests. The system is composed of three main components: a server, a terminal, and the user.

[1180] The server generates experts using a generative AI model. This generative AI model leverages natural language processing techniques, for example, by using common natural language processing algorithms. The server receives user requests, creates prompts based on them, and inputs them into the generative AI model. An example of a prompt might be, "The user is seeking new business ideas. Please provide advice from a marketing perspective."

[1181] The terminal functions as an interface for receiving requests from the user. The user enters requests through the terminal and receives advice from the server. The terminal displays the advice sent from the server in text format, making it easy for the user to understand.

[1182] Users access the system via a terminal and input their requests. For example, if a user wants to open a new restaurant, they input this into the terminal, and the server generates a suitable expert and provides advice.

[1183] Furthermore, the server uses an emotion engine to analyze the user's emotions. The emotion engine identifies emotions from the user's input, for example, by utilizing a common emotion analysis API. If the user indicates anger, the server generates additional advice to alleviate that emotion.

[1184] In this way, the system provides advice from various perspectives that meet the user's needs and responds appropriately to the user's emotions. The flow of a specific process in Example 3 will be explained using Figure 21.

[1185] Step 1:

[1186] Users access the system through a terminal and enter their requests. For example, they might enter a request such as, "I want new business ideas." The entered request is then sent from the terminal to the server.

[1187] Step 2:

[1188] The server analyzes the user's request and creates a prompt message to input into the generative AI model. For example, it might generate a prompt message such as, "The user is seeking new business ideas. Please provide advice from a marketing perspective." This prompt message is then input into the generative AI model.

[1189] Step 3:

[1190] The server uses a generative AI model to generate experts based on the prompt text. The generative AI model uses natural language processing techniques to generate advice tailored to the user's request. The generated advice is temporarily stored within the server.

[1191] Step 4:

[1192] The server uses an emotion engine to analyze the user's emotions. It sends the user's requests and entered text to the emotion analysis API to identify the user's emotional state. For example, if the user is showing anger, that information is returned to the server.

[1193] Step 5:

[1194] The server adjusts the advice generated based on the sentiment analysis results. Depending on the user's emotions, it modifies the content of the advice or generates additional advice. For example, it might add suggestions to alleviate anger.

[1195] Step 6:

[1196] The server sends the final advice to the terminal. The terminal displays the received advice to the user. The user can review the advice on the terminal screen and decide on their next course of action.

[1197] (Application Example 3)

[1198] Next, we will describe application example 3 of form example 3. 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."

[1199] When users seek new business ideas, there is a challenge in providing appropriate advice and ideas that resonate with their emotions. Furthermore, ideas provided without considering the user's feelings may not meet their needs, potentially leading to decreased user satisfaction.

[1200] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means.

[1201] In this invention, the server includes means for generating experts on a specific theme based on user requests, means for the generated experts to provide advice to the user, means for providing ideas in response to user requests, means for analyzing the user's emotions, means for inputting prompt sentences into a generation AI model based on the analyzed emotions to generate appropriate ideas, and means for presenting the generated ideas to the user. This makes it possible to provide personalized advice and ideas that are tailored to the user's emotions.

[1202] A "user" is an individual or group that uses the system to seek advice or ideas.

[1203] A "request" is information that indicates the specific needs and desires that a user has for the system.

[1204] An "expert" is a virtual entity created based on a specific theme, which provides advice and ideas to users.

[1205] A "generative AI model" is an artificial intelligence technology that generates experts based on user requests and provides appropriate ideas.

[1206] A "prompt message" is text containing instructions or questions that are input into a generative AI model.

[1207] "Emotional analysis methods" are technologies used to analyze a user's emotions and identify their emotional state.

[1208] An "idea" is a new concept or proposal provided in response to user requests.

[1209] "Advice" refers to suggestions and guidance provided based on the user's requests.

[1210] The system for implementing this invention generates experts based on user requests, performs sentiment analysis, and provides appropriate advice and ideas. The system mainly consists of a server and user terminals.

[1211] The server generates experts based on user requests using a generative AI model. These generated experts are responsible for providing advice to the user. Requests from the user's terminal are sent to the server in text format. The server receives the user's request and analyzes the user's emotions using sentiment analysis tools. Machine learning libraries such as TensorFlow can be used for this analysis.

[1212] Based on the analyzed emotions, the server inputs a prompt message into the generative AI model. This prompt message takes into account the user's request and emotional state, and the generative AI model generates appropriate ideas based on this. The generated ideas are then sent to the user's device and presented to them.

[1213] For example, if a user requests "I want new cafe business ideas," the server performs sentiment analysis and detects that the user is feeling stressed. Based on this, it inputs the prompt "Please suggest business ideas that will help the user relax when they are feeling stressed" into the generative AI model. The generative AI model then generates the idea of ​​a cafe that provides a relaxing space and presents it to the user.

[1214] In this way, the system can provide personalized advice and ideas that respond to the user's emotions.

[1215] The flow of the specific processing in Application Example 3 will be explained using Figure 22.

[1216] Step 1:

[1217] The user uses a terminal to input specific requests to the system in text format. The entered requests are sent to the server.

[1218] Step 2:

[1219] The server analyzes the received user request and identifies the user's emotions using sentiment analysis techniques. The input is the user's request text, and the output is the user's emotional state. Natural language processing techniques using TensorFlow are used for sentiment analysis.

[1220] Step 3:

[1221] The server generates prompt sentences to input to the generative AI model based on the analyzed emotional state. The input is the user's request and emotional state, and the output is the prompt sentence. The prompt sentence contains instructions for generating appropriate ideas based on the user's emotions.

[1222] Step 4:

[1223] The server inputs prompt text into a generative AI model and generates ideas in response to the user's request. The input is the prompt text, and the output is the generated idea. The generative AI model generates ideas using technologies such as OpenAI GPT.

[1224] Step 5:

[1225] The server sends the generated ideas to the user's terminal. The user can review the presented ideas on their terminal and provide feedback as needed. The output is the ideas presented to the user.

[1226] (Other examples)

[1227] Since this is the same as the specific processing described in the other embodiments of the first embodiment above, the explanation will be omitted.

[1228] 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.

[1229] The data generation model 58 is a form of so-called generative AI (Artificial Intelligence). One example of the data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> 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.

[1230] Other examples of generative AI include Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) are some examples.

[1231] 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.

[1232] [Fourth Embodiment]

[1233] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1234] 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.

[1235] 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).

[1236] 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.

[1237] 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.

[1238] 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).

[1239] 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.

[1240] 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.

[1241] 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.

[1242] 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.

[1243] 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.

[1244] 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.

[1245] Next, the identification process performed by the identification processing unit 290 of the data processing device 12 will be described.

[1246] "Example of form 1"

[1247] The system of the present invention includes an interface for receiving requests from users. This interface may take the form of a web page or application, for example, and may provide fields for the user to input themes or requests.

[1248] "Example of form 2"

[1249] Upon receiving a request from a user, the system uses GPT to generate three experts aligned with the specific theme. These experts possess different areas of expertise, such as business strategy, marketing, and product development, depending on the user's request.

[1250] "Example of form 3"

[1251] Each generated expert provides advice to the user. This is achieved, for example, by displaying advice to the user in text format. Furthermore, experts offer ideas in response to user requests. This is achieved, for example, if a user is seeking new business ideas, by having each expert offer ideas from a different perspective.

[1252] The following describes the processing flow for each example of the form.

[1253] "Example of form 1"

[1254] Step 1: The user enters a specific theme or request through the system's interface (e.g., a web page or application).

[1255] Step 2: The system receives input from the user and analyzes it.

[1256] Step 3: Based on the analysis results, the system uses GPT to generate three experts on a specific topic.

[1257] "Example of form 2"

[1258] Step 1: After receiving a request from the user, the system uses GPT to generate three experts on the specific topic.

[1259] Step 2: Each generated expert provides advice to the user. This is achieved, for example, by displaying the advice to the user in text format.

[1260] Step 3: Experts also provide ideas in response to user requests. This is achieved, for example, when a user is looking for new business ideas, by having each expert provide ideas from a different perspective.

[1261] "Example of form 3"

[1262] Step 1: The system receives a request from the user and analyzes it.

[1263] Step 2: Based on the analysis results, the system uses GPT to generate three experts on a specific topic.

[1264] Step 3: Each generated expert provides advice to the user. This is achieved, for example, by displaying the advice to the user in text format.

[1265] Step 4: Experts also provide ideas in response to user requests. This is achieved, for example, when a user is looking for new business ideas, by having each expert provide ideas from a different perspective.

[1266] (Example 1)

[1267] Next, we will describe Embodiment 1 of Example Form 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1268] Traditional systems had the problem of being inefficient, as users had to manually search multiple sources to obtain information on a specific topic. Furthermore, there was the challenge of providing appropriate and timely responses to user requests.

[1269] 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.

[1270] In this invention, the server includes means for providing an interface for receiving requests from users, means for converting themes and requests entered by the user into prompt sentences, and means for sending the generated prompt sentences to a generation AI model and receiving a response. This enables users to efficiently obtain information and quickly receive appropriate responses to their requests.

[1271] An "interface" is the means by which a user accesses a system and enters requests, and it can take the form of a web page or an application.

[1272] A "prompt message" is a sentence that converts the theme or request entered by the user into a format suitable for the generating AI model.

[1273] A "generative AI model" is a type of artificial intelligence that uses natural language processing technology to generate responses based on input prompt sentences.

[1274] A "response" is information or suggestions generated by a generative AI model based on a prompt, and provided to the user.

[1275] A "server" is a computer system that receives requests from users, generates prompt messages, and sends them to the AI ​​model.

[1276] In an embodiment of this invention, the system is configured as follows: The server provides an interface for receiving requests from users. This interface is implemented as a web page or application and includes fields for the user to input themes or requests. When a user accesses the interface and enters a specific theme or request, the server receives the input and converts it into a prompt statement.

[1277] The server uses a programming language such as Python to format user input into a format suitable for the generative AI model. This generative AI model uses a general artificial intelligence model that employs natural language processing techniques. Specifically, if the user inputs "Think of a new recipe," the server converts this into the prompt "Please suggest a new recipe."

[1278] The server sends the generated prompt to the generative AI model and receives a response. The generative AI model generates a response based on the prompt and returns it to the server. For example, the generative AI model might suggest a "simple pasta recipe using tomatoes and basil." The server returns this response to the user, who can then view the result on the interface.

[1279] In this way, users can efficiently obtain information and quickly receive appropriate responses to their requests.

[1280] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1281] Step 1:

[1282] The user accesses the interface and inputs themes and requests. The user accesses the interface through a web browser or application and inputs requests such as "come up with a new recipe." This input forms the basis for the next processing step.

[1283] Step 2:

[1284] The server receives user input and converts it into a prompt. The server uses a Python script to format the user's request into a prompt such as "Please suggest a new recipe." This conversion is to process the data into a format that the generative AI model can easily understand.

[1285] Step 3:

[1286] The server sends the generated prompt message to the AI ​​model. The server sends the prompt message to the AI ​​model via the API and waits for a response. In this step, the prompt message is passed to the AI ​​model as input data.

[1287] Step 4:

[1288] The generative AI model generates a response based on the prompt. Using natural language processing techniques, the generative AI model analyzes the prompt and generates a response such as "a simple pasta recipe using tomatoes and basil." This response becomes the output data.

[1289] Step 5:

[1290] The server receives a response from the generated AI model and returns it to the user. The server receives the generated response and displays it to the user through the interface. The user can gain new information and ideas based on this response.

[1291] (Application Example 1)

[1292] Next, we will describe Application Example 1 of Form 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".

[1293] In today's information-saturated age, it is difficult for users to efficiently obtain information related to specific topics. Furthermore, there is a lack of means to provide customized content tailored to user interests, creating a demand for information that meets user needs.

[1294] 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.

[1295] In this invention, the server includes means for generating information related to a specific theme based on a user request, means for providing the generated information to the user, and means for generating related content based on the theme entered by the user. This enables the user to efficiently obtain customized information according to their interests.

[1296] "User requests" refer to instructions or wishes that users input when seeking specific information or services.

[1297] "Specific theme" refers to a particular topic or field that a user is interested in.

[1298] "Means of generating information" refers to methods or devices that create relevant data and content based on user requests.

[1299] "Generated information" refers to a collection of data and content created in response to user requests.

[1300] "Means of generating content" refers to methods or devices for creating new content by combining information related to a specific theme.

[1301] "Means of providing to users" refers to methods and devices for presenting generated information or content to users.

[1302] The system for implementing this invention consists of a user terminal and a server. The user terminal is a device such as a smartphone or computer, and provides an interface for the user to input a specific theme. The server is responsible for receiving requests from the user and generating relevant information and content using a generative AI model. Specifically, the server is built using Python and Flask, and uses OpenAI's GPT-3 as the generative AI model.

[1303] When a user enters a specific theme through their device, that theme is sent to the server. The server then sends the received theme to GPT-3 as a prompt, generating related information and content. The generated information is provided to the user in various formats, such as news articles, video links, and music playlists.

[1304] For example, if a user enters "space exploration," the server uses GPT-3 to generate and provide the user with the latest news articles about space exploration, links to related documentary videos, and space-related music playlists. By using a prompt such as "Please tell me the latest news articles about space exploration," it is possible to efficiently obtain information that meets the user's request.

[1305] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1306] Step 1:

[1307] The user uses a device to input a specific theme. The entered theme reflects the user's interests and concerns and is sent to the server through the device's interface. The input data is in text format and is processed as a request to the server.

[1308] Step 2:

[1309] The server sends the theme received from the user as a prompt to the generative AI model. Specifically, the server processes the request using Python and Flask and sends the prompt to OpenAI's GPT-3 API. The input is the user's theme, and the output is the related information generated by the generative AI model.

[1310] Step 3:

[1311] The GPT-3 generative AI model generates relevant information and content based on the received prompt text. The generated data includes a variety of formats, such as news articles, video links, and music playlists. The input is the prompt text, and the output is the generated content.

[1312] Step 4:

[1313] The server converts the information received from the generated AI model into an appropriate format for the user. Specifically, it converts the generated content into HTML or JSON format and sends it to the user's device. The input is the generated content, and the output is data in a format that can be displayed to the user.

[1314] Step 5:

[1315] The user's device displays and provides data received from the server. Through the displayed information, the user can obtain the latest information related to a specific topic. The input is data from the server, and the output is information visually presented to the user.

[1316] (Example 2)

[1317] Next, we will describe Example 2 of the morphological example. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1318] In today's information society, users are required to quickly obtain diverse expertise. However, obtaining expert opinions on specific topics presents a challenge, requiring considerable time and effort. Furthermore, finding the right expert to meet a user's needs is not easy.

[1319] 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.

[1320] In this invention, the server includes means for analyzing user requests and identifying relevant areas of expertise, means for generating experts corresponding to the identified areas of expertise using a generative AI model, and means for the generated experts to provide advice to the user. This enables users to quickly and efficiently obtain expert opinions on specific topics.

[1321] A "user" is an entity that inputs requests to a system and receives advice from experts.

[1322] A "request" is information that a user enters into the system to ask for information or advice on a specific topic.

[1323] A "specialized area" is a specific field in which an expert possesses knowledge and experience, identified based on the user's requirements.

[1324] A "generative AI model" is an artificial intelligence technology used to generate experts in response to user requests.

[1325] A "specialist" is a virtual character generated by a generative AI model to correspond to a specific area of ​​expertise and to provide advice to the user.

[1326] "Advice" refers to the knowledge and suggestions on a specific topic that a generated expert provides to the user.

[1327] "Feedback" refers to evaluations and additional requests that users make regarding advice from experts, and this information is used to improve the system.

[1328] The following system configurations are possible as embodiments for carrying out this invention.

[1329] The server provides an interface for receiving requests from users. Users enter requests on specific topics through a terminal. For example, they might enter a request such as, "I want to know the market launch strategy for the new product."

[1330] The server analyzes the received request and identifies the relevant areas of expertise. Natural language processing techniques can be used for this analysis. As a result of the analysis, for example, from the keyword "market entry strategy," three areas of expertise are identified: business strategy, marketing, and product development.

[1331] Next, the server uses the Generative AI Model (GPT) to generate experts corresponding to the identified area of ​​expertise. The server creates prompt statements for each expert and inputs them into GPT to generate the expert's character and their expertise. As a concrete example, a prompt statement such as "As a business strategy expert, please provide advice on launching a new product to market" is used.

[1332] The generated expert information is presented to the user via the device. The user can view advice from each expert on the device. For example, advice from a business strategy expert might include information such as, "It is important to conduct a detailed analysis of the target market and clearly define points of differentiation from competitors."

[1333] This system allows users to quickly and efficiently obtain expert opinions on specific topics.

[1334] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1335] Step 1:

[1336] The user enters a request into the system via a terminal. The information entered is related to a specific topic, such as "I want to know the market launch strategy for the new product." The terminal sends this request to the server.

[1337] Step 2:

[1338] The server analyzes requests received from users. Natural language processing techniques are used for the analysis, identifying relevant areas of expertise based on keywords and context contained in the request. For example, the keyword "market entry strategy" might identify three areas of expertise: business strategy, marketing, and product development. The analysis results are output as identified areas of expertise.

[1339] Step 3:

[1340] The server generates experts using the Generative AI Model (GPT) based on identified areas of expertise. The server creates prompt statements for each expert and inputs them into GPT. For example, it might use a prompt statement like, "As a business strategy expert, please provide advice on launching a new product to market." Based on this, GPT generates the expert's character and expertise, and outputs it as expert information.

[1341] Step 4:

[1342] The terminal displays expert information received from the server to the user. The user can view advice from each expert on the terminal. For example, advice from a business strategy expert might include information such as, "It is important to conduct a detailed analysis of the target market and clearly define points of differentiation from competitors."

[1343] Step 5:

[1344] Users can make decisions based on the expert advice presented. They can also ask additional questions as needed to obtain further information. User feedback is sent to the server and used to improve the system.

[1345] (Application Example 2)

[1346] Next, we will describe application example 2 of form 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".

[1347] In today's information society, users are required to quickly and accurately obtain specialized information from multiple perspectives on specific subjects that interest them. However, conventional methods make it difficult to efficiently provide users with the information they need, and there is a particular challenge in obtaining information from different perspectives at once.

[1348] 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.

[1349] In this invention, the server includes means for generating experts on a specific subject based on user requests, means for the generated experts to provide advice to the user, and means for providing ideas in response to user requests. This makes it possible for users to quickly obtain expert information on subjects of interest from multiple perspectives.

[1350] A "user" is an individual or group that attempts to obtain information using the system.

[1351] A "request" is the act or content of a user asking a system for specific information or services.

[1352] A "subject" is a specific topic or theme that a user is interested in and wants to learn more about.

[1353] An "expert" is a virtual entity that possesses advanced knowledge and experience in a specific subject and provides advice and information to users.

[1354] "Advice" refers to the knowledge and suggestions that experts provide to users, supporting their decision-making.

[1355] "Invention" refers to new ideas or solutions generated based on user requirements.

[1356] A "perspective" is an expert's unique viewpoint or approach to a particular subject.

[1357] A "generative AI model" is an artificial intelligence technology used to generate experts based on user requests.

[1358] The system for carrying out this invention generates experts related to a specific subject based on user requests and provides the user with information from multiple perspectives. The system includes a terminal such as a smartphone and a server that runs the generated AI model.

[1359] Upon receiving a request from a user, the server uses a generative AI model to generate three experts on a specific topic. These experts offer advice and insights from different perspectives. The generated information is then displayed to the user via their device.

[1360] This system utilizes generative AI models such as the OpenAI GPT API. The device is developed using mobile app development frameworks such as React Native. When a user inputs a topic of interest, experts related to that topic are generated, each providing information from a different perspective.

[1361] For example, if a user enters "sustainable energy" as the topic, the server will generate "business strategy experts on sustainable energy," "marketing experts," and "technology development experts," and provide information from each perspective. An example of a prompt would be, "Please generate experts in business strategy, marketing, and technology development on sustainable energy."

[1362] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1363] Step 1:

[1364] The user uses their device to enter a subject they are interested in. The entered subject is then sent to the server as text data by the application on the device.

[1365] Step 2:

[1366] The server inputs the received subject as a prompt into the AI ​​model. Specifically, it generates the prompt in the format "Generate experts in business strategy, marketing, and technology development related to the entered subject."

[1367] Step 3:

[1368] The generative AI model generates three experts based on the prompt text. Each expert is configured by the generative AI model to have a different knowledge domain in order to provide information from a different perspective.

[1369] Step 4:

[1370] The server receives the information generated by the experts and processes it into a format that is easy for the user to understand. This processed information is then sent to the terminal as text data.

[1371] Step 5:

[1372] The terminal displays information received from the server to the user. Based on the displayed information, the user can gain a multifaceted perspective on a subject of interest.

[1373] (Example 3)

[1374] Next, we will describe Embodiment 3 of Example 3. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1375] In today's information society, users are required to quickly obtain diverse information and ideas. However, traditional methods make it difficult to find individuals with specific expertise and obtain appropriate advice and ideas. Therefore, there is a need to develop a system that can efficiently provide users with the information they require.

[1376] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 3 is realized by the following means.

[1377] In this invention, the server includes means for generating a virtual expert with specialized knowledge related to a specific subject based on input information from the user, means for the generated virtual expert to provide information to the user, and means for providing new concepts in relation to the input information from the user. This enables the user to quickly and efficiently obtain the necessary information and ideas.

[1378] An "information processing device" is a device that has the function of receiving, processing, and outputting data, and includes devices such as computers and servers.

[1379] "User" refers to an individual or group that operates an information processing device and seeks information or ideas.

[1380] "Input information" refers to the data and requests that a user provides to an information processing device, and includes formats such as prompt messages.

[1381] A "specific subject" refers to a particular field or theme of interest to the user, and serves as a standard for information processing equipment to provide information related to that subject.

[1382] A "virtual expert" refers to a virtual entity possessing specialized knowledge related to a specific subject, generated by an information processing device using a generative AI model.

[1383] "Means of providing information" refers to the methods and processes for transmitting information generated by virtual experts to users.

[1384] A "new concept" refers to novel ideas or perspectives generated by a virtual expert based on user input.

[1385] This invention is a system that uses an information processing device to generate a virtual expert related to a specific subject based on input information from a user, and provides the user with information and new concepts.

[1386] The server generates virtual experts using a generative AI model. This generative AI model includes models that utilize natural language processing techniques. Specifically, it leverages widely known natural language processing technologies. The server analyzes the prompt sentences received from the user and generates appropriate virtual experts.

[1387] The terminal's role is to send prompt messages entered by the user to the server. These prompt messages specifically indicate the information or ideas the user is seeking. For example, a user might enter a prompt message such as, "Please tell me how to conduct market research for a new product."

[1388] The server receives information from the generated virtual experts and sends it to the terminal. The terminal displays the received information to the user. This allows the user to quickly and efficiently obtain the necessary information and ideas.

[1389] This system enables users to quickly obtain diverse information and ideas, and eliminates the effort required to find personnel with specific expertise. The flow of the specific processing in Example 3 will be explained using Figure 15.

[1390] Step 1:

[1391] The user enters a prompt message through the terminal. This prompt message specifically indicates the information or idea the user is seeking. For example, the user might enter a prompt message such as, "Please tell me how to conduct market research for a new product." This input forms the basis for the next process.

[1392] Step 2:

[1393] The terminal sends the prompt text entered by the user to the server. Here, the input is the prompt text, and the output is the data sent to the server. The terminal sends this data to the server via the internet.

[1394] Step 3:

[1395] The server generates virtual experts using a generative AI model based on the received prompt text. The input is the prompt text, and the output is the generated virtual expert. The server analyzes the prompt text using natural language processing techniques and generates virtual experts with appropriate expertise.

[1396] Step 4:

[1397] The server receives information from the generated virtual experts. The input is the results generated by the virtual experts, and the output is the information provided to the user. The virtual experts generate information and ideas in text format based on prompt statements.

[1398] Step 5:

[1399] The server transmits information received from the virtual expert to the terminal. The input is the information from the virtual expert, and the output is the data to be sent to the terminal. The server transmits this data to the terminal via the internet.

[1400] Step 6:

[1401] The terminal displays information received from the server to the user. The input is information from the server, and the output is what is displayed to the user. This allows the user to review information and ideas from virtual experts.

[1402] (Application Example 3)

[1403] Next, we will describe application example 3 of form example 3. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1404] Modern consumers face the challenge of choosing products from a multitude of options, making it difficult to determine which product is best suited to their needs. Furthermore, while access to expert advice from different perspectives would facilitate better purchasing decisions, there is a lack of readily available means to obtain such information.

[1405] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means.

[1406] In this invention, the server includes means for generating experts on a specific topic based on user requests, means for the generated experts to provide advice to the user, and means for providing ideas in response to user requests. This makes it possible for users to easily obtain expert advice from different perspectives.

[1407] A "user" refers to a consumer who uses the system to select products or seek advice.

[1408] A "request" refers to a request made by a user to the system for information or advice.

[1409] A "specific theme" refers to a topic or field that serves as a basis for experts to provide advice, based on user requests.

[1410] A "specialist" refers to a virtual advisor with knowledge on a specific topic, generated using a generative AI model in response to user requests.

[1411] "Generative AI models" refer to artificial intelligence technology used to generate experts based on user requests and provide advice.

[1412] A "prompt sentence" is text data input into a generative AI model, and refers to a sentence containing instructions or information for experts to generate advice.

[1413] "Advice" refers to suggestions and advice provided by experts in response to user requests.

[1414] "Perspective" refers to a specific viewpoint or stance on which an expert provides advice.

[1415] "Text format" refers to the format of the text information displayed to the user when the generated advice is presented.

[1416] One embodiment of this invention is a system that provides users with access to expert advice when selecting products. The system generates experts on specific topics based on the user's requests, and these experts provide advice to the user.

[1417] The server processes user requests using a generative AI model and generates prompt messages. These prompt messages contain instructions for experts to generate advice. The generative AI model used is one that leverages natural language processing techniques. Specifically, software such as TensorFlow or Hugging Face Transformers can be used.

[1418] The user's device is a smartphone or similar, and it displays advice sent from the server in text format. This allows users to easily obtain expert advice from different perspectives.

[1419] For example, if a user enters the request "I want to buy a new smartphone," the server will generate a prompt message such as "Please provide expert advice on the product the user is considering purchasing. The product name is 'Latest Smartphone'." Based on this prompt message, the AI ​​model generates advice, which is then displayed on the user's device.

[1420] The flow of the specific processing in Application Example 3 will be explained using Figure 16.

[1421] Step 1:

[1422] The user uses a terminal to enter a request regarding product selection. The entered request includes information such as product name and category. The terminal sends this request to the server.

[1423] Step 2:

[1424] The server analyzes the received user request and generates a prompt sentence aligned with a specific theme. This prompt sentence contains instructions that are input into the generation AI model. As part of the data processing, the user request is analyzed using natural language processing techniques to construct an appropriate prompt sentence.

[1425] Step 3:

[1426] The server processes prompt text using a generative AI model and generates expert advice. It receives prompt text as input, and the generative AI model outputs advice using natural language generation technology.

[1427] Step 4:

[1428] The server sends the generated advice to the user's terminal in text format. The outputted advice is then formatted in a way that is easy for the user to understand.

[1429] Step 5:

[1430] The user's device displays the received advice on the screen. Users can review expert advice from different perspectives and use it as a reference when choosing products.

[1431] 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.

[1432] "Example of form 1"

[1433] One embodiment of the present invention incorporates an emotion engine that recognizes the user's emotions into a system that includes means for generating experts on a specific theme based on user requests, means for the generated experts to provide advice to the user, and means for providing ideas in response to user requests. This emotion engine analyzes the user's emotions from facial expressions, tone of voice, text input, etc., and feeds the results back to the system. For example, if the user is showing emotion of joy, the emotion engine conveys that information to the system, and the system generates an expert related to joy.

[1434] "Example of form 2"

[1435] Furthermore, the generated experts provide advice to the user based on feedback from the emotion engine. For example, if the user is expressing sadness, the generated expert will offer words of comfort, providing advice tailored to the user's emotions.

[1436] "Example of form 3"

[1437] Furthermore, when providing ideas in response to user requests, feedback from the emotion engine is also taken into consideration. For example, if a user expresses anger, the generated expert will offer ideas to alleviate that anger. This makes it possible to provide more appropriate ideas that respond to the user's emotions.

[1438] The following describes the processing flow for each example of the form.

[1439] "Example of form 1"

[1440] Step 1: Receive a request from the user. This request seeks advice or ideas on a specific topic.

[1441] Step 2: Analyze the user's emotions using the emotion engine. The analysis is performed based on the user's facial expressions, tone of voice, text input, etc.

[1442] Step 3: Based on the analysis results of the emotion engine, the system generates experts aligned with a specific theme. For example, if the user indicates an emotion of joy, the system will generate experts related to joy.

[1443] "Example of form 2"

[1444] Step 1: The generated expert provides advice to the user based on feedback from the emotion engine. For example, if the user is expressing sadness, the generated expert will provide comforting words and other advice tailored to the user's emotions.

[1445] "Example of form 3"

[1446] Step 1: When providing ideas in response to user requests, feedback from the emotion engine is also taken into consideration. For example, if a user expresses anger, the generated expert will provide ideas to resolve that anger. This makes it possible to provide more appropriate ideas that respond to the user's emotions.

[1447] (Example 1)

[1448] Next, we will describe Embodiment 1 of Example Form 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1449] Traditional systems can generate appropriate experts and provide advice in response to user requests, but they have a challenge in providing advice and ideas that take user emotions into consideration. Furthermore, while flexible responses tailored to the user's emotional state are required, there has been a lack of effective means to achieve this.

[1450] 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.

[1451] In this invention, the server includes means for providing an interface for receiving requests from users, means for generating experts on specific themes based on user requests, and means for recognizing the user's emotions and feeding the results back to the system. This makes it possible to provide advice and ideas that are tailored to the user's emotional state.

[1452] An "interface" is the means by which a user accesses a system and enters requests, and it can take the form of a web page or an application.

[1453] An "expert" is a virtual entity created based on user requests, possessing knowledge on a specific topic and providing advice.

[1454] A "generative AI model" is an artificial intelligence technology used to generate experts in response to user requests, and it includes natural language processing.

[1455] An "emotion engine" is a technology that analyzes a user's facial expressions, tone of voice, text input, etc., to recognize the user's emotional state.

[1456] "Advice" refers to the information and suggestions that the generated expert provides to the user, and includes content that is tailored to the user's needs.

[1457] An "idea" is a new concept or proposal offered in response to user requests, and is generated according to user needs.

[1458] This invention begins with a user accessing the system through a webpage or application and entering a theme or request. For example, the user might enter a request such as "I want to learn new cooking recipes."

[1459] The server receives requests from users and analyzes their content using natural language processing techniques. This analysis extracts information necessary to generate appropriate experts based on the requests. A generative AI model is used to generate experts. This model generates virtual experts with knowledge on specific topics in response to user requests.

[1460] Furthermore, the server uses an emotion engine to recognize the user's emotions. The emotion engine analyzes the user's facial expressions, tone of voice, and text input to identify the user's emotional state. This information is fed back into the system to help provide advice and ideas tailored to the user's emotional state.

[1461] The device provides users with advice and ideas through a generated expert. For example, if a user enters "I want to know how to reduce stress," the server generates a stress management expert who, taking into account the user's emotional state, suggests relaxation techniques and stress relief methods.

[1462] An example of a prompt message might be, "I've been feeling tired lately. Please give me some advice on how to relax." In this way, users can obtain information and advice tailored to their needs.

[1463] The flow of the specific processing in Example 1 will be explained using Figure 17.

[1464] Step 1:

[1465] Users input themes and requests through the interface of a web page or application. The entered requests are sent to the server as text data. For example, a user might input "I want to know new cooking recipes."

[1466] Step 2:

[1467] The server analyzes the request received from the user. Using natural language processing techniques, it understands the content of the request and extracts information to generate the appropriate expert. This analysis identifies keywords and themes related to the request. As output, the analysis results are passed to the generating AI model.

[1468] Step 3:

[1469] The server uses a generative AI model to generate experts based on user requests. The model takes analysis results as input and generates virtual experts with knowledge on specific topics. As output, a profile of the generated expert is created.

[1470] Step 4:

[1471] The server uses an emotion engine to recognize the user's emotions. It analyzes the user's facial expressions, tone of voice, and text input to identify the user's emotional state. User interaction data is used as input, and the output is an evaluation of the emotional state.

[1472] Step 5:

[1473] The device provides the user with advice and ideas through a generated expert. The content and tone of the advice are adjusted according to the user's emotional state. For example, if the user wants to relax, it will suggest a simple, relaxing cooking recipe. The output presents specific advice and ideas for the user.

[1474] (Application Example 1)

[1475] Next, we will describe Application Example 1 of Form 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".

[1476] In today's information society, users find it difficult to find relevant information from the vast amount of data available. Furthermore, there is a demand for personalized information tailored to users' emotions and circumstances, but conventional systems fail to adequately consider user emotions when providing information. Therefore, a system is needed that simultaneously generates experts who meet user needs and provides content based on user emotions.

[1477] 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.

[1478] In this invention, the server includes means for generating experts on a specific theme based on user requests, means for the generated experts to provide advice to the user, and means for analyzing the user's emotions and providing content that corresponds to those emotions. This enables the generation of experts in response to user requests and the provision of personalized content based on the user's emotions.

[1479] "User requests" refer to the wishes and needs that users input into the system.

[1480] A "specialized expert" is a virtual advisor with knowledge and experience in a specific field, generated based on the user's requests.

[1481] "Means of providing advice" refers to functions that allow generated experts to offer advice and suggestions to users.

[1482] "Means of providing ideas" refers to functions that present new ideas and solutions in response to user requests.

[1483] An "emotional engine for analyzing emotions" is a system that reads and analyzes emotions from a user's facial expressions, tone of voice, and other factors.

[1484] A "generative AI model" refers to an algorithm or program that uses artificial intelligence technology to generate experts and content.

[1485] A "prompt message" is text containing instructions or questions that are input into a generative AI model.

[1486] "Means of providing content" refers to functions that provide appropriate information and entertainment in response to the user's emotions and needs.

[1487] The system for implementing this invention generates experts based on user requests and provides content tailored to the user's emotions. The system consists of the user's terminal and a server.

[1488] The user's device is a smartphone or tablet equipped with a camera and microphone. This allows for the capture of the user's facial expressions and voice tone, collecting data for sentiment analysis. For sentiment analysis, Google Cloud's "Cloud Natural Language API" or Microsoft Azure's "Emotion API" can be used.

[1489] The server receives requests from users and uses a generative AI model to generate experts on specific topics. OpenAI's "GPT-3" and "ChatGPT" can be used as generative AI models. The generated experts provide advice to users and deliver personalized content based on the user's emotions.

[1490] For example, if a user enters "travel" as a theme and expresses a desire to relax, the server will suggest travel destinations and activities related to relaxation. An example of a prompt message would be, "The user wants to relax. Please suggest relaxing travel activities."

[1491] In this way, it becomes possible to generate experts and provide content that meets the user's needs and emotions, thereby improving the user experience.

[1492] The flow of a specific process in Application Example 1 will be explained using Figure 18.

[1493] Step 1:

[1494] The user uses a terminal to enter a request related to a specific theme. The entered request is sent to the server as text data.

[1495] Step 2:

[1496] The device's camera and microphone are used to capture the user's facial expressions and voice tone. This data is then sent to a server for sentiment analysis.

[1497] Step 3:

[1498] The server performs emotion analysis using the received user's facial expression and voice tone data. For emotion analysis, it uses Google Cloud's "Cloud Natural Language API" or Microsoft Azure's "Emotion API." The analysis results output the user's emotional state.

[1499] Step 4:

[1500] The server inputs prompt statements into the generative AI model based on the user's requests and emotional state. These prompt statements contain instructions for generating an expert that responds to the user's requests and emotions.

[1501] Step 5:

[1502] The generative AI model takes a prompt as input and generates an expert on a specific topic. The generated expert includes information to provide advice to the user.

[1503] Step 6:

[1504] The server provides the user with expert advice that has been generated. The advice is sent to the device as personalized content based on the user's emotions.

[1505] Step 7:

[1506] Users receive and view advice and content provided through their devices. This ensures that information is provided that meets the user's needs.

[1507] (Example 2)

[1508] Next, we will describe Example 2 of the morphological example. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1509] While conventional systems could provide expert advice in response to user requests, they struggled to provide appropriate advice that took into account the user's emotional state. Therefore, there is a need for a system that can provide advice that considers the user's feelings.

[1510] 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.

[1511] In this invention, the server includes means for generating experts on a specific topic based on user requests, means for the generated experts to provide advice to the user, and means for analyzing the user's emotional state. This makes it possible to provide advice that is adapted to the user's emotions.

[1512] A "user" is the entity that inputs requests into the system and receives advice from experts.

[1513] A "request" is information that a user enters into the system to ask for information or advice on a specific topic.

[1514] An "expert" is a virtual entity created based on user requests, possessing knowledge on a specific topic and providing advice to the user.

[1515] A "generative AI model" is an artificial intelligence technology used to generate experts based on user requests.

[1516] "Emotional state" refers to the emotional state a user expresses towards the system, and is analyzed by the system to provide appropriate advice to the user.

[1517] "Advice" refers to suggestions and recommendations provided by a generated expert to the user, which are tailored based on the user's needs and emotional state.

[1518] A description of embodiments for carrying out this invention will be given.

[1519] The user inputs a request on a specific topic via a terminal and sends it to the server. The server uses a generative AI model to generate three experts who respond to the user's request. This generative AI model utilizes natural language processing technology. Each of the generated experts has a different area of ​​expertise and provides appropriate advice to the user's request.

[1520] Furthermore, the server uses an emotion engine to analyze the user's emotional state. The emotion engine determines the user's emotions based on the user's input text and past conversation history. Based on this analysis, the generated expert provides advice tailored to the user's emotions.

[1521] For example, if a user requests to "develop a marketing strategy for a new product," the server uses a generative AI model to generate experts in marketing, business strategy, and product development. If the emotion engine determines the user's emotion is "anxiety," the expert will provide advice such as, "To alleviate your anxiety, let's start by conducting market research and developing a data-driven strategy."

[1522] An example of a prompt message would be, "I want to develop a marketing strategy for a new product. I would like expert advice."

[1523] The flow of the specific processing in Example 2 will be explained using Figure 19.

[1524] Step 1:

[1525] The user enters a request on a specific topic via their device and sends it to the server. The input includes the user's request. For example, the user enters "I want to think about a marketing strategy for a new product" into the input form on their device and clicks the "Submit" button. The output is the user's request data received by the server.

[1526] Step 2:

[1527] The server generates three experts using a generative AI model based on the user's request. The input includes the user's request data. The server sends a prompt to the generative AI model, giving instructions such as "Generate a business strategy expert." The output is the data of the generated experts. Specifically, the server calls the generative AI model and generates the expert characters.

[1528] Step 3:

[1529] The server uses an emotion engine to analyze the user's emotional state. Input includes user request data and past conversation history. The server instructs the emotion engine to "analyze the user's emotions from this text." The output is data indicating the user's emotional state. Specifically, the server invokes the emotion engine and determines the user's emotions as "anxiety," "joy," etc.

[1530] Step 4:

[1531] The generated expert provides advice to the user based on feedback from the emotion engine. Input includes expert data and user emotional state data. The server instructs the expert, "The user is feeling anxious, so please provide advice with words of comfort." Output is the data of the advice provided to the user. Specifically, the expert generates advice such as, "To alleviate anxiety, let's start by conducting market research," and sends it to the user.

[1532] (Application Example 2)

[1533] Next, we will describe application example 2 of form 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".

[1534] In modern e-commerce, users often struggle to make informed decisions about products and have difficulty obtaining appropriate advice. Furthermore, there is a need to provide advice tailored to the user's emotional state, but this is difficult to achieve with conventional systems.

[1535] 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.

[1536] In this invention, the server includes means for generating experts on a specific theme based on user requests, means for the generated experts to provide advice to the user, and means for analyzing the user's emotional state and adjusting the advice based on the analysis results. This makes it possible to provide appropriate advice that responds to the user's emotions when they are unsure about which product to choose.

[1537] "User requests" refer to the wishes and questions that users have about the system.

[1538] A "specialized expert" is a virtual advisor with knowledge in a specific field, generated based on the user's requests.

[1539] A "generated expert" is a virtual advisor created by the system, whose role is to provide advice to the user.

[1540] "Means of providing advice" refers to the methods and processes by which generated experts provide advice and suggestions to users.

[1541] "Means for analyzing a user's emotional state" refers to methods and techniques for analyzing a user's emotions and understanding their emotional state.

[1542] "Means of adjusting advice based on analysis results" refers to methods and processes for changing the content and method of advice provided based on the results of the user's emotion analysis.

[1543] "Means of providing advice regarding product selection" refers to methods and processes for providing advice and suggestions to help users make appropriate choices when selecting products.

[1544] A "generative AI model" is a model that uses artificial intelligence technology to generate data and information tailored to a specific purpose.

[1545] The system for implementing this invention generates experts on specific themes based on user requests and provides advice to the users. The system includes a function to analyze the user's emotional state and adjust the advice based on the analysis results.

[1546] The server uses a generative AI model to generate experts tailored to the user's requests. These experts provide the user with advice on product selection. The user's emotional state is analyzed using a sentiment analysis API (e.g., IBM Watson Tone Analyzer), and the advice is adjusted based on the results.

[1547] A terminal is a device such as a smartphone or tablet that provides an interface for users to access the system. Through the terminal, users can enter requests and receive advice from experts.

[1548] For example, if a user enters a request such as, "I want to buy a new smartphone, but I don't know which one to choose," the server performs sentiment analysis and detects that the user is feeling anxious. Based on this information, the generative AI model generates an expert who provides advice on how to choose a smartphone that is right for the user. The expert might say something like, "Here are some of the latest smartphones that would suit your needs. Please choose with confidence."

[1549] Examples of prompt messages include the following:

[1550] User's request: "I want to buy a new smartphone, but I don't know which one to choose." Emotion: "Anxiety." Prompt to GPT-3: "Generate an expert who can provide advice on choosing a smartphone when the user is feeling anxious."

[1551] The flow of a specific process in Application Example 2 will be explained using Figure 20.

[1552] Step 1:

[1553] The user enters a request through the device. For example, the user might enter a request in text format, such as "I want to buy a new smartphone, but I don't know which one to get." This input becomes the basic data for the next process.

[1554] Step 2:

[1555] The terminal sends the user's request to the server. The server passes the received request to a sentiment analysis API, which analyzes the user's emotional state. The sentiment analysis API parses the request text and outputs an emotion such as "anxiety." This emotional information is used to adjust advice in the next step.

[1556] Step 3:

[1557] The server uses a generative AI model to generate experts based on user requests and emotional information. Specifically, it uses the GPT-3 API to generate prompt statements and determine the expert's character and a...

Claims

[Claim 1] A system comprising a processor, The aforementioned processor, A means for receiving the user's request regarding a specific theme as text data via an interface for receiving user requests, A means for analyzing the user's emotional state using an emotion engine based on data of the user's facial expressions and voice tone captured using the camera and microphone of the user's terminal, A means for analyzing the received request and, based on the analyzed request and emotional state, dynamically generating a prompt statement for input to a generative AI model, which instructs the model to generate an expert related to the specific theme; A means for inputting the generated prompt sentence into the generating AI model to generate an expert in line with the specific theme, obtaining a response from the generating AI model that includes advice or ideas provided by the generated expert, and analyzing the obtained response to extract the advice or ideas, Means for outputting the extracted advice or ideas to the user, Includes, A system for generating the prompt statement, wherein if the analyzed emotional state is anger, the prompt statement is generated so that the expert can provide the idea for resolving the anger, and if the analyzed emotional state is sadness, the prompt statement is generated so that the expert can provide the advice, including words of comfort.

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