system

Abstract

We provide the system. [Solution] A means for receiving the inquiry content entered by the user, A generative artificial intelligence means for analyzing the content of received inquiries, A generative artificial intelligence means that generates an appropriate answer based on the analysis results, A means of sending the generated response to the user's terminal, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Modern enterprises are required to respond to customer inquiries quickly and accurately. However, conventional methods rely heavily on manual labor, making it difficult to respond efficiently and time-consuming. This may lead to a decrease in customer satisfaction and affect the company's revenue. In addition, especially regarding technical inquiries or specific operation procedures, there is also a problem that the quality of responses varies greatly depending on the expertise of the responders.

Means for Solving the Problems

[0005] To solve the above problems, the present invention provides the following means.

[0006] Means for receiving the inquiry content input by the user

[0007] A generative artificial intelligence means for analyzing the content of received inquiries,

[0008] A generative artificial intelligence means that generates an appropriate answer based on the analysis results.

[0009] A means of sending the generated response to the user's terminal.

[0010] By providing a system that includes this feature, it is possible to eliminate the delays and quality in responses that occurred with conventional methods. Furthermore, the present invention can improve user convenience by configuring the user terminal to input and transmit inquiry content via a web interface or application. In addition, by providing a generative artificial intelligence means that generates specific information according to the type of inquiry content based on the analysis results, it becomes possible to provide high-quality responses to technical questions and specific operating procedures.

[0011] A "user" is an individual or legal entity that uses the system to make an inquiry.

[0012] "Inquiry content" refers to questions or information requests that users enter into the system.

[0013] "Means of receiving" refers to the mechanism or method by which the system receives the content of an inquiry sent by a user.

[0014] "Generative artificial intelligence means" refers to artificial intelligence technology that has the function of analyzing natural language and generating appropriate responses.

[0015] "Analyzing" refers to understanding the content of a received inquiry and grasping its meaning and context.

[0016] "Generating appropriate answers" means creating responses to user inquiries based on analysis results.

[0017] The "user terminal" is a device that the user uses to input inquiry content and communicate with the system.

[0018] The "means for transmitting" is a mechanism or method for transmitting the answer generated by the system to the user terminal.

[0019] The "web interface" refers to a web page or web application through which the system and the user interact via the Internet.

[0020] An "application" refers to a software program installed and used by the user, which provides an interface for communicating with the system.

[0021] The "specific information" refers to the specific answer content provided by the generative artificial intelligence according to the inquiry content based on the analysis result.

[0022] "High-quality response" means answering the user's inquiry quickly and accurately, which means a response that improves user satisfaction.

Brief Explanation of Drawings

[0023] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6]This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0024] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0025] First, let's explain the terminology used in the following explanation.

[0026] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), and APU (Accelerated Processing Unit).

[0027] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0028] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0029] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0030] 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."

[0031] [First Embodiment]

[0032] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0033] 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.

[0034] 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).

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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.

[0039] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0040] 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.

[0041] 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.

[0042] 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.

[0043] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0044] This invention relates to a system that efficiently processes user-inputted inquiries using generative artificial intelligence (AI) and provides appropriate answers quickly. This system consists of multiple elements, including a server, a terminal, and generative artificial intelligence.

[0045] 1. User input of inquiry

[0046] First, the user enters their inquiry using a device. This device operates via a web interface or application. For example, the user might enter, "How do I reset my iPhone (registered trademark)?"

[0047] 2. Submit your inquiry.

[0048] The terminal sends the entered query content to the server. The server receives the query content using a communication protocol and prepares to perform appropriate analysis.

[0049] 3. Receiving queries by the server

[0050] The server receives the content of inquiries sent by users and passes that content to a generative artificial intelligence system. The server is required to efficiently process this reception and analysis request process.

[0051] 4. Analysis using generative artificial intelligence

[0052] Next, a generative artificial intelligence analyzes the received inquiry. The AI ​​uses text analysis techniques to identify key keywords and context. For example, it extracts keywords such as "iPhone," "initialization," and "method."

[0053] 5. Generating the answer

[0054] The generative artificial intelligence generates an appropriate answer based on the analysis results. Specifically, for example, it might generate the procedure "How to reset your iPhone: Settings > General > Reset > Erase All Content and Settings".

[0055] 6. Submit your response

[0056] The server receives the response from the generative artificial intelligence and sends the response to the user's terminal. In this process, the server again uses a communication protocol to send the response text.

[0057] 7. Receiving and confirming user responses

[0058] Ultimately, the user's device receives the response from the server and displays it on the user interface. For example, the user can check the initialization procedure on their smartphone or computer screen.

[0059] Specific example

[0060] Specific examples are given below.

[0061] Example 1: A user asks, "How do I use Corporate Concierge?"

[0062] The user enters a question into their device and sends it to the server.

[0063] The server receives the question and requests analysis from a generative artificial intelligence.

[0064] Generative artificial intelligence generates information on "how to use corporate concierge services."

[0065] The server sends the generated information to the user.

[0066] The user's device displays the following instructions: "How to use Corporate Concierge: First, launch the app and log in. Next..."

[0067] In this way, the system of the present invention allows users to obtain the necessary information quickly and accurately. Furthermore, companies can significantly improve the efficiency of responding to inquiries.

[0068] The above describes specific embodiments for carrying out the present invention. This will enable users to easily utilize the system and receive appropriate support.

[0069] The following describes the processing flow.

[0070] Step 1:

[0071] The user enters their inquiry using a device. The device provides a user interface via a web interface or application. For example, the user might type, "How do I reset my iPhone?"

[0072] Step 2:

[0073] The terminal sends the entered query content to the server. The terminal uses a communication protocol (e.g., HTTP POST request) to send the query content to the server. At this point, the query content arrives at the server in text format.

[0074] Step 3:

[0075] The server receives the query content sent by the user. The server receives the request through the API endpoint and passes the query content to its internal processing.

[0076] Step 4:

[0077] The server requests a generative artificial intelligence (AI) to analyze the received inquiry. The server calls the generative AI's API and sends the inquiry text to analyze the inquiry.

[0078] Step 5:

[0079] The generative AI analyzes the received inquiry. Using natural language processing techniques, the generative AI analyzes key keywords and context to understand the intent of the inquiry. For example, it extracts keywords such as "iPhone," "initialization," and "method."

[0080] Step 6:

[0081] The generative AI generates appropriate answers based on the analysis results. The AI ​​generates specific operating procedures and information in text format according to the content of the inquiry. For example, it might generate the procedure: "How to reset your iPhone: Settings > General > Reset > Erase All Content and Settings".

[0082] Step 7:

[0083] The server receives the response text from the generative AI. The server receives the generated response text and prepares to send it to the user's terminal.

[0084] Step 8:

[0085] The server sends the generated response to the user's device. The server then uses the communication protocol again to send the response text to the user's device.

[0086] Step 9:

[0087] The user's device receives a response from the server. The device displays the received response text on the user interface. For example, the initialization procedure is displayed on the user's smartphone or computer screen.

[0088] Step 10:

[0089] The user checks the response displayed on the device and performs the necessary actions according to the instructions. For example, the user may perform a factory reset of their iPhone by following the displayed instructions.

[0090] As described above, the system of the present invention responds quickly and accurately to user inquiries, improving user convenience. This allows companies to significantly improve the efficiency of their inquiry handling.

[0091] (Example 1)

[0092] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0093] Many modern systems suffer from delays in providing responses to user inquiries and incur significant manual workloads. There is a need to solve this problem and create a system that can respond to user inquiries quickly and accurately.

[0094] 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.

[0095] In this invention, the server includes means for receiving the content of an inquiry entered by the user, means for passing the received content of the inquiry to a generative artificial intelligence, means for the generative artificial intelligence to analyze the received content and identify key keywords and context, means for the generative artificial intelligence to generate an appropriate answer based on the analysis results, means for sending the generated answer to the user terminal, and means for the user terminal to receive the answer from the server and display it on the user interface. This enables the user to obtain the necessary information quickly and accurately.

[0096] A "user" refers to an end-user who enters an inquiry into the system and receives a response.

[0097] A "terminal" refers to a device such as a computer or smartphone that a user uses to input their inquiry and receive a response from a server.

[0098] A "server" refers to a computing system that receives user inquiries, forwards them to a generative artificial intelligence system, and generates and sends responses.

[0099] "Generative artificial intelligence" refers to an AI model equipped with natural language processing that analyzes user inquiries and generates appropriate responses.

[0100] "Means of receiving" refers to the function that allows the server to receive the content of inquiries sent by the user.

[0101] "Means of transferring to generative artificial intelligence" refers to the function that allows the server to transfer the content of the inquiry it receives to the generative artificial intelligence.

[0102] "Means of analysis" refers to the functions that generative artificial intelligence uses to analyze the content of inquiries and identify key keywords and context.

[0103] "Means for generating answers" refers to the function that allows generative artificial intelligence to come up with appropriate answers based on analysis results.

[0104] "Means of transmission" refers to the function that allows the server to send the generated response to the user's terminal.

[0105] "User interface" refers to the screen display or application interface that users use to input inquiries or view received responses.

[0106] This invention relates to a system that efficiently processes user-inputted inquiries using generative artificial intelligence and provides appropriate answers quickly. This system consists of multiple elements, including a server, a terminal, and generative artificial intelligence.

[0107] First, the user enters their inquiry using a device. This device operates via a web interface or application. For example, the user might enter "How do I reset my iPhone?". This inquiry is sent from the device to the server using the HTTP / HTTPS communication protocol.

[0108] Next, the server receives the query content sent by the user. The server analyzes the received query content and prepares it for passing on to the generative artificial intelligence described later. The receiving terminal can also temporarily store the received content in a database.

[0109] The server then passes the query to a generative artificial intelligence (AI). The AI ​​uses NLP (Natural Language Processing) techniques to analyze the query. This analysis process identifies key keywords and context, and generates an appropriate response based on them. For example, it might extract keywords such as "iPhone," "initialization," and "method."

[0110] The generative artificial intelligence generates an appropriate answer based on the analysis results. Specifically, it generates the procedure "How to reset your iPhone: Settings > General > Reset > Erase All Content and Settings". This generation process is performed using a generative artificial intelligence model and prompt statements.

[0111] The generated response is sent back to the server, which then transmits the response data to the user's device. The communication protocol used is again HTTP / HTTPS. This data transfer process is required to be secure and fast.

[0112] Finally, the user's device receives the response sent from the server and displays it on the user interface. The user can then review the initialization procedure on their smartphone or computer screen.

[0113] As a concrete example, let's consider a scenario where a user asks, "How do I use Corporate Concierge?" In this case, the user types "How do I use Corporate Concierge?" into their device and sends it to the server. The server receives the question and requests analysis from a generative artificial intelligence. The generative AI generates information about "how to use Corporate Concierge," and the server sends this information to the user. Then, the user's device displays instructions such as "How to use Corporate Concierge: First, launch the app and log in. Next..."

[0114] In this way, the system of the present invention allows users to obtain the necessary information quickly and accurately. Furthermore, by using this system, companies can significantly improve the efficiency of responding to inquiries.

[0115] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0116] Step 1: User enters inquiry

[0117] The user enters their inquiry details using a terminal. This terminal operates via a web interface or application.

[0118] Input: The user enters "How do I reset my iPhone?" into the text input box on the device.

[0119] Output: Stored as text data in the terminal's memory.

[0120] Specific action: The user types text using the keyboard and presses the send button.

[0121] Step 2: Submit your inquiry

[0122] The terminal sends the entered query content to the server. The terminal does this using an HTTP POST request.

[0123] Input: Text data entered by the user.

[0124] Output: The HTTP request sent to the server.

[0125] Specific operation: The terminal stores the query text in the body of the HTTP request and sends it to the specified server endpoint.

[0126] Step 3: The server receives the query.

[0127] The server receives the query content sent by the user. The received content is temporarily stored in the server's memory and then saved to the database as needed.

[0128] Input: HTTP request sent from the terminal.

[0129] Output: The query content stored in the server's memory or database.

[0130] Specific operation: The server receives the request, extracts the query content from the request body, and stores it temporarily.

[0131] Step 4: Transferring the received content to the generational artificial intelligence system.

[0132] The server passes the received query details to the generative artificial intelligence. An API request is then made to the generative artificial intelligence.

[0133] Input: The query content stored on the server.

[0134] Output: API request to a generative artificial intelligence system.

[0135] Specific operation: The server converts the query content into an API request format and sends it to the generative artificial intelligence endpoint.

[0136] Step 5: Analysis using generative artificial intelligence

[0137] Generative artificial intelligence analyzes the content of the received inquiry. NLP techniques are used to identify key keywords and context.

[0138] Input: The content of the inquiry sent from the server.

[0139] Output: Keywords and contextual information as analysis results.

[0140] Specific operation: The generative artificial intelligence tokenizes the query content and performs principal component analysis and contextual analysis.

[0141] Step 6: Generating the answer

[0142] Generative artificial intelligence generates appropriate answers based on the analysis results. For example, it might generate the procedure: "How to reset your iPhone: Settings > General > Reset > Erase All Content and Settings".

[0143] Input: Keywords and contextual information as analysis results.

[0144] Output: Answer text.

[0145] Specific operation: Generative artificial intelligence searches for appropriate information from a knowledge base and generates an answer using a text generation algorithm.

[0146] Step 7: Submit your response

[0147] The server receives a response from a generative artificial intelligence and sends it to the user's terminal. HTTP responses are used.

[0148] Input: Text response from a generative artificial intelligence.

[0149] Output: The HTTP response sent to the user's terminal.

[0150] Specific operation: The server stores the response text in the body of the HTTP response and sends it to the user's terminal.

[0151] Step 8: Receiving and confirming the user's response

[0152] The user's device receives the response from the server and displays it on the user interface.

[0153] Input: HTTP response sent from the server.

[0154] Output: The answer displayed on the device's screen.

[0155] Specific operation: The terminal receives an HTTP response, updates the corresponding part of the interface, and displays the answer.

[0156] (Application Example 1)

[0157] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0158] Online content distribution services have faced problems such as users easily becoming confused when trying to find the next content to watch, and taking a long time to get appropriate answers. Therefore, there is a need for a system that can quickly and accurately suggest the most suitable content based on viewing and usage history.

[0159] 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.

[0160] In this invention, the server includes means for receiving inquiry content entered by the user, means for generative artificial intelligence for analyzing the received inquiry content, means for generative artificial intelligence for generating an appropriate response based on the analysis results, means for transmitting the generated response to the user terminal, and means for suggesting the next content to be viewed based on the usage history and viewing history of the content distribution service. This makes it possible for the user to quickly find content to watch.

[0161] "User terminal" refers to devices used by the user, such as computers, smartphones, tablets, smart glasses, head-mounted displays, or robots.

[0162] "Inquiry content" refers to the text information of questions and requests entered by the user through their device.

[0163] "Generative artificial intelligence" refers to an AI model that analyzes user-inputted inquiries and generates appropriate responses.

[0164] "Generated answers" refer to the answers to user questions generated by generative artificial intelligence based on analysis results.

[0165] A "content distribution service" refers to an online service that provides users with digital content such as movies, television programs, documentaries, and animation.

[0166] "Usage history" refers to a record of content that a user has previously viewed or downloaded from a content distribution service.

[0167] "Viewing history" refers to a record of the content that a user has actually viewed using a content distribution service.

[0168] "The suggested methods" refer to a function that uses generative artificial intelligence to analyze a user's usage history and viewing history, and then suggests content that the user should watch next.

[0169] The system implementing this invention consists of multiple elements, including a user terminal, a server, and a generative artificial intelligence system. This system is specifically designed for content distribution services and helps users easily find the next content they should watch.

[0170] First, the user terminal consists of devices such as smartphones, smart glasses, head-mounted displays, or robots. The user enters their inquiry through these terminals. For example, they might enter a question like, "Which movie should I watch next?"

[0171] The entered query content is sent to the server. The server receives this query content and passes it on to a generative artificial intelligence for appropriate analysis. HTTPS is used as the communication protocol by the server to ensure security.

[0172] Generative artificial intelligence analyzes user inquiries based on their viewing and usage history. This analysis extracts key keywords and context from the viewing history and generates responses that suggest the most suitable content. For example, it might generate a response like, "Based on your current viewing history and favorite genres, the recommended movie is 'Inception'."

[0173] The server receives the generated response and sends it to the user's device. The user's device displays the received response on its screen. The user can then review this response on their device and select the next content to view.

[0174] The specific software used will be the OpenAI® API for generative artificial intelligence, and Python for program implementation. The requests module will be used for sending and receiving data, and the HTTPS protocol will be used to ensure secure communication.

[0175] Specific example:

[0176] When a user types "What movie should I watch next?" on their smartphone,

[0177] The generative artificial intelligence analyzes the user's viewing history and generates a response such as, "Based on your current viewing history and favorite genres, the recommended movie is 'Inception'."

[0178] Example of a prompt:

[0179] User: What movie should I watch next?

[0180] Generative AI Model: Based on your current viewing history and favorite genres, the recommended movie is "Inception".

[0181] This allows users to easily discover appropriate content, thus avoiding content fatigue. Using server-side and generative artificial intelligence, users can quickly and accurately find the next content they should watch.

[0182] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0183] Step 1:

[0184] The user enters their inquiry using a terminal. For example, they might enter, "Which movie should I see next?" The input data is retrieved in text format.

[0185] Step 2:

[0186] The terminal sends the entered query content to the server. The data sent is in text format and is transmitted securely using the HTTPS protocol.

[0187] Step 3:

[0188] The server receives the inquiry content sent by the user and prepares to pass that text data to the generative artificial intelligence. The input data here is the received inquiry content in text format.

[0189] Step 4:

[0190] A generative artificial intelligence analyzes the received inquiry content and the user's viewing history data. This analysis extracts keywords from the viewing history and generates a response appropriate to the inquiry. The input is the inquiry content and viewing history data, and the output is the generated response text.

[0191] Step 5:

[0192] The server receives the generated response and sends it to the user's terminal. The transmitted data is the generated response text. The server then uses the HTTPS protocol to send the data again.

[0193] Step 6:

[0194] The user's device receives the response sent from the server and displays it on the user interface. The displayed data is the generated response text. For example, the response "Based on your current viewing history and favorite genres, the recommended movie is 'Inception'" might be displayed on the user's device.

[0195] As described above, the overall processing flow of the system is as follows: User inputs inquiry content → Terminal sends it to the server → Server receives the inquiry and passes it to the generative AI → Generative AI analyzes and generates an answer → Server sends the answer to the user terminal → User terminal displays the answer.

[0196] 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.

[0197] This invention is a system that efficiently analyzes user-inputted inquiries using generative artificial intelligence (AI) and an emotion engine, and generates appropriate responses that reflect the user's emotions. This system consists of elements such as a server, a terminal, a generative AI, and an emotion engine.

[0198] 1. User input of inquiry

[0199] First, the user enters their inquiry using their device. The device provides a user interface via a web interface or application. For example, the user might type, "How do I reset my iPhone?"

[0200] 2. Submit your inquiry.

[0201] The terminal sends the entered query content to the server. The terminal uses a communication protocol (e.g., HTTP POST request) to send the query content to the server. At this point, the query content arrives at the server in text format.

[0202] 3. Receiving queries by the server

[0203] The server receives the query content sent by the user. The server receives the request through the API endpoint and passes the query content to its internal processing.

[0204] 4. Request for emotion recognition by the server

[0205] Next, the server passes the received query to the sentiment engine and asks it to recognize the user's emotions. The sentiment engine analyzes the query text and identifies the user's emotions. For example, the sentiment engine might recognize emotions such as "frustration," "anxiety," or "confusion."

[0206] 5. Analysis using generative artificial intelligence

[0207] The server then passes the emotion recognition results to a generative artificial intelligence (AI) and requests it to analyze the inquiry. The generative AI uses natural language processing techniques to understand the main keywords and context and grasp the intent of the inquiry. For example, it might extract keywords such as "iPhone," "initialization," and "method."

[0208] 6. Generating the answer

[0209] The generative AI generates appropriate responses based on the analysis results and emotion recognition. For example, if the AI ​​detects that the user is confused, it generates a response that includes more detailed and polite instructions. Specifically, it adjusts the tone and style, such as, "How to reset your iPhone: Settings > General > Reset > Erase All Content and Settings. Please note that this operation will erase all your data."

[0210] 7. Submit your response

[0211] The server receives the response text from the generative AI and sends the response to the user's device. In this process, the server again uses a communication protocol to send the response text to the user's device.

[0212] 8. Confirmation of user responses

[0213] The user's device receives the response from the server and displays it on the user interface. For example, the user can check the initialization procedure on their smartphone or computer screen.

[0214] Specific example

[0215] The following will explain this with specific examples.

[0216] Example 1: A user asks, "How do I use Corporate Concierge?", and the emotion engine recognizes the user's frustration.

[0217] The user enters a question into their device and sends it to the server.

[0218] The server receives the question and requests analysis from the emotion engine.

[0219] The emotion engine recognizes the user's "frustration."

[0220] The server sends the emotion recognition results and the question content to the generative AI.

[0221] The generative AI generates detailed and easy-to-understand information on "how to use Corporate Concierge."

[0222] The server sends the generated information to the user.

[0223] The user's device displays the following instructions: "How to use Corporate Concierge: First, launch the app and log in. Next..."

[0224] In this way, the system of the present invention allows users to obtain the necessary information quickly and accurately, as well as receive optimal support tailored to their emotional needs. This enables companies to significantly improve the efficiency of handling inquiries and enhance user satisfaction.

[0225] The following describes the processing flow.

[0226] Step 1:

[0227] The user enters their inquiry using a device. The device provides a user interface via a web interface or application, for example, the user might type, "How do I reset my iPhone?"

[0228] Step 2:

[0229] The terminal sends the entered query content to the server. The terminal uses a communication protocol (e.g., HTTP POST request) to send the query content to the server. At this point, the query content arrives at the server in text format.

[0230] Step 3:

[0231] The server receives the query content sent by the user. The server receives the request through the API endpoint and passes the query content to its internal processing.

[0232] Step 4:

[0233] The server passes the received query content to the sentiment engine for analysis. The sentiment engine uses natural language processing techniques to analyze the query text and recognize the user's emotions. For example, the sentiment engine might identify emotions such as "frustration" or "confusion" from the query text.

[0234] Step 5:

[0235] The emotion engine returns the user's recognized emotions to the server. The server receives these emotion recognition results and prepares to request the next step of processing from the generative artificial intelligence (AI).

[0236] Step 6:

[0237] The server passes the emotion recognition results and the inquiry content to a generative artificial intelligence (AI) system, requesting analysis and response generation. The generative AI analyzes the inquiry content while considering the emotion recognition results. Specifically, it uses automated text analysis technology to understand key keywords and context.

[0238] Step 7:

[0239] Generative artificial intelligence generates appropriate responses based on analysis results and the user's emotions. For example, if the user is perceived as "frustrated," it will provide a more helpful and polite response. Specifically, it will adjust the tone and style of a response such as, "How to reset your iPhone: Settings > General > Reset > Erase All Content and Settings. Please note that this operation will erase all your data."

[0240] Step 8:

[0241] The server receives the response text generated by the generative AI and sends the response to the user's terminal. The server uses a communication protocol to send the response text to the user's terminal.

[0242] Step 9:

[0243] The device receives a response from the server and displays it on the user interface. Specifically, the initialization procedure is displayed on the user's smartphone or computer screen.

[0244] Step 10:

[0245] The user checks the response displayed on the device and performs the necessary actions according to the instructions. For example, the user may perform a factory reset of their iPhone by following the displayed instructions.

[0246] As described above, the system of the present invention can respond to user inquiries quickly and accurately, and further provide optimal support tailored to the user's emotions. This allows companies to significantly improve the efficiency of inquiry handling and increase user satisfaction.

[0247] (Example 2)

[0248] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0249] Traditional inquiry systems often fail to provide satisfactory answers because they analyze only the content of the question without considering the user's emotions. Furthermore, inappropriate answers can exacerbate dissatisfaction, especially for users experiencing frustration or confusion. Therefore, there is a need for a system that provides appropriate and effective answers tailored to the user's emotions.

[0250] The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving inquiry content entered by the user, means for identifying the emotion of the received inquiry content using emotion analysis means, means for passing the emotion information obtained from the emotion analysis means and the inquiry content to a generative artificial intelligence means, means for analyzing the inquiry content using the generative artificial intelligence means and generating an appropriate answer based on the analysis results, and means for transmitting the generated answer to the user terminal. This makes it possible to provide an appropriate and highly satisfying answer that takes the user's emotions into consideration.

[0251] A "user" refers to a person who uses the system to input inquiry details and obtain information.

[0252] "Inquiry content" refers to the text data of questions and requests that users enter through the system.

[0253] A "terminal" refers to a hardware device used by a user to input and submit inquiry details. This includes smartphones, personal computers, tablets, and other similar devices.

[0254] A "server" refers to a central computing system that receives inquiries, analyzes them, performs emotion recognition and generative artificial intelligence processing, and ultimately generates and sends answers to terminals.

[0255] "Emotional analysis means" refers to a technology or program that analyzes the text data of an input inquiry to identify the user's emotional state.

[0256] "Generative artificial intelligence" refers to artificial intelligence that uses natural language processing technology to analyze input inquiries and generate appropriate responses.

[0257] "Analysis results" refer to the understanding of the query content as analyzed by generative artificial intelligence. This includes keyword extraction and contextual understanding.

[0258] "Answer" refers to an appropriate response to a user's inquiry generated by a generative artificial intelligence system.

[0259] This invention is a system that efficiently analyzes user-inputted inquiries using generative artificial intelligence (AI) and an emotion engine, and generates appropriate responses that reflect the user's emotions. The system consists of elements such as a server, a terminal, a generative AI, and an emotion engine.

[0260] First, the user enters their inquiry using their device. The device provides a user interface via a web interface or application. Let's take the example where the user enters "How do I reset my iPhone?"

[0261] Next, the terminal sends the entered query content to the server. The terminal uses a communication protocol such as an HTTP POST request to send the query content to the server. At this point, the query content arrives at the server in text format.

[0262] The server receives the query content sent by the user. The server receives the request through the API endpoint and passes the query content to its internal processing.

[0263] The server passes the received query to the emotion engine and requests it to recognize the user's emotions. The emotion engine analyzes the query text and identifies the user's emotions. For example, the emotion engine might recognize emotions such as "frustration," "anxiety," or "confusion."

[0264] The server then passes the emotion recognition results to a generative artificial intelligence (AI) and requests it to analyze the inquiry. The generative AI uses natural language processing techniques to understand the main keywords and context and grasp the intent of the inquiry. For example, it might extract keywords such as "iPhone," "initialization," and "method."

[0265] Generative AI generates appropriate responses based on analysis results and emotion recognition. For example, if the AI ​​detects that the user is confused, it will generate a response that includes more detailed and polite instructions. Specifically, it will adjust the tone and style, such as, "How to reset your iPhone: Settings > General > Reset > Erase All Content and Settings. Please note that this operation will erase all your data."

[0266] Next, the server receives the response text from the generative AI and sends the response to the user's device. In this process, the server again uses a communication protocol to send the response text to the user's device.

[0267] Finally, the user's device receives the response from the server and displays it on the user interface. For example, the user can check the initialization procedure on their smartphone or computer screen.

[0268] The following will explain this with specific examples.

[0269] Example 1: A user asks, "How do I use Corporate Concierge?", and the emotion engine recognizes the user's frustration.

[0270] The user enters a question into their device and sends it to the server.

[0271] The server receives the question and requests analysis from the emotion engine.

[0272] The emotion engine recognizes the user's "frustration."

[0273] The server sends the emotion recognition results and the question content to the generative AI.

[0274] The generative AI generates detailed and easy-to-understand information on "how to use Corporate Concierge."

[0275] The server sends the generated information to the user.

[0276] The user's device displays the following instructions: "How to use Corporate Concierge: First, launch the app and log in. Next..."

[0277] In this way, the system of the present invention allows users to obtain the necessary information quickly and accurately, as well as receive optimal support tailored to their emotional needs. This enables companies to significantly improve the efficiency of handling inquiries and enhance user satisfaction.

[0278] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0279] Step 1:

[0280] The user enters their inquiry using a device. The device provides a user interface through a web interface or application. Specifically, the user enters "How do I reset my iPhone?" into the text input field on the device and clicks the send button. The input is stored on the device as text data.

[0281] Step 2:

[0282] The device sends the entered query content to the server. The device uses a communication protocol such as an HTTP POST request to send the query content to the server. The data sent is the query content in text format and reaches the server. Specifically, the device sends the text "Please tell me how to reset my iPhone" via an HTTP POST request.

[0283] Step 3:

[0284] The server receives the inquiry content sent by the user. The server receives the request through the API endpoint and adds the inquiry content to the internal processing queue. The input is the text data sent from the terminal and is received at the server's API endpoint. As a specific operation, the server analyzes the HTTP POST request to obtain the text "Please teach me how to initialize an iPhone" and adds it to the internal processing queue.

[0285] Step 4:

[0286] The server passes the received inquiry content to the sentiment engine and requests it to recognize the user's sentiment. The input is the text data of the received inquiry content, which the sentiment engine receives. The sentiment engine analyzes the text data to identify the user's sentiment state. As a specific operation, the server sends the text "Please teach me how to initialize an iPhone" to the sentiment engine, and the sentiment engine recognizes emotions such as frustration and confusion.

[0287] Step 5:

[0288] The server passes the result of sentiment recognition to the generative AI and requests an analysis of the inquiry content. The input is the result of sentiment recognition and the text data of the inquiry content, which the generative AI receives. The generative AI uses natural language processing technology to understand the main keywords and context and grasp the intention of the inquiry content. Specifically, the generative AI extracts the keywords "iPhone", "initialization", and "method".

[0289] Step 6:

[0290] The generative AI generates an appropriate response based on the analysis results and emotion recognition results. The input is the analysis results and emotion recognition results from the generative AI, and the output is the specific response text for the user. Specifically, the generative AI generates a response that includes detailed instructions, such as "How to reset your iPhone: Settings > General > Reset > Erase All Content and Settings. Please note that this operation will erase all your data."

[0291] Step 7:

[0292] The server receives the response text from the generative AI and sends the response to the user's device. The input is the response text received from the generative AI, and the output is the text data sent to the user's device. Specifically, the server sends the response text to the device as an HTTP response.

[0293] Step 8:

[0294] The user's device receives a response from the server and displays it on the user interface. The input is the response text received from the server, and the output is the information displayed on the device's screen. Specifically, the device displays the received response and shows the steps "How to reset your iPhone: Settings > General > Reset > Erase All Content and Settings..." on the screen. The user can then reset their iPhone by following the displayed steps.

[0295] (Application Example 2)

[0296] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0297] Conventional customer support systems in virtual stores have suffered from low customer satisfaction because they provide mechanical responses without considering the user's emotions. In particular, when customers are feeling frustrated or anxious, appropriate responses may not be provided, potentially damaging customer trust. This invention aims to solve this problem and provide a system that provides appropriate and detailed support tailored to the customer's emotions.

[0298] 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. In this invention, the server includes means for receiving the inquiry content entered by the user, means for generating artificial intelligence for analyzing the received inquiry content, means for generating an appropriate answer based on the analysis results, means for analyzing the user's emotions, means for adjusting the answer according to the emotion analysis results, and means for transmitting the generated answer to the user terminal. This makes it possible to provide an optimized answer according to the emotions in response to the inquiry content entered by the user.

[0299] "Means for receiving user-entered inquiry content" refers to a function that receives text-formatted inquiry content entered by the user on their device.

[0300] "Generative artificial intelligence means for analyzing received inquiry content" refers to an AI model that analyzes received text-based inquiry content using natural language processing technology and extracts key keywords and context.

[0301] "Generative artificial intelligence means for generating appropriate answers based on analysis results" refers to an AI model that automatically generates appropriate answers to inquiries based on analysis results.

[0302] "Means for analyzing user emotions" refers to a function that recognizes and identifies emotional states (e.g., frustration, impatience, confusion, etc.) from text entered by the user.

[0303] The "means for adjusting the response according to the sentiment analysis result" is a function that appropriately adjusts the tone and details of the response according to the sentiment analysis result and generates a response that takes into account the user's sentiment.

[0304] The "means for transmitting the generated response to the user terminal" is a function that transmits the generated response to the user's terminal using a communication protocol so that the user can view it.

[0305] The present invention is applied to a customer support system in a virtual store. The system efficiently analyzes the content of the inquiry input by the user and has a function of generating an appropriate response according to the user's sentiment using a generative artificial intelligence (AI) and a sentiment engine. This system is configured as follows.

[0306] First, the user uses their terminal to input the content of the inquiry. The terminal provides a function of inputting and transmitting the content of the inquiry via a web interface or an application. For example, when the user inputs "Please teach me the return method in the virtual store", the terminal uses a communication protocol (e.g., HTTP POST request) to transmit the content of the inquiry to the server.

[0307] The server passes the received inquiry content to the sentiment engine to analyze the user's sentiment. The sentiment engine analyzes the inquiry text and identifies sentiment states such as "frustration", "anxiety", and "confusion". By passing this sentiment analysis result to the generative AI on the server, the generative AI uses natural language processing technology to understand the main keywords and context and grasp the intention of the inquiry content.

[0308] The generative AI generates an appropriate response based on the analysis result and the result of sentiment recognition. For example, when the user is recognized as being confused, the generative AI generates a response that includes detailed and polite procedures. A specific prompt sentence is "The user asked a question in a frustrated state: I don't know the size of this dress. Answer:". In this way, the tone and content of the response are adjusted according to the sentiment.

[0309] The generated answers are sent from the server to the user's device and displayed in the user interface. Users can check the answers on their own devices and obtain specific instructions and information. For example, if a user asks, "How do I return an item in the virtual store?", detailed return instructions will be displayed in an emotionally sensitive tone. If a user asks in frustration, "I don't know the size of this dress," they will receive a detailed answer such as, "The size of this dress is as follows: Size S: Shoulder width 34cm, bust 76cm, waist 60cm..."

[0310] The system uses the following hardware and software: a server (such as Amazon Web Services (AWS®) EC2), the sentiment-analysis model from the transformers library for sentiment recognition, and GPT-2 for the generative AI model. This makes it possible to provide responses optimized according to the emotions expressed in user inquiries.

[0311] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0312] Step 1:

[0313] The user enters their inquiry on their device. The user uses a web interface or application to enter an inquiry, such as "How do I return an item at a virtual store?". The entered inquiry is stored on the device as text data.

[0314] Step 2:

[0315] The terminal sends the entered query content to the server. The terminal uses a communication protocol (e.g., HTTP POST request) to send the query content in text format to the server. The input data is sent to the endpoint and reaches the server.

[0316] Step 3:

[0317] The server passes the received query content to the emotion engine, which analyzes the user's emotions. The emotion engine analyzes the query text and identifies emotional states such as "frustration," "anxiety," or "confusion." The emotion engine takes text data as input and outputs emotional states.

[0318] Step 4:

[0319] The server passes the emotion recognition results to a generative artificial intelligence (AI) and requests its analysis of the inquiry. The generative AI uses natural language processing techniques to understand key keywords and context, grasping the intent of the inquiry. For example, it extracts keywords such as "virtual store," "how to return items," and "tell me." The analysis data, along with the emotion recognition results, is then passed to the generative AI.

[0320] Step 5:

[0321] The generative AI generates an appropriate response based on the analysis results and sentiment recognition results. The generative AI generates responses using prompt sentences. For example, it takes a prompt sentence such as "The user asked this question while frustrated: How do I return an item in a virtual store? Answer:" as input and outputs a response such as "The return procedure is as follows: Please prepare proof of purchase first, according to the return policy..."

[0322] Step 6:

[0323] The server receives the generated response and sends it to the user's device. The server uses a communication protocol (e.g., HTTP POST request) to send the generated response text to the user's device. The response data is sent to the endpoint and reaches the user's device.

[0324] Step 7:

[0325] The user's device receives the response from the server and displays it in the user interface. The response text is displayed in the device's UI component for the user to review. Specifically, detailed instructions such as, "The return procedure is as follows: Please follow our return policy and first prepare proof of purchase..." are displayed.

[0326] 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.

[0327] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0328] 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.

[0329] [Second Embodiment]

[0330] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0331] 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.

[0332] 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).

[0333] 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.

[0334] 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.

[0335] 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).

[0336] 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.

[0337] 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.

[0338] 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.

[0339] 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.

[0340] 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.

[0341] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0342] This invention relates to a system that efficiently processes user-inputted inquiries using generative artificial intelligence (AI) and provides appropriate answers quickly. This system consists of multiple elements, including a server, a terminal, and generative artificial intelligence.

[0343] 1. User input of inquiry

[0344] First, the user enters their inquiry using a device. This device operates via a web interface or application. For example, the user might type, "How do I reset my iPhone?"

[0345] 2. Submit your inquiry.

[0346] The terminal sends the entered query content to the server. The server receives the query content using a communication protocol and prepares to perform appropriate analysis.

[0347] 3. Receiving queries by the server

[0348] The server receives the content of inquiries sent by users and passes that content to a generative artificial intelligence system. The server is required to efficiently process this reception and analysis request process.

[0349] 4. Analysis using generative artificial intelligence

[0350] Next, a generative artificial intelligence analyzes the received inquiry. The AI ​​uses text analysis techniques to identify key keywords and context. For example, it extracts keywords such as "iPhone," "initialization," and "method."

[0351] 5. Generating the answer

[0352] The generative artificial intelligence generates an appropriate answer based on the analysis results. Specifically, for example, it might generate the procedure "How to reset your iPhone: Settings > General > Reset > Erase All Content and Settings".

[0353] 6. Submit your response

[0354] The server receives the response from the generative artificial intelligence and sends the response to the user's terminal. In this process, the server again uses a communication protocol to send the response text.

[0355] 7. Receiving and confirming user responses

[0356] Ultimately, the user's device receives the response from the server and displays it on the user interface. For example, the user can check the initialization procedure on their smartphone or computer screen.

[0357] Specific example

[0358] Specific examples are given below.

[0359] Example 1: A user asks, "How do I use Corporate Concierge?"

[0360] The user enters a question into their device and sends it to the server.

[0361] The server receives the question and requests analysis from a generative artificial intelligence.

[0362] Generative artificial intelligence generates information on "how to use corporate concierge services."

[0363] The server sends the generated information to the user.

[0364] The user's device displays the following instructions: "How to use Corporate Concierge: First, launch the app and log in. Next..."

[0365] In this way, the system of the present invention allows users to obtain the necessary information quickly and accurately. Furthermore, companies can significantly improve the efficiency of responding to inquiries.

[0366] The above describes specific embodiments for carrying out the present invention. This will enable users to easily utilize the system and receive appropriate support.

[0367] The following describes the processing flow.

[0368] Step 1:

[0369] The user enters their inquiry using a device. The device provides a user interface via a web interface or application. For example, the user might type, "How do I reset my iPhone?"

[0370] Step 2:

[0371] The terminal sends the entered query content to the server. The terminal uses a communication protocol (e.g., HTTP POST request) to send the query content to the server. At this point, the query content arrives at the server in text format.

[0372] Step 3:

[0373] The server receives the query content sent by the user. The server receives the request through the API endpoint and passes the query content to its internal processing.

[0374] Step 4:

[0375] The server requests a generative artificial intelligence (AI) to analyze the received inquiry. The server calls the generative AI's API and sends the inquiry text to analyze the inquiry.

[0376] Step 5:

[0377] The generative AI analyzes the received inquiry. Using natural language processing techniques, the generative AI analyzes key keywords and context to understand the intent of the inquiry. For example, it extracts keywords such as "iPhone," "initialization," and "method."

[0378] Step 6:

[0379] The generative AI generates appropriate answers based on the analysis results. The AI ​​generates specific operating procedures and information in text format according to the content of the inquiry. For example, it might generate the procedure: "How to reset your iPhone: Settings > General > Reset > Erase All Content and Settings".

[0380] Step 7:

[0381] The server receives the response text from the generative AI. The server receives the generated response text and prepares to send it to the user's terminal.

[0382] Step 8:

[0383] The server sends the generated response to the user's device. The server then uses the communication protocol again to send the response text to the user's device.

[0384] Step 9:

[0385] The user's device receives a response from the server. The device displays the received response text on the user interface. For example, the initialization procedure is displayed on the user's smartphone or computer screen.

[0386] Step 10:

[0387] The user checks the response displayed on the device and performs the necessary actions according to the instructions. For example, the user may perform a factory reset of their iPhone by following the displayed instructions.

[0388] As described above, the system of the present invention responds quickly and accurately to user inquiries, improving user convenience. This allows companies to significantly improve the efficiency of their inquiry handling.

[0389] (Example 1)

[0390] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0391] Many modern systems suffer from delays in providing responses to user inquiries and incur significant manual workloads. There is a need to solve this problem and create a system that can respond to user inquiries quickly and accurately.

[0392] 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.

[0393] In this invention, the server includes means for receiving the content of an inquiry entered by the user, means for passing the received content of the inquiry to a generative artificial intelligence, means for the generative artificial intelligence to analyze the received content and identify key keywords and context, means for the generative artificial intelligence to generate an appropriate answer based on the analysis results, means for sending the generated answer to the user terminal, and means for the user terminal to receive the answer from the server and display it on the user interface. This enables the user to obtain the necessary information quickly and accurately.

[0394] A "user" refers to an end-user who enters an inquiry into the system and receives a response.

[0395] A "terminal" refers to a device such as a computer or smartphone that a user uses to input their inquiry and receive a response from a server.

[0396] A "server" refers to a computing system that receives user inquiries, forwards them to a generative artificial intelligence system, and generates and sends responses.

[0397] "Generative artificial intelligence" refers to an AI model equipped with natural language processing that analyzes user inquiries and generates appropriate responses.

[0398] "Means of receiving" refers to the function that allows the server to receive the content of inquiries sent by the user.

[0399] "Means of transferring to generative artificial intelligence" refers to the function that allows the server to transfer the content of the inquiry it receives to the generative artificial intelligence.

[0400] "Means of analysis" refers to the functions that generative artificial intelligence uses to analyze the content of inquiries and identify key keywords and context.

[0401] "Means for generating answers" refers to the function that allows generative artificial intelligence to come up with appropriate answers based on analysis results.

[0402] "Means of transmission" refers to the function that allows the server to send the generated response to the user's terminal.

[0403] "User interface" refers to the screen display or application interface that users use to input inquiries or view received responses.

[0404] This invention relates to a system that efficiently processes user-inputted inquiries using generative artificial intelligence and provides appropriate answers quickly. This system consists of multiple elements, including a server, a terminal, and generative artificial intelligence.

[0405] First, the user enters their inquiry using a device. This device operates via a web interface or application. For example, the user might enter "How do I reset my iPhone?". This inquiry is sent from the device to the server using the HTTP / HTTPS communication protocol.

[0406] Next, the server receives the query content sent by the user. The server analyzes the received query content and prepares it for passing on to the generative artificial intelligence described later. The receiving terminal can also temporarily store the received content in a database.

[0407] The server then passes the query to a generative artificial intelligence (AI). The AI ​​uses NLP (Natural Language Processing) techniques to analyze the query. This analysis process identifies key keywords and context, and generates an appropriate response based on them. For example, it might extract keywords such as "iPhone," "initialization," and "method."

[0408] The generative artificial intelligence generates an appropriate answer based on the analysis results. Specifically, it generates the procedure "How to reset your iPhone: Settings > General > Reset > Erase All Content and Settings". This generation process is performed using a generative artificial intelligence model and prompt statements.

[0409] The generated response is sent back to the server, which then transmits the response data to the user's device. The communication protocol used is again HTTP / HTTPS. This data transfer process is required to be secure and fast.

[0410] Finally, the user's device receives the response sent from the server and displays it on the user interface. The user can then review the initialization procedure on their smartphone or computer screen.

[0411] As a concrete example, let's consider a scenario where a user asks, "How do I use Corporate Concierge?" In this case, the user types "How do I use Corporate Concierge?" into their device and sends it to the server. The server receives the question and requests analysis from a generative artificial intelligence. The generative AI generates information about "how to use Corporate Concierge," and the server sends this information to the user. Then, the user's device displays instructions such as "How to use Corporate Concierge: First, launch the app and log in. Next..."

[0412] In this way, the system of the present invention allows users to obtain the necessary information quickly and accurately. Furthermore, by using this system, companies can significantly improve the efficiency of responding to inquiries.

[0413] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0414] Step 1: User enters inquiry

[0415] The user enters their inquiry details using a terminal. This terminal operates via a web interface or application.

[0416] Input: The user enters "How do I reset my iPhone?" into the text input box on the device.

[0417] Output: Stored as text data in the terminal's memory.

[0418] Specific action: The user types text using the keyboard and presses the send button.

[0419] Step 2: Submit your inquiry

[0420] The terminal sends the entered query content to the server. The terminal does this using an HTTP POST request.

[0421] Input: Text data entered by the user.

[0422] Output: The HTTP request sent to the server.

[0423] Specific operation: The terminal stores the query text in the body of the HTTP request and sends it to the specified server endpoint.

[0424] Step 3: The server receives the query.

[0425] The server receives the query content sent by the user. The received content is temporarily stored in the server's memory and then saved to the database as needed.

[0426] Input: HTTP request sent from the terminal.

[0427] Output: The query content stored in the server's memory or database.

[0428] Specific operation: The server receives the request, extracts the query content from the request body, and stores it temporarily.

[0429] Step 4: Transferring the received content to the generational artificial intelligence system.

[0430] The server passes the received query details to the generative artificial intelligence. An API request is then made to the generative artificial intelligence.

[0431] Input: The query content stored on the server.

[0432] Output: API request to a generative artificial intelligence system.

[0433] Specific operation: The server converts the query content into an API request format and sends it to the generative artificial intelligence endpoint.

[0434] Step 5: Analysis using generative artificial intelligence

[0435] Generative artificial intelligence analyzes the content of the received inquiry. NLP techniques are used to identify key keywords and context.

[0436] Input: The content of the inquiry sent from the server.

[0437] Output: Keywords and contextual information as analysis results.

[0438] Specific operation: The generative artificial intelligence tokenizes the query content and performs principal component analysis and contextual analysis.

[0439] Step 6: Generating the answer

[0440] Generative artificial intelligence generates appropriate answers based on the analysis results. For example, it might generate the procedure: "How to reset your iPhone: Settings > General > Reset > Erase All Content and Settings".

[0441] Input: Keywords and contextual information as analysis results.

[0442] Output: Answer text.

[0443] Specific operation: Generative artificial intelligence searches for appropriate information from a knowledge base and generates an answer using a text generation algorithm.

[0444] Step 7: Submit your response

[0445] The server receives a response from a generative artificial intelligence and sends it to the user's terminal. HTTP responses are used.

[0446] Input: Text response from a generative artificial intelligence.

[0447] Output: The HTTP response sent to the user's terminal.

[0448] Specific operation: The server stores the response text in the body of the HTTP response and sends it to the user's terminal.

[0449] Step 8: Receiving and confirming the user's response

[0450] The user's device receives the response from the server and displays it on the user interface.

[0451] Input: HTTP response sent from the server.

[0452] Output: The answer displayed on the device's screen.

[0453] Specific operation: The terminal receives an HTTP response, updates the corresponding part of the interface, and displays the answer.

[0454] (Application Example 1)

[0455] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0456] Online content distribution services have faced problems such as users easily becoming confused when trying to find the next content to watch, and taking a long time to get appropriate answers. Therefore, there is a need for a system that can quickly and accurately suggest the most suitable content based on viewing and usage history.

[0457] 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.

[0458] In this invention, the server includes means for receiving inquiry content entered by the user, means for generative artificial intelligence for analyzing the received inquiry content, means for generative artificial intelligence for generating an appropriate response based on the analysis results, means for transmitting the generated response to the user terminal, and means for suggesting the next content to be viewed based on the usage history and viewing history of the content distribution service. This makes it possible for the user to quickly find content to watch.

[0459] "User terminal" refers to devices used by the user, such as computers, smartphones, tablets, smart glasses, head-mounted displays, or robots.

[0460] "Inquiry content" refers to the text information of questions and requests entered by the user through their device.

[0461] "Generative artificial intelligence" refers to an AI model that analyzes user-inputted inquiries and generates appropriate responses.

[0462] "Generated answers" refer to the answers to user questions generated by generative artificial intelligence based on analysis results.

[0463] A "content distribution service" refers to an online service that provides users with digital content such as movies, television programs, documentaries, and animation.

[0464] "Usage history" refers to a record of content that a user has previously viewed or downloaded from a content distribution service.

[0465] "Viewing history" refers to a record of the content that a user has actually viewed using a content distribution service.

[0466] "The suggested methods" refer to a function that uses generative artificial intelligence to analyze a user's usage history and viewing history, and then suggests content that the user should watch next.

[0467] The system implementing this invention consists of multiple elements, including a user terminal, a server, and a generative artificial intelligence system. This system is specifically designed for content distribution services and helps users easily find the next content they should watch.

[0468] First, the user terminal consists of devices such as smartphones, smart glasses, head-mounted displays, or robots. The user enters their inquiry through these terminals. For example, they might enter a question like, "Which movie should I watch next?"

[0469] The entered query content is sent to the server. The server receives this query content and passes it on to a generative artificial intelligence for appropriate analysis. HTTPS is used as the communication protocol by the server to ensure security.

[0470] Generative artificial intelligence analyzes user inquiries based on their viewing and usage history. This analysis extracts key keywords and context from the viewing history and generates responses that suggest the most suitable content. For example, it might generate a response like, "Based on your current viewing history and favorite genres, the recommended movie is 'Inception'."

[0471] The server receives the generated response and sends it to the user's device. The user's device displays the received response on its screen. The user can then review this response on their device and select the next content to view.

[0472] The specific software used will be the OpenAI API for generative artificial intelligence, and Python for program implementation. The requests module will be used for sending and receiving data, and the HTTPS protocol will be used to ensure secure communication.

[0473] Specific example:

[0474] When a user types "What movie should I watch next?" on their smartphone,

[0475] The generative artificial intelligence analyzes the user's viewing history and generates a response such as, "Based on your current viewing history and favorite genres, the recommended movie is 'Inception'."

[0476] Example of a prompt:

[0477] User: What movie should I watch next?

[0478] Generative AI Model: Based on your current viewing history and favorite genres, the recommended movie is "Inception".

[0479] This allows users to easily discover appropriate content, thus avoiding content fatigue. Using server-side and generative artificial intelligence, users can quickly and accurately find the next content they should watch.

[0480] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0481] Step 1:

[0482] The user enters their inquiry using a terminal. For example, they might enter, "Which movie should I see next?" The input data is retrieved in text format.

[0483] Step 2:

[0484] The terminal sends the entered query content to the server. The data sent is in text format and is transmitted securely using the HTTPS protocol.

[0485] Step 3:

[0486] The server receives the inquiry content sent by the user and prepares to pass that text data to the generative artificial intelligence. The input data here is the received inquiry content in text format.

[0487] Step 4:

[0488] A generative artificial intelligence analyzes the received inquiry content and the user's viewing history data. This analysis extracts keywords from the viewing history and generates a response appropriate to the inquiry. The input is the inquiry content and viewing history data, and the output is the generated response text.

[0489] Step 5:

[0490] The server receives the generated response and sends it to the user's terminal. The transmitted data is the generated response text. The server then uses the HTTPS protocol to send the data again.

[0491] Step 6:

[0492] The user's device receives the response sent from the server and displays it on the user interface. The displayed data is the generated response text. For example, the response "Based on your current viewing history and favorite genres, the recommended movie is 'Inception'" might be displayed on the user's device.

[0493] As described above, the overall processing flow of the system is as follows: User inputs inquiry content → Terminal sends it to the server → Server receives the inquiry and passes it to the generative AI → Generative AI analyzes and generates an answer → Server sends the answer to the user terminal → User terminal displays the answer.

[0494] 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.

[0495] This invention is a system that efficiently analyzes user-inputted inquiries using generative artificial intelligence (AI) and an emotion engine, and generates appropriate responses that reflect the user's emotions. This system consists of elements such as a server, a terminal, a generative AI, and an emotion engine.

[0496] 1. User input of inquiry

[0497] First, the user enters their inquiry using their device. The device provides a user interface via a web interface or application. For example, the user might type, "How do I reset my iPhone?"

[0498] 2. Submit your inquiry.

[0499] The terminal sends the entered query content to the server. The terminal uses a communication protocol (e.g., HTTP POST request) to send the query content to the server. At this point, the query content arrives at the server in text format.

[0500] 3. Receiving queries by the server

[0501] The server receives the query content sent by the user. The server receives the request through the API endpoint and passes the query content to its internal processing.

[0502] 4. Request for emotion recognition by the server

[0503] Next, the server passes the received query to the sentiment engine and asks it to recognize the user's emotions. The sentiment engine analyzes the query text and identifies the user's emotions. For example, the sentiment engine might recognize emotions such as "frustration," "anxiety," or "confusion."

[0504] 5. Analysis using generative artificial intelligence

[0505] The server then passes the emotion recognition results to a generative artificial intelligence (AI) and requests it to analyze the inquiry. The generative AI uses natural language processing techniques to understand the main keywords and context and grasp the intent of the inquiry. For example, it might extract keywords such as "iPhone," "initialization," and "method."

[0506] 6. Generating the answer

[0507] The generative AI generates appropriate responses based on the analysis results and emotion recognition. For example, if the AI ​​detects that the user is confused, it generates a response that includes more detailed and polite instructions. Specifically, it adjusts the tone and style, such as, "How to reset your iPhone: Settings > General > Reset > Erase All Content and Settings. Please note that this operation will erase all your data."

[0508] 7. Submit your response

[0509] The server receives the response text from the generative AI and sends the response to the user's device. In this process, the server again uses a communication protocol to send the response text to the user's device.

[0510] 8. Confirmation of user responses

[0511] The user's device receives the response from the server and displays it on the user interface. For example, the user can check the initialization procedure on their smartphone or computer screen.

[0512] Specific example

[0513] The following will explain this with specific examples.

[0514] Example 1: A user asks, "How do I use Corporate Concierge?", and the emotion engine recognizes the user's frustration.

[0515] The user enters a question into their device and sends it to the server.

[0516] The server receives the question and requests analysis from the emotion engine.

[0517] The emotion engine recognizes the user's "frustration."

[0518] The server sends the emotion recognition results and the question content to the generative AI.

[0519] The generative AI generates detailed and easy-to-understand information on "how to use Corporate Concierge."

[0520] The server sends the generated information to the user.

[0521] The user's device displays the following instructions: "How to use Corporate Concierge: First, launch the app and log in. Next..."

[0522] In this way, the system of the present invention allows users to obtain the necessary information quickly and accurately, as well as receive optimal support tailored to their emotional needs. This enables companies to significantly improve the efficiency of handling inquiries and enhance user satisfaction.

[0523] The following describes the processing flow.

[0524] Step 1:

[0525] The user enters their inquiry using a device. The device provides a user interface via a web interface or application, for example, the user might type, "How do I reset my iPhone?"

[0526] Step 2:

[0527] The terminal sends the entered query content to the server. The terminal uses a communication protocol (e.g., HTTP POST request) to send the query content to the server. At this point, the query content arrives at the server in text format.

[0528] Step 3:

[0529] The server receives the query content sent by the user. The server receives the request through the API endpoint and passes the query content to its internal processing.

[0530] Step 4:

[0531] The server passes the received query content to the sentiment engine for analysis. The sentiment engine uses natural language processing techniques to analyze the query text and recognize the user's emotions. For example, the sentiment engine might identify emotions such as "frustration" or "confusion" from the query text.

[0532] Step 5:

[0533] The emotion engine returns the user's recognized emotions to the server. The server receives these emotion recognition results and prepares to request the next step of processing from the generative artificial intelligence (AI).

[0534] Step 6:

[0535] The server passes the emotion recognition results and the inquiry content to a generative artificial intelligence (AI) system, requesting analysis and response generation. The generative AI analyzes the inquiry content while considering the emotion recognition results. Specifically, it uses automated text analysis technology to understand key keywords and context.

[0536] Step 7:

[0537] Generative artificial intelligence generates appropriate responses based on analysis results and the user's emotions. For example, if the user is perceived as "frustrated," it will provide a more helpful and polite response. Specifically, it will adjust the tone and style of a response such as, "How to reset your iPhone: Settings > General > Reset > Erase All Content and Settings. Please note that this operation will erase all your data."

[0538] Step 8:

[0539] The server receives the response text generated by the generative AI and sends the response to the user's terminal. The server uses a communication protocol to send the response text to the user's terminal.

[0540] Step 9:

[0541] The device receives a response from the server and displays it on the user interface. Specifically, the initialization procedure is displayed on the user's smartphone or computer screen.

[0542] Step 10:

[0543] The user checks the response displayed on the device and performs the necessary actions according to the instructions. For example, the user may perform a factory reset of their iPhone by following the displayed instructions.

[0544] As described above, the system of the present invention can respond to user inquiries quickly and accurately, and further provide optimal support tailored to the user's emotions. This allows companies to significantly improve the efficiency of inquiry handling and increase user satisfaction.

[0545] (Example 2)

[0546] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0547] Traditional inquiry systems often fail to provide satisfactory answers because they analyze only the content of the question without considering the user's emotions. Furthermore, inappropriate answers can exacerbate dissatisfaction, especially for users experiencing frustration or confusion. Therefore, there is a need for a system that provides appropriate and effective answers tailored to the user's emotions.

[0548] The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving inquiry content entered by the user, means for identifying the emotion of the received inquiry content using emotion analysis means, means for passing the emotion information obtained from the emotion analysis means and the inquiry content to a generative artificial intelligence means, means for analyzing the inquiry content using the generative artificial intelligence means and generating an appropriate answer based on the analysis results, and means for transmitting the generated answer to the user terminal. This makes it possible to provide an appropriate and highly satisfying answer that takes the user's emotions into consideration.

[0549] A "user" refers to a person who uses the system to input inquiry details and obtain information.

[0550] "Inquiry content" refers to the text data of questions and requests that users enter through the system.

[0551] A "terminal" refers to a hardware device used by a user to input and submit inquiry details. This includes smartphones, personal computers, tablets, and other similar devices.

[0552] A "server" refers to a central computing system that receives inquiries, analyzes them, performs emotion recognition and generative artificial intelligence processing, and ultimately generates and sends answers to terminals.

[0553] "Emotional analysis means" refers to a technology or program that analyzes the text data of an input inquiry to identify the user's emotional state.

[0554] "Generative artificial intelligence" refers to artificial intelligence that uses natural language processing technology to analyze input inquiries and generate appropriate responses.

[0555] "Analysis results" refer to the understanding of the query content as analyzed by generative artificial intelligence. This includes keyword extraction and contextual understanding.

[0556] "Answer" refers to an appropriate response to a user's inquiry generated by a generative artificial intelligence system.

[0557] This invention is a system that efficiently analyzes user-inputted inquiries using generative artificial intelligence (AI) and an emotion engine, and generates appropriate responses that reflect the user's emotions. The system consists of elements such as a server, a terminal, a generative AI, and an emotion engine.

[0558] First, the user enters their inquiry using their device. The device provides a user interface via a web interface or application. Let's take the example where the user enters "How do I reset my iPhone?"

[0559] Next, the terminal sends the entered query content to the server. The terminal uses a communication protocol such as an HTTP POST request to send the query content to the server. At this point, the query content arrives at the server in text format.

[0560] The server receives the query content sent by the user. The server receives the request through the API endpoint and passes the query content to its internal processing.

[0561] The server passes the received query to the emotion engine and requests it to recognize the user's emotions. The emotion engine analyzes the query text and identifies the user's emotions. For example, the emotion engine might recognize emotions such as "frustration," "anxiety," or "confusion."

[0562] The server then passes the emotion recognition results to a generative artificial intelligence (AI) and requests it to analyze the inquiry. The generative AI uses natural language processing techniques to understand the main keywords and context and grasp the intent of the inquiry. For example, it might extract keywords such as "iPhone," "initialization," and "method."

[0563] Generative AI generates appropriate responses based on analysis results and emotion recognition. For example, if the AI ​​detects that the user is confused, it will generate a response that includes more detailed and polite instructions. Specifically, it will adjust the tone and style, such as, "How to reset your iPhone: Settings > General > Reset > Erase All Content and Settings. Please note that this operation will erase all your data."

[0564] Next, the server receives the response text from the generative AI and sends the response to the user's device. In this process, the server again uses a communication protocol to send the response text to the user's device.

[0565] Finally, the user's device receives the response from the server and displays it on the user interface. For example, the user can check the initialization procedure on their smartphone or computer screen.

[0566] The following will explain this with specific examples.

[0567] Example 1: A user asks, "How do I use Corporate Concierge?", and the emotion engine recognizes the user's frustration.

[0568] The user enters a question into their device and sends it to the server.

[0569] The server receives the question and requests analysis from the emotion engine.

[0570] The emotion engine recognizes the user's "frustration."

[0571] The server sends the emotion recognition results and the question content to the generative AI.

[0572] The generative AI generates detailed and easy-to-understand information on "how to use Corporate Concierge."

[0573] The server sends the generated information to the user.

[0574] The user's device displays the following instructions: "How to use Corporate Concierge: First, launch the app and log in. Next..."

[0575] In this way, the system of the present invention allows users to obtain the necessary information quickly and accurately, as well as receive optimal support tailored to their emotional needs. This enables companies to significantly improve the efficiency of handling inquiries and enhance user satisfaction.

[0576] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0577] Step 1:

[0578] The user enters their inquiry using a device. The device provides a user interface through a web interface or application. Specifically, the user enters "How do I reset my iPhone?" into the text input field on the device and clicks the send button. The input is stored on the device as text data.

[0579] Step 2:

[0580] The device sends the entered query content to the server. The device uses a communication protocol such as an HTTP POST request to send the query content to the server. The data sent is the query content in text format and reaches the server. Specifically, the device sends the text "Please tell me how to reset my iPhone" via an HTTP POST request.

[0581] Step 3:

[0582] The server receives the inquiry sent by the user. The server receives the request through the API endpoint and adds the inquiry to a queue for internal processing. The input is text data sent from the device and received at the server's API endpoint. Specifically, the server parses the HTTP POST request to obtain the text "How do I reset my iPhone?" and adds it to the internal processing queue.

[0583] Step 4:

[0584] The server passes the received inquiry to the emotion engine and requests it to recognize the user's emotions. The input is the text data of the received inquiry, which the emotion engine receives. The emotion engine analyzes the text data and identifies the user's emotional state. Specifically, the server sends the text "How do I reset my iPhone?" to the emotion engine, and the emotion engine recognizes emotions such as frustration or confusion.

[0585] Step 5:

[0586] The server passes the emotion recognition results to the generative artificial intelligence and requests its analysis of the inquiry. The input consists of the emotion recognition results and the text data of the inquiry, which the generative AI receives. The generative AI uses natural language processing techniques to understand the main keywords and context and grasp the intent of the inquiry. Specifically, the generative AI extracts the keywords "iPhone," "initialization," and "method."

[0587] Step 6:

[0588] The generative AI generates an appropriate response based on the analysis results and emotion recognition results. The input is the analysis results and emotion recognition results from the generative AI, and the output is the specific response text for the user. Specifically, the generative AI generates a response that includes detailed instructions, such as "How to reset your iPhone: Settings > General > Reset > Erase All Content and Settings. Please note that this operation will erase all your data."

[0589] Step 7:

[0590] The server receives the response text from the generative AI and sends the response to the user's device. The input is the response text received from the generative AI, and the output is the text data sent to the user's device. Specifically, the server sends the response text to the device as an HTTP response.

[0591] Step 8:

[0592] The user's device receives a response from the server and displays it on the user interface. The input is the response text received from the server, and the output is the information displayed on the device's screen. Specifically, the device displays the received response and shows the steps "How to reset your iPhone: Settings > General > Reset > Erase All Content and Settings..." on the screen. The user can then reset their iPhone by following the displayed steps.

[0593] (Application Example 2)

[0594] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0595] Conventional customer support systems in virtual stores have suffered from low customer satisfaction because they provide mechanical responses without considering the user's emotions. In particular, when customers are feeling frustrated or anxious, appropriate responses may not be provided, potentially damaging customer trust. This invention aims to solve this problem and provide a system that provides appropriate and detailed support tailored to the customer's emotions.

[0596] 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. In this invention, the server includes means for receiving the inquiry content entered by the user, means for generating artificial intelligence for analyzing the received inquiry content, means for generating an appropriate answer based on the analysis results, means for analyzing the user's emotions, means for adjusting the answer according to the emotion analysis results, and means for transmitting the generated answer to the user terminal. This makes it possible to provide an optimized answer according to the emotions in response to the inquiry content entered by the user.

[0597] "Means for receiving user-entered inquiry content" refers to a function that receives text-formatted inquiry content entered by the user on their device.

[0598] "Generative artificial intelligence means for analyzing received inquiry content" refers to an AI model that analyzes received text-based inquiry content using natural language processing technology and extracts key keywords and context.

[0599] "Generative artificial intelligence means for generating appropriate answers based on analysis results" refers to an AI model that automatically generates appropriate answers to inquiries based on analysis results.

[0600] "Means for analyzing user emotions" refers to a function that recognizes and identifies emotional states (e.g., frustration, impatience, confusion, etc.) from text entered by the user.

[0601] "Means of adjusting responses according to sentiment analysis results" refers to a function that appropriately adjusts the tone and details of responses according to sentiment analysis results, thereby generating responses that take the user's emotions into consideration.

[0602] "Means for sending the generated response to the user's terminal" refers to a function that sends the generated response to the user's terminal using a communication protocol, allowing the user to confirm it.

[0603] This invention is applied to customer support systems in virtual stores. The system efficiently analyzes user inquiries and utilizes generative artificial intelligence (AI) and an emotion engine to generate appropriate responses tailored to the user's emotions. The system is configured as follows:

[0604] First, the user enters their inquiry using their device. The device provides the functionality to enter and send the inquiry via a web interface or application. For example, if the user enters "How do I return an item at the virtual store?", the device uses a communication protocol (e.g., an HTTP POST request) to send the inquiry to the server.

[0605] The server passes the received inquiry to the emotion engine, which analyzes the user's emotions. The emotion engine analyzes the inquiry text and identifies emotional states such as "frustration," "anxiety," or "confusion." The server then passes these emotion analysis results to the generative AI, which uses natural language processing techniques to understand key keywords and context, and grasp the intent of the inquiry.

[0606] Generative AI generates appropriate answers based on analysis results and emotion recognition. For example, if the AI ​​detects that the user is confused, it will generate an answer that includes detailed and polite instructions. A specific prompt might be: "The user asked this question while feeling frustrated: I don't know the size of this dress. Answer:" In this way, the tone and content of the answer are adjusted according to the user's emotions.

[0607] The generated answers are sent from the server to the user's device and displayed in the user interface. Users can check the answers on their own devices and obtain specific instructions and information. For example, if a user asks, "How do I return an item in the virtual store?", detailed return instructions will be displayed in an emotionally sensitive tone. If a user asks in frustration, "I don't know the size of this dress," they will receive a detailed answer such as, "The size of this dress is as follows: Size S: Shoulder width 34cm, bust 76cm, waist 60cm..."

[0608] The system uses the following hardware and software: a server (such as Amazon Web Services (AWS) EC2), the sentiment-analysis model from the transformers library for sentiment recognition, and GPT-2 for generative AI models. This enables the system to provide responses optimized according to the emotions expressed in user inquiries.

[0609] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0610] Step 1:

[0611] The user enters their inquiry on their device. The user uses a web interface or application to enter an inquiry, such as "How do I return an item at a virtual store?". The entered inquiry is stored on the device as text data.

[0612] Step 2:

[0613] The terminal sends the entered query content to the server. The terminal uses a communication protocol (e.g., HTTP POST request) to send the query content in text format to the server. The input data is sent to the endpoint and reaches the server.

[0614] Step 3:

[0615] The server passes the received query content to the emotion engine, which analyzes the user's emotions. The emotion engine analyzes the query text and identifies emotional states such as "frustration," "anxiety," or "confusion." The emotion engine takes text data as input and outputs emotional states.

[0616] Step 4:

[0617] The server passes the emotion recognition results to a generative artificial intelligence (AI) and requests its analysis of the inquiry. The generative AI uses natural language processing techniques to understand key keywords and context, grasping the intent of the inquiry. For example, it extracts keywords such as "virtual store," "how to return items," and "tell me." The analysis data, along with the emotion recognition results, is then passed to the generative AI.

[0618] Step 5:

[0619] The generative AI generates an appropriate response based on the analysis results and sentiment recognition results. The generative AI generates responses using prompt sentences. For example, it takes a prompt sentence such as "The user asked this question while frustrated: How do I return an item in a virtual store? Answer:" as input and outputs a response such as "The return procedure is as follows: Please prepare proof of purchase first, according to the return policy..."

[0620] Step 6:

[0621] The server receives the generated response and sends it to the user's device. The server uses a communication protocol (e.g., HTTP POST request) to send the generated response text to the user's device. The response data is sent to the endpoint and reaches the user's device.

[0622] Step 7:

[0623] The user's device receives the response from the server and displays it in the user interface. The response text is displayed in the device's UI component for the user to review. Specifically, detailed instructions such as, "The return procedure is as follows: Please follow our return policy and first prepare proof of purchase..." are displayed.

[0624] 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.

[0625] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0626] 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.

[0627] [Third Embodiment]

[0628] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0629] 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.

[0630] 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).

[0631] 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.

[0632] 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.

[0633] 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).

[0634] 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.

[0635] 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.

[0636] 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.

[0637] 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.

[0638] 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.

[0639] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0640] This invention relates to a system that efficiently processes user-inputted inquiries using generative artificial intelligence (AI) and provides appropriate answers quickly. This system consists of multiple elements, including a server, a terminal, and generative artificial intelligence.

[0641] 1. User input of inquiry

[0642] First, the user enters their inquiry using a device. This device operates via a web interface or application. For example, the user might type, "How do I reset my iPhone?"

[0643] 2. Submit your inquiry.

[0644] The terminal sends the entered query content to the server. The server receives the query content using a communication protocol and prepares to perform appropriate analysis.

[0645] 3. Receiving queries by the server

[0646] The server receives the content of inquiries sent by users and passes that content to a generative artificial intelligence system. The server is required to efficiently process this reception and analysis request process.

[0647] 4. Analysis using generative artificial intelligence

[0648] Next, a generative artificial intelligence analyzes the received inquiry. The AI ​​uses text analysis techniques to identify key keywords and context. For example, it extracts keywords such as "iPhone," "initialization," and "method."

[0649] 5. Generating the answer

[0650] The generative artificial intelligence generates an appropriate answer based on the analysis results. Specifically, for example, it might generate the procedure "How to reset your iPhone: Settings > General > Reset > Erase All Content and Settings".

[0651] 6. Submit your response

[0652] The server receives the response from the generative artificial intelligence and sends the response to the user's terminal. In this process, the server again uses a communication protocol to send the response text.

[0653] 7. Receiving and confirming user responses

[0654] Ultimately, the user's device receives the response from the server and displays it on the user interface. For example, the user can check the initialization procedure on their smartphone or computer screen.

[0655] Specific example

[0656] Specific examples are given below.

[0657] Example 1: A user asks, "How do I use Corporate Concierge?"

[0658] The user enters a question into their device and sends it to the server.

[0659] The server receives the question and requests analysis from a generative artificial intelligence.

[0660] Generative artificial intelligence generates information on "how to use corporate concierge services."

[0661] The server sends the generated information to the user.

[0662] The user's device displays the following instructions: "How to use Corporate Concierge: First, launch the app and log in. Next..."

[0663] In this way, the system of the present invention allows users to obtain the necessary information quickly and accurately. Furthermore, companies can significantly improve the efficiency of responding to inquiries.

[0664] The above describes specific embodiments for carrying out the present invention. This will enable users to easily utilize the system and receive appropriate support.

[0665] The following describes the processing flow.

[0666] Step 1:

[0667] The user enters their inquiry using a device. The device provides a user interface via a web interface or application. For example, the user might type, "How do I reset my iPhone?"

[0668] Step 2:

[0669] The terminal sends the entered query content to the server. The terminal uses a communication protocol (e.g., HTTP POST request) to send the query content to the server. At this point, the query content arrives at the server in text format.

[0670] Step 3:

[0671] The server receives the query content sent by the user. The server receives the request through the API endpoint and passes the query content to its internal processing.

[0672] Step 4:

[0673] The server requests a generative artificial intelligence (AI) to analyze the received inquiry. The server calls the generative AI's API and sends the inquiry text to analyze the inquiry.

[0674] Step 5:

[0675] The generative AI analyzes the received inquiry. Using natural language processing techniques, the generative AI analyzes key keywords and context to understand the intent of the inquiry. For example, it extracts keywords such as "iPhone," "initialization," and "method."

[0676] Step 6:

[0677] The generative AI generates appropriate answers based on the analysis results. The AI ​​generates specific operating procedures and information in text format according to the content of the inquiry. For example, it might generate the procedure: "How to reset your iPhone: Settings > General > Reset > Erase All Content and Settings".

[0678] Step 7:

[0679] The server receives the response text from the generative AI. The server receives the generated response text and prepares to send it to the user's terminal.

[0680] Step 8:

[0681] The server sends the generated response to the user's device. The server then uses the communication protocol again to send the response text to the user's device.

[0682] Step 9:

[0683] The user's device receives a response from the server. The device displays the received response text on the user interface. For example, the initialization procedure is displayed on the user's smartphone or computer screen.

[0684] Step 10:

[0685] The user checks the response displayed on the device and performs the necessary actions according to the instructions. For example, the user may perform a factory reset of their iPhone by following the displayed instructions.

[0686] As described above, the system of the present invention responds quickly and accurately to user inquiries, improving user convenience. This allows companies to significantly improve the efficiency of their inquiry handling.

[0687] (Example 1)

[0688] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0689] Many modern systems suffer from delays in providing responses to user inquiries and incur significant manual workloads. There is a need to solve this problem and create a system that can respond to user inquiries quickly and accurately.

[0690] 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.

[0691] In this invention, the server includes means for receiving the content of an inquiry entered by the user, means for passing the received content of the inquiry to a generative artificial intelligence, means for the generative artificial intelligence to analyze the received content and identify key keywords and context, means for the generative artificial intelligence to generate an appropriate answer based on the analysis results, means for sending the generated answer to the user terminal, and means for the user terminal to receive the answer from the server and display it on the user interface. This enables the user to obtain the necessary information quickly and accurately.

[0692] A "user" refers to an end-user who enters an inquiry into the system and receives a response.

[0693] A "terminal" refers to a device such as a computer or smartphone that a user uses to input their inquiry and receive a response from a server.

[0694] A "server" refers to a computing system that receives user inquiries, forwards them to a generative artificial intelligence system, and generates and sends responses.

[0695] "Generative artificial intelligence" refers to an AI model equipped with natural language processing that analyzes user inquiries and generates appropriate responses.

[0696] "Means of receiving" refers to the function that allows the server to receive the content of inquiries sent by the user.

[0697] "Means of transferring to generative artificial intelligence" refers to the function that allows the server to transfer the content of the inquiry it receives to the generative artificial intelligence.

[0698] "Means of analysis" refers to the functions that generative artificial intelligence uses to analyze the content of inquiries and identify key keywords and context.

[0699] "Means for generating answers" refers to the function that allows generative artificial intelligence to come up with appropriate answers based on analysis results.

[0700] "Means of transmission" refers to the function that allows the server to send the generated response to the user's terminal.

[0701] "User interface" refers to the screen display or application interface that users use to input inquiries or view received responses.

[0702] This invention relates to a system that efficiently processes user-inputted inquiries using generative artificial intelligence and provides appropriate answers quickly. This system consists of multiple elements, including a server, a terminal, and generative artificial intelligence.

[0703] First, the user enters their inquiry using a device. This device operates via a web interface or application. For example, the user might enter "How do I reset my iPhone?". This inquiry is sent from the device to the server using the HTTP / HTTPS communication protocol.

[0704] Next, the server receives the query content sent by the user. The server analyzes the received query content and prepares it for passing on to the generative artificial intelligence described later. The receiving terminal can also temporarily store the received content in a database.

[0705] The server then passes the query to a generative artificial intelligence (AI). The AI ​​uses NLP (Natural Language Processing) techniques to analyze the query. This analysis process identifies key keywords and context, and generates an appropriate response based on them. For example, it might extract keywords such as "iPhone," "initialization," and "method."

[0706] The generative artificial intelligence generates an appropriate answer based on the analysis results. Specifically, it generates the procedure "How to reset your iPhone: Settings > General > Reset > Erase All Content and Settings". This generation process is performed using a generative artificial intelligence model and prompt statements.

[0707] The generated response is sent back to the server, which then transmits the response data to the user's device. The communication protocol used is again HTTP / HTTPS. This data transfer process is required to be secure and fast.

[0708] Finally, the user's device receives the response sent from the server and displays it on the user interface. The user can then review the initialization procedure on their smartphone or computer screen.

[0709] As a concrete example, let's consider a scenario where a user asks, "How do I use Corporate Concierge?" In this case, the user types "How do I use Corporate Concierge?" into their device and sends it to the server. The server receives the question and requests analysis from a generative artificial intelligence. The generative AI generates information about "how to use Corporate Concierge," and the server sends this information to the user. Then, the user's device displays instructions such as "How to use Corporate Concierge: First, launch the app and log in. Next..."

[0710] In this way, the system of the present invention allows users to obtain the necessary information quickly and accurately. Furthermore, by using this system, companies can significantly improve the efficiency of responding to inquiries.

[0711] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0712] Step 1: User enters inquiry

[0713] The user enters their inquiry details using a terminal. This terminal operates via a web interface or application.

[0714] Input: The user enters "How do I reset my iPhone?" into the text input box on the device.

[0715] Output: Stored as text data in the terminal's memory.

[0716] Specific action: The user types text using the keyboard and presses the send button.

[0717] Step 2: Submit your inquiry

[0718] The terminal sends the entered query content to the server. The terminal does this using an HTTP POST request.

[0719] Input: Text data entered by the user.

[0720] Output: The HTTP request sent to the server.

[0721] Specific operation: The terminal stores the query text in the body of the HTTP request and sends it to the specified server endpoint.

[0722] Step 3: The server receives the query.

[0723] The server receives the query content sent by the user. The received content is temporarily stored in the server's memory and then saved to the database as needed.

[0724] Input: HTTP request sent from the terminal.

[0725] Output: The query content stored in the server's memory or database.

[0726] Specific operation: The server receives the request, extracts the query content from the request body, and stores it temporarily.

[0727] Step 4: Transferring the received content to the generational artificial intelligence system.

[0728] The server passes the received query details to the generative artificial intelligence. An API request is then made to the generative artificial intelligence.

[0729] Input: The query content stored on the server.

[0730] Output: API request to a generative artificial intelligence system.

[0731] Specific operation: The server converts the query content into an API request format and sends it to the generative artificial intelligence endpoint.

[0732] Step 5: Analysis using generative artificial intelligence

[0733] Generative artificial intelligence analyzes the content of the received inquiry. NLP techniques are used to identify key keywords and context.

[0734] Input: The content of the inquiry sent from the server.

[0735] Output: Keywords and contextual information as analysis results.

[0736] Specific operation: The generative artificial intelligence tokenizes the query content and performs principal component analysis and contextual analysis.

[0737] Step 6: Generating the answer

[0738] Generative artificial intelligence generates appropriate answers based on the analysis results. For example, it might generate the procedure: "How to reset your iPhone: Settings > General > Reset > Erase All Content and Settings".

[0739] Input: Keywords and contextual information as analysis results.

[0740] Output: Answer text.

[0741] Specific operation: Generative artificial intelligence searches for appropriate information from a knowledge base and generates an answer using a text generation algorithm.

[0742] Step 7: Submit your response

[0743] The server receives a response from a generative artificial intelligence and sends it to the user's terminal. HTTP responses are used.

[0744] Input: Text response from a generative artificial intelligence.

[0745] Output: The HTTP response sent to the user's terminal.

[0746] Specific operation: The server stores the response text in the body of the HTTP response and sends it to the user's terminal.

[0747] Step 8: Receiving and confirming the user's response

[0748] The user's device receives the response from the server and displays it on the user interface.

[0749] Input: HTTP response sent from the server.

[0750] Output: The answer displayed on the device's screen.

[0751] Specific operation: The terminal receives an HTTP response, updates the corresponding part of the interface, and displays the answer.

[0752] (Application Example 1)

[0753] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0754] Online content distribution services have faced problems such as users easily becoming confused when trying to find the next content to watch, and taking a long time to get appropriate answers. Therefore, there is a need for a system that can quickly and accurately suggest the most suitable content based on viewing and usage history.

[0755] 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.

[0756] In this invention, the server includes means for receiving inquiry content entered by the user, means for generative artificial intelligence for analyzing the received inquiry content, means for generative artificial intelligence for generating an appropriate response based on the analysis results, means for transmitting the generated response to the user terminal, and means for suggesting the next content to be viewed based on the usage history and viewing history of the content distribution service. This makes it possible for the user to quickly find content to watch.

[0757] "User terminal" refers to devices used by the user, such as computers, smartphones, tablets, smart glasses, head-mounted displays, or robots.

[0758] "Inquiry content" refers to the text information of questions and requests entered by the user through their device.

[0759] "Generative artificial intelligence" refers to an AI model that analyzes user-inputted inquiries and generates appropriate responses.

[0760] "Generated answers" refer to the answers to user questions generated by generative artificial intelligence based on analysis results.

[0761] A "content distribution service" refers to an online service that provides users with digital content such as movies, television programs, documentaries, and animation.

[0762] "Usage history" refers to a record of content that a user has previously viewed or downloaded from a content distribution service.

[0763] "Viewing history" refers to a record of the content that a user has actually viewed using a content distribution service.

[0764] "The suggested methods" refer to a function that uses generative artificial intelligence to analyze a user's usage history and viewing history, and then suggests content that the user should watch next.

[0765] The system implementing this invention consists of multiple elements, including a user terminal, a server, and a generative artificial intelligence system. This system is specifically designed for content distribution services and helps users easily find the next content they should watch.

[0766] First, the user terminal consists of devices such as smartphones, smart glasses, head-mounted displays, or robots. The user enters their inquiry through these terminals. For example, they might enter a question like, "Which movie should I watch next?"

[0767] The entered query content is sent to the server. The server receives this query content and passes it on to a generative artificial intelligence for appropriate analysis. HTTPS is used as the communication protocol by the server to ensure security.

[0768] Generative artificial intelligence analyzes user inquiries based on their viewing and usage history. This analysis extracts key keywords and context from the viewing history and generates responses that suggest the most suitable content. For example, it might generate a response like, "Based on your current viewing history and favorite genres, the recommended movie is 'Inception'."

[0769] The server receives the generated response and sends it to the user's device. The user's device displays the received response on its screen. The user can then review this response on their device and select the next content to view.

[0770] The specific software used will be the OpenAI API for generative artificial intelligence, and Python for program implementation. The requests module will be used for sending and receiving data, and the HTTPS protocol will be used to ensure secure communication.

[0771] Specific example:

[0772] When a user types "What movie should I watch next?" on their smartphone,

[0773] The generative artificial intelligence analyzes the user's viewing history and generates a response such as, "Based on your current viewing history and favorite genres, the recommended movie is 'Inception'."

[0774] Example of a prompt:

[0775] User: What movie should I watch next?

[0776] Generative AI Model: Based on your current viewing history and favorite genres, the recommended movie is "Inception".

[0777] This allows users to easily discover appropriate content, thus avoiding content fatigue. Using server-side and generative artificial intelligence, users can quickly and accurately find the next content they should watch.

[0778] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0779] Step 1:

[0780] The user enters their inquiry using a terminal. For example, they might enter, "Which movie should I see next?" The input data is retrieved in text format.

[0781] Step 2:

[0782] The terminal sends the entered query content to the server. The data sent is in text format and is transmitted securely using the HTTPS protocol.

[0783] Step 3:

[0784] The server receives the inquiry content sent by the user and prepares to pass that text data to the generative artificial intelligence. The input data here is the received inquiry content in text format.

[0785] Step 4:

[0786] A generative artificial intelligence analyzes the received inquiry content and the user's viewing history data. This analysis extracts keywords from the viewing history and generates a response appropriate to the inquiry. The input is the inquiry content and viewing history data, and the output is the generated response text.

[0787] Step 5:

[0788] The server receives the generated response and sends it to the user's terminal. The transmitted data is the generated response text. The server then uses the HTTPS protocol to send the data again.

[0789] Step 6:

[0790] The user's device receives the response sent from the server and displays it on the user interface. The displayed data is the generated response text. For example, the response "Based on your current viewing history and favorite genres, the recommended movie is 'Inception'" might be displayed on the user's device.

[0791] As described above, the overall processing flow of the system is as follows: User inputs inquiry content → Terminal sends it to the server → Server receives the inquiry and passes it to the generative AI → Generative AI analyzes and generates an answer → Server sends the answer to the user terminal → User terminal displays the answer.

[0792] 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.

[0793] This invention is a system that efficiently analyzes user-inputted inquiries using generative artificial intelligence (AI) and an emotion engine, and generates appropriate responses that reflect the user's emotions. This system consists of elements such as a server, a terminal, a generative AI, and an emotion engine.

[0794] 1. User input of inquiry

[0795] First, the user enters their inquiry using their device. The device provides a user interface via a web interface or application. For example, the user might type, "How do I reset my iPhone?"

[0796] 2. Submit your inquiry.

[0797] The terminal sends the entered query content to the server. The terminal uses a communication protocol (e.g., HTTP POST request) to send the query content to the server. At this point, the query content arrives at the server in text format.

[0798] 3. Receiving queries by the server

[0799] The server receives the query content sent by the user. The server receives the request through the API endpoint and passes the query content to its internal processing.

[0800] 4. Request for emotion recognition by the server

[0801] Next, the server passes the received query to the sentiment engine and asks it to recognize the user's emotions. The sentiment engine analyzes the query text and identifies the user's emotions. For example, the sentiment engine might recognize emotions such as "frustration," "anxiety," or "confusion."

[0802] 5. Analysis using generative artificial intelligence

[0803] The server then passes the emotion recognition results to a generative artificial intelligence (AI) and requests it to analyze the inquiry. The generative AI uses natural language processing techniques to understand the main keywords and context and grasp the intent of the inquiry. For example, it might extract keywords such as "iPhone," "initialization," and "method."

[0804] 6. Generating the answer

[0805] The generative AI generates appropriate responses based on the analysis results and emotion recognition. For example, if the AI ​​detects that the user is confused, it generates a response that includes more detailed and polite instructions. Specifically, it adjusts the tone and style, such as, "How to reset your iPhone: Settings > General > Reset > Erase All Content and Settings. Please note that this operation will erase all your data."

[0806] 7. Submit your response

[0807] The server receives the response text from the generative AI and sends the response to the user's device. In this process, the server again uses a communication protocol to send the response text to the user's device.

[0808] 8. Confirmation of user responses

[0809] The user's device receives the response from the server and displays it on the user interface. For example, the user can check the initialization procedure on their smartphone or computer screen.

[0810] Specific example

[0811] The following will explain this with specific examples.

[0812] Example 1: A user asks, "How do I use Corporate Concierge?", and the emotion engine recognizes the user's frustration.

[0813] The user enters a question into their device and sends it to the server.

[0814] The server receives the question and requests analysis from the emotion engine.

[0815] The emotion engine recognizes the user's "frustration."

[0816] The server sends the emotion recognition results and the question content to the generative AI.

[0817] The generative AI generates detailed and easy-to-understand information on "how to use Corporate Concierge."

[0818] The server sends the generated information to the user.

[0819] The user's device displays the following instructions: "How to use Corporate Concierge: First, launch the app and log in. Next..."

[0820] In this way, the system of the present invention allows users to obtain the necessary information quickly and accurately, as well as receive optimal support tailored to their emotional needs. This enables companies to significantly improve the efficiency of handling inquiries and enhance user satisfaction.

[0821] The following describes the processing flow.

[0822] Step 1:

[0823] The user enters their inquiry using a device. The device provides a user interface via a web interface or application, for example, the user might type, "How do I reset my iPhone?"

[0824] Step 2:

[0825] The terminal sends the entered query content to the server. The terminal uses a communication protocol (e.g., HTTP POST request) to send the query content to the server. At this point, the query content arrives at the server in text format.

[0826] Step 3:

[0827] The server receives the query content sent by the user. The server receives the request through the API endpoint and passes the query content to its internal processing.

[0828] Step 4:

[0829] The server passes the received query content to the sentiment engine for analysis. The sentiment engine uses natural language processing techniques to analyze the query text and recognize the user's emotions. For example, the sentiment engine might identify emotions such as "frustration" or "confusion" from the query text.

[0830] Step 5:

[0831] The emotion engine returns the user's recognized emotions to the server. The server receives these emotion recognition results and prepares to request the next step of processing from the generative artificial intelligence (AI).

[0832] Step 6:

[0833] The server passes the emotion recognition results and the inquiry content to a generative artificial intelligence (AI) system, requesting analysis and response generation. The generative AI analyzes the inquiry content while considering the emotion recognition results. Specifically, it uses automated text analysis technology to understand key keywords and context.

[0834] Step 7:

[0835] Generative artificial intelligence generates appropriate responses based on analysis results and the user's emotions. For example, if the user is perceived as "frustrated," it will provide a more helpful and polite response. Specifically, it will adjust the tone and style of a response such as, "How to reset your iPhone: Settings > General > Reset > Erase All Content and Settings. Please note that this operation will erase all your data."

[0836] Step 8:

[0837] The server receives the response text generated by the generative AI and sends the response to the user's terminal. The server uses a communication protocol to send the response text to the user's terminal.

[0838] Step 9:

[0839] The device receives a response from the server and displays it on the user interface. Specifically, the initialization procedure is displayed on the user's smartphone or computer screen.

[0840] Step 10:

[0841] The user checks the response displayed on the device and performs the necessary actions according to the instructions. For example, the user may perform a factory reset of their iPhone by following the displayed instructions.

[0842] As described above, the system of the present invention can respond to user inquiries quickly and accurately, and further provide optimal support tailored to the user's emotions. This allows companies to significantly improve the efficiency of inquiry handling and increase user satisfaction.

[0843] (Example 2)

[0844] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0845] Traditional inquiry systems often fail to provide satisfactory answers because they analyze only the content of the question without considering the user's emotions. Furthermore, inappropriate answers can exacerbate dissatisfaction, especially for users experiencing frustration or confusion. Therefore, there is a need for a system that provides appropriate and effective answers tailored to the user's emotions.

[0846] The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving inquiry content entered by the user, means for identifying the emotion of the received inquiry content using emotion analysis means, means for passing the emotion information obtained from the emotion analysis means and the inquiry content to a generative artificial intelligence means, means for analyzing the inquiry content using the generative artificial intelligence means and generating an appropriate answer based on the analysis results, and means for transmitting the generated answer to the user terminal. This makes it possible to provide an appropriate and highly satisfying answer that takes the user's emotions into consideration.

[0847] A "user" refers to a person who uses the system to input inquiry details and obtain information.

[0848] "Inquiry content" refers to the text data of questions and requests that users enter through the system.

[0849] A "terminal" refers to a hardware device used by a user to input and submit inquiry details. This includes smartphones, personal computers, tablets, and other similar devices.

[0850] A "server" refers to a central computing system that receives inquiries, analyzes them, performs emotion recognition and generative artificial intelligence processing, and ultimately generates and sends answers to terminals.

[0851] "Emotional analysis means" refers to a technology or program that analyzes the text data of an input inquiry to identify the user's emotional state.

[0852] "Generative artificial intelligence" refers to artificial intelligence that uses natural language processing technology to analyze input inquiries and generate appropriate responses.

[0853] "Analysis results" refer to the understanding of the query content as analyzed by generative artificial intelligence. This includes keyword extraction and contextual understanding.

[0854] "Answer" refers to an appropriate response to a user's inquiry generated by a generative artificial intelligence system.

[0855] This invention is a system that efficiently analyzes user-inputted inquiries using generative artificial intelligence (AI) and an emotion engine, and generates appropriate responses that reflect the user's emotions. The system consists of elements such as a server, a terminal, a generative AI, and an emotion engine.

[0856] First, the user enters their inquiry using their device. The device provides a user interface via a web interface or application. Let's take the example where the user enters "How do I reset my iPhone?"

[0857] Next, the terminal sends the entered query content to the server. The terminal uses a communication protocol such as an HTTP POST request to send the query content to the server. At this point, the query content arrives at the server in text format.

[0858] The server receives the query content sent by the user. The server receives the request through the API endpoint and passes the query content to its internal processing.

[0859] The server passes the received query to the emotion engine and requests it to recognize the user's emotions. The emotion engine analyzes the query text and identifies the user's emotions. For example, the emotion engine might recognize emotions such as "frustration," "anxiety," or "confusion."

[0860] The server then passes the emotion recognition results to a generative artificial intelligence (AI) and requests it to analyze the inquiry. The generative AI uses natural language processing techniques to understand the main keywords and context and grasp the intent of the inquiry. For example, it might extract keywords such as "iPhone," "initialization," and "method."

[0861] Generative AI generates appropriate responses based on analysis results and emotion recognition. For example, if the AI ​​detects that the user is confused, it will generate a response that includes more detailed and polite instructions. Specifically, it will adjust the tone and style, such as, "How to reset your iPhone: Settings > General > Reset > Erase All Content and Settings. Please note that this operation will erase all your data."

[0862] Next, the server receives the response text from the generative AI and sends the response to the user's device. In this process, the server again uses a communication protocol to send the response text to the user's device.

[0863] Finally, the user's device receives the response from the server and displays it on the user interface. For example, the user can check the initialization procedure on their smartphone or computer screen.

[0864] The following will explain this with specific examples.

[0865] Example 1: A user asks, "How do I use Corporate Concierge?", and the emotion engine recognizes the user's frustration.

[0866] The user enters a question into their device and sends it to the server.

[0867] The server receives the question and requests analysis from the emotion engine.

[0868] The emotion engine recognizes the user's "frustration."

[0869] The server sends the emotion recognition results and the question content to the generative AI.

[0870] The generative AI generates detailed and easy-to-understand information on "how to use Corporate Concierge."

[0871] The server sends the generated information to the user.

[0872] The user's device displays the following instructions: "How to use Corporate Concierge: First, launch the app and log in. Next..."

[0873] In this way, the system of the present invention allows users to obtain the necessary information quickly and accurately, as well as receive optimal support tailored to their emotional needs. This enables companies to significantly improve the efficiency of handling inquiries and enhance user satisfaction.

[0874] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0875] Step 1:

[0876] The user enters their inquiry using a device. The device provides a user interface through a web interface or application. Specifically, the user enters "How do I reset my iPhone?" into the text input field on the device and clicks the send button. The input is stored on the device as text data.

[0877] Step 2:

[0878] The device sends the entered query content to the server. The device uses a communication protocol such as an HTTP POST request to send the query content to the server. The data sent is the query content in text format and reaches the server. Specifically, the device sends the text "Please tell me how to reset my iPhone" via an HTTP POST request.

[0879] Step 3:

[0880] The server receives the inquiry sent by the user. The server receives the request through the API endpoint and adds the inquiry to a queue for internal processing. The input is text data sent from the device and received at the server's API endpoint. Specifically, the server parses the HTTP POST request to obtain the text "How do I reset my iPhone?" and adds it to the internal processing queue.

[0881] Step 4:

[0882] The server passes the received inquiry to the emotion engine and requests it to recognize the user's emotions. The input is the text data of the received inquiry, which the emotion engine receives. The emotion engine analyzes the text data and identifies the user's emotional state. Specifically, the server sends the text "How do I reset my iPhone?" to the emotion engine, and the emotion engine recognizes emotions such as frustration or confusion.

[0883] Step 5:

[0884] The server passes the emotion recognition results to the generative artificial intelligence and requests its analysis of the inquiry. The input consists of the emotion recognition results and the text data of the inquiry, which the generative AI receives. The generative AI uses natural language processing techniques to understand the main keywords and context and grasp the intent of the inquiry. Specifically, the generative AI extracts the keywords "iPhone," "initialization," and "method."

[0885] Step 6:

[0886] The generative AI generates an appropriate response based on the analysis results and emotion recognition results. The input is the analysis results and emotion recognition results from the generative AI, and the output is the specific response text for the user. Specifically, the generative AI generates a response that includes detailed instructions, such as "How to reset your iPhone: Settings > General > Reset > Erase All Content and Settings. Please note that this operation will erase all your data."

[0887] Step 7:

[0888] The server receives the response text from the generative AI and sends the response to the user's device. The input is the response text received from the generative AI, and the output is the text data sent to the user's device. Specifically, the server sends the response text to the device as an HTTP response.

[0889] Step 8:

[0890] The user's device receives a response from the server and displays it on the user interface. The input is the response text received from the server, and the output is the information displayed on the device's screen. Specifically, the device displays the received response and shows the steps "How to reset your iPhone: Settings > General > Reset > Erase All Content and Settings..." on the screen. The user can then reset their iPhone by following the displayed steps.

[0891] (Application Example 2)

[0892] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0893] Conventional customer support systems in virtual stores have suffered from low customer satisfaction because they provide mechanical responses without considering the user's emotions. In particular, when customers are feeling frustrated or anxious, appropriate responses may not be provided, potentially damaging customer trust. This invention aims to solve this problem and provide a system that provides appropriate and detailed support tailored to the customer's emotions.

[0894] 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. In this invention, the server includes means for receiving the inquiry content entered by the user, means for generating artificial intelligence for analyzing the received inquiry content, means for generating an appropriate answer based on the analysis results, means for analyzing the user's emotions, means for adjusting the answer according to the emotion analysis results, and means for transmitting the generated answer to the user terminal. This makes it possible to provide an optimized answer according to the emotions in response to the inquiry content entered by the user.

[0895] "Means for receiving user-entered inquiry content" refers to a function that receives text-formatted inquiry content entered by the user on their device.

[0896] "Generative artificial intelligence means for analyzing received inquiry content" refers to an AI model that analyzes received text-based inquiry content using natural language processing technology and extracts key keywords and context.

[0897] "Generative artificial intelligence means for generating appropriate answers based on analysis results" refers to an AI model that automatically generates appropriate answers to inquiries based on analysis results.

[0898] "Means for analyzing user emotions" refers to a function that recognizes and identifies emotional states (e.g., frustration, impatience, confusion, etc.) from text entered by the user.

[0899] "Means of adjusting responses according to sentiment analysis results" refers to a function that appropriately adjusts the tone and details of responses according to sentiment analysis results, thereby generating responses that take the user's emotions into consideration.

[0900] "Means for sending the generated response to the user's terminal" refers to a function that sends the generated response to the user's terminal using a communication protocol, allowing the user to confirm it.

[0901] This invention is applied to customer support systems in virtual stores. The system efficiently analyzes user inquiries and utilizes generative artificial intelligence (AI) and an emotion engine to generate appropriate responses tailored to the user's emotions. The system is configured as follows:

[0902] First, the user enters their inquiry using their device. The device provides the functionality to enter and send the inquiry via a web interface or application. For example, if the user enters "How do I return an item at the virtual store?", the device uses a communication protocol (e.g., an HTTP POST request) to send the inquiry to the server.

[0903] The server passes the received inquiry to the emotion engine, which analyzes the user's emotions. The emotion engine analyzes the inquiry text and identifies emotional states such as "frustration," "anxiety," or "confusion." The server then passes these emotion analysis results to the generative AI, which uses natural language processing techniques to understand key keywords and context, and grasp the intent of the inquiry.

[0904] Generative AI generates appropriate answers based on analysis results and emotion recognition. For example, if the AI ​​detects that the user is confused, it will generate an answer that includes detailed and polite instructions. A specific prompt might be: "The user asked this question while feeling frustrated: I don't know the size of this dress. Answer:" In this way, the tone and content of the answer are adjusted according to the user's emotions.

[0905] The generated answers are sent from the server to the user's device and displayed in the user interface. Users can check the answers on their own devices and obtain specific instructions and information. For example, if a user asks, "How do I return an item in the virtual store?", detailed return instructions will be displayed in an emotionally sensitive tone. If a user asks in frustration, "I don't know the size of this dress," they will receive a detailed answer such as, "The size of this dress is as follows: Size S: Shoulder width 34cm, bust 76cm, waist 60cm..."

[0906] The system uses the following hardware and software: a server (such as Amazon Web Services (AWS) EC2), the sentiment-analysis model from the transformers library for sentiment recognition, and GPT-2 for generative AI models. This enables the system to provide responses optimized according to the emotions expressed in user inquiries.

[0907] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0908] Step 1:

[0909] The user enters their inquiry on their device. The user uses a web interface or application to enter an inquiry, such as "How do I return an item at a virtual store?". The entered inquiry is stored on the device as text data.

[0910] Step 2:

[0911] The terminal sends the entered query content to the server. The terminal uses a communication protocol (e.g., HTTP POST request) to send the query content in text format to the server. The input data is sent to the endpoint and reaches the server.

[0912] Step 3:

[0913] The server passes the received query content to the emotion engine, which analyzes the user's emotions. The emotion engine analyzes the query text and identifies emotional states such as "frustration," "anxiety," or "confusion." The emotion engine takes text data as input and outputs emotional states.

[0914] Step 4:

[0915] The server passes the emotion recognition results to a generative artificial intelligence (AI) and requests its analysis of the inquiry. The generative AI uses natural language processing techniques to understand key keywords and context, grasping the intent of the inquiry. For example, it extracts keywords such as "virtual store," "how to return items," and "tell me." The analysis data, along with the emotion recognition results, is then passed to the generative AI.

[0916] Step 5:

[0917] The generative AI generates an appropriate response based on the analysis results and sentiment recognition results. The generative AI generates responses using prompt sentences. For example, it takes a prompt sentence such as "The user asked this question while frustrated: How do I return an item in a virtual store? Answer:" as input and outputs a response such as "The return procedure is as follows: Please prepare proof of purchase first, according to the return policy..."

[0918] Step 6:

[0919] The server receives the generated response and sends it to the user's device. The server uses a communication protocol (e.g., HTTP POST request) to send the generated response text to the user's device. The response data is sent to the endpoint and reaches the user's device.

[0920] Step 7:

[0921] The user's device receives the response from the server and displays it in the user interface. The response text is displayed in the device's UI component for the user to review. Specifically, detailed instructions such as, "The return procedure is as follows: Please follow our return policy and first prepare proof of purchase..." are displayed.

[0922] 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.

[0923] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0924] 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.

[0925] [Fourth Embodiment]

[0926] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0927] 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.

[0928] 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).

[0929] 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.

[0930] 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.

[0931] 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).

[0932] 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.

[0933] 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.

[0934] 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.

[0935] 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.

[0936] 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.

[0937] 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.

[0938] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0939] This invention relates to a system that efficiently processes user-inputted inquiries using generative artificial intelligence (AI) and provides appropriate answers quickly. This system consists of multiple elements, including a server, a terminal, and generative artificial intelligence.

[0940] 1. User input of inquiry

[0941] First, the user enters their inquiry using a device. This device operates via a web interface or application. For example, the user might type, "How do I reset my iPhone?"

[0942] 2. Submit your inquiry.

[0943] The terminal sends the entered query content to the server. The server receives the query content using a communication protocol and prepares to perform appropriate analysis.

[0944] 3. Receiving queries by the server

[0945] The server receives the content of inquiries sent by users and passes that content to a generative artificial intelligence system. The server is required to efficiently process this reception and analysis request process.

[0946] 4. Analysis using generative artificial intelligence

[0947] Next, a generative artificial intelligence analyzes the received inquiry. The AI ​​uses text analysis techniques to identify key keywords and context. For example, it extracts keywords such as "iPhone," "initialization," and "method."

[0948] 5. Generating the answer

[0949] The generative artificial intelligence generates an appropriate answer based on the analysis results. Specifically, for example, it might generate the procedure "How to reset your iPhone: Settings > General > Reset > Erase All Content and Settings".

[0950] 6. Submit your response

[0951] The server receives the response from the generative artificial intelligence and sends the response to the user's terminal. In this process, the server again uses a communication protocol to send the response text.

[0952] 7. Receiving and confirming user responses

[0953] Ultimately, the user's device receives the response from the server and displays it on the user interface. For example, the user can check the initialization procedure on their smartphone or computer screen.

[0954] Specific example

[0955] Specific examples are given below.

[0956] Example 1: A user asks, "How do I use Corporate Concierge?"

[0957] The user enters a question into their device and sends it to the server.

[0958] The server receives the question and requests analysis from a generative artificial intelligence.

[0959] Generative artificial intelligence generates information on "how to use corporate concierge services."

[0960] The server sends the generated information to the user.

[0961] The user's device displays the following instructions: "How to use Corporate Concierge: First, launch the app and log in. Next..."

[0962] In this way, the system of the present invention allows users to obtain the necessary information quickly and accurately. Furthermore, companies can significantly improve the efficiency of responding to inquiries.

[0963] The above describes specific embodiments for carrying out the present invention. This will enable users to easily utilize the system and receive appropriate support.

[0964] The following describes the processing flow.

[0965] Step 1:

[0966] The user enters their inquiry using a device. The device provides a user interface via a web interface or application. For example, the user might type, "How do I reset my iPhone?"

[0967] Step 2:

[0968] The terminal sends the entered query content to the server. The terminal uses a communication protocol (e.g., HTTP POST request) to send the query content to the server. At this point, the query content arrives at the server in text format.

[0969] Step 3:

[0970] The server receives the query content sent by the user. The server receives the request through the API endpoint and passes the query content to its internal processing.

[0971] Step 4:

[0972] The server requests a generative artificial intelligence (AI) to analyze the received inquiry. The server calls the generative AI's API and sends the inquiry text to analyze the inquiry.

[0973] Step 5:

[0974] The generative AI analyzes the received inquiry. Using natural language processing techniques, the generative AI analyzes key keywords and context to understand the intent of the inquiry. For example, it extracts keywords such as "iPhone," "initialization," and "method."

[0975] Step 6:

[0976] The generative AI generates appropriate answers based on the analysis results. The AI ​​generates specific operating procedures and information in text format according to the content of the inquiry. For example, it might generate the procedure: "How to reset your iPhone: Settings > General > Reset > Erase All Content and Settings".

[0977] Step 7:

[0978] The server receives the response text from the generative AI. The server receives the generated response text and prepares to send it to the user's terminal.

[0979] Step 8:

[0980] The server sends the generated response to the user's device. The server then uses the communication protocol again to send the response text to the user's device.

[0981] Step 9:

[0982] The user's device receives a response from the server. The device displays the received response text on the user interface. For example, the initialization procedure is displayed on the user's smartphone or computer screen.

[0983] Step 10:

[0984] The user checks the response displayed on the device and performs the necessary actions according to the instructions. For example, the user may perform a factory reset of their iPhone by following the displayed instructions.

[0985] As described above, the system of the present invention responds quickly and accurately to user inquiries, improving user convenience. This allows companies to significantly improve the efficiency of their inquiry handling.

[0986] (Example 1)

[0987] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0988] Many modern systems suffer from delays in providing responses to user inquiries and incur significant manual workloads. There is a need to solve this problem and create a system that can respond to user inquiries quickly and accurately.

[0989] 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.

[0990] In this invention, the server includes means for receiving the content of an inquiry entered by the user, means for passing the received content of the inquiry to a generative artificial intelligence, means for the generative artificial intelligence to analyze the received content and identify key keywords and context, means for the generative artificial intelligence to generate an appropriate answer based on the analysis results, means for sending the generated answer to the user terminal, and means for the user terminal to receive the answer from the server and display it on the user interface. This enables the user to obtain the necessary information quickly and accurately.

[0991] A "user" refers to an end-user who enters an inquiry into the system and receives a response.

[0992] A "terminal" refers to a device such as a computer or smartphone that a user uses to input their inquiry and receive a response from a server.

[0993] A "server" refers to a computing system that receives user inquiries, forwards them to a generative artificial intelligence system, and generates and sends responses.

[0994] "Generative artificial intelligence" refers to an AI model equipped with natural language processing that analyzes user inquiries and generates appropriate responses.

[0995] "Means of receiving" refers to the function that allows the server to receive the content of inquiries sent by the user.

[0996] "Means of transferring to generative artificial intelligence" refers to the function that allows the server to transfer the content of the inquiry it receives to the generative artificial intelligence.

[0997] "Means of analysis" refers to the functions that generative artificial intelligence uses to analyze the content of inquiries and identify key keywords and context.

[0998] "Means for generating answers" refers to the function that allows generative artificial intelligence to come up with appropriate answers based on analysis results.

[0999] "Means of transmission" refers to the function that allows the server to send the generated response to the user's terminal.

[1000] "User interface" refers to the screen display or application interface that users use to input inquiries or view received responses.

[1001] This invention relates to a system that efficiently processes user-inputted inquiries using generative artificial intelligence and provides appropriate answers quickly. This system consists of multiple elements, including a server, a terminal, and generative artificial intelligence.

[1002] First, the user enters their inquiry using a device. This device operates via a web interface or application. For example, the user might enter "How do I reset my iPhone?". This inquiry is sent from the device to the server using the HTTP / HTTPS communication protocol.

[1003] Next, the server receives the query content sent by the user. The server analyzes the received query content and prepares it for passing on to the generative artificial intelligence described later. The receiving terminal can also temporarily store the received content in a database.

[1004] The server then passes the query to a generative artificial intelligence (AI). The AI ​​uses NLP (Natural Language Processing) techniques to analyze the query. This analysis process identifies key keywords and context, and generates an appropriate response based on them. For example, it might extract keywords such as "iPhone," "initialization," and "method."

[1005] The generative artificial intelligence generates an appropriate answer based on the analysis results. Specifically, it generates the procedure "How to reset your iPhone: Settings > General > Reset > Erase All Content and Settings". This generation process is performed using a generative artificial intelligence model and prompt statements.

[1006] The generated response is sent back to the server, which then transmits the response data to the user's device. The communication protocol used is again HTTP / HTTPS. This data transfer process is required to be secure and fast.

[1007] Finally, the user's device receives the response sent from the server and displays it on the user interface. The user can then review the initialization procedure on their smartphone or computer screen.

[1008] As a concrete example, let's consider a scenario where a user asks, "How do I use Corporate Concierge?" In this case, the user types "How do I use Corporate Concierge?" into their device and sends it to the server. The server receives the question and requests analysis from a generative artificial intelligence. The generative AI generates information about "how to use Corporate Concierge," and the server sends this information to the user. Then, the user's device displays instructions such as "How to use Corporate Concierge: First, launch the app and log in. Next..."

[1009] In this way, the system of the present invention allows users to obtain the necessary information quickly and accurately. Furthermore, by using this system, companies can significantly improve the efficiency of responding to inquiries.

[1010] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1011] Step 1: User enters inquiry

[1012] The user enters their inquiry details using a terminal. This terminal operates via a web interface or application.

[1013] Input: The user enters "How do I reset my iPhone?" into the text input box on the device.

[1014] Output: Stored as text data in the terminal's memory.

[1015] Specific action: The user types text using the keyboard and presses the send button.

[1016] Step 2: Submit your inquiry

[1017] The terminal sends the entered query content to the server. The terminal does this using an HTTP POST request.

[1018] Input: Text data entered by the user.

[1019] Output: The HTTP request sent to the server.

[1020] Specific operation: The terminal stores the query text in the body of the HTTP request and sends it to the specified server endpoint.

[1021] Step 3: The server receives the query.

[1022] The server receives the query content sent by the user. The received content is temporarily stored in the server's memory and then saved to the database as needed.

[1023] Input: HTTP request sent from the terminal.

[1024] Output: The query content stored in the server's memory or database.

[1025] Specific operation: The server receives the request, extracts the query content from the request body, and stores it temporarily.

[1026] Step 4: Transferring the received content to the generational artificial intelligence system.

[1027] The server passes the received query details to the generative artificial intelligence. An API request is then made to the generative artificial intelligence.

[1028] Input: The query content stored on the server.

[1029] Output: API request to a generative artificial intelligence system.

[1030] Specific operation: The server converts the query content into an API request format and sends it to the generative artificial intelligence endpoint.

[1031] Step 5: Analysis using generative artificial intelligence

[1032] Generative artificial intelligence analyzes the content of the received inquiry. NLP techniques are used to identify key keywords and context.

[1033] Input: The content of the inquiry sent from the server.

[1034] Output: Keywords and contextual information as analysis results.

[1035] Specific operation: The generative artificial intelligence tokenizes the query content and performs principal component analysis and contextual analysis.

[1036] Step 6: Generating the answer

[1037] Generative artificial intelligence generates appropriate answers based on the analysis results. For example, it might generate the procedure: "How to reset your iPhone: Settings > General > Reset > Erase All Content and Settings".

[1038] Input: Keywords and contextual information as analysis results.

[1039] Output: Answer text.

[1040] Specific operation: Generative artificial intelligence searches for appropriate information from a knowledge base and generates an answer using a text generation algorithm.

[1041] Step 7: Submit your response

[1042] The server receives a response from a generative artificial intelligence and sends it to the user's terminal. HTTP responses are used.

[1043] Input: Text response from a generative artificial intelligence.

[1044] Output: The HTTP response sent to the user's terminal.

[1045] Specific operation: The server stores the response text in the body of the HTTP response and sends it to the user's terminal.

[1046] Step 8: Receiving and confirming the user's response

[1047] The user's device receives the response from the server and displays it on the user interface.

[1048] Input: HTTP response sent from the server.

[1049] Output: The answer displayed on the device's screen.

[1050] Specific operation: The terminal receives an HTTP response, updates the corresponding part of the interface, and displays the answer.

[1051] (Application Example 1)

[1052] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1053] Online content distribution services have faced problems such as users easily becoming confused when trying to find the next content to watch, and taking a long time to get appropriate answers. Therefore, there is a need for a system that can quickly and accurately suggest the most suitable content based on viewing and usage history.

[1054] 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.

[1055] In this invention, the server includes means for receiving inquiry content entered by the user, means for generative artificial intelligence for analyzing the received inquiry content, means for generative artificial intelligence for generating an appropriate response based on the analysis results, means for transmitting the generated response to the user terminal, and means for suggesting the next content to be viewed based on the usage history and viewing history of the content distribution service. This makes it possible for the user to quickly find content to watch.

[1056] "User terminal" refers to devices used by the user, such as computers, smartphones, tablets, smart glasses, head-mounted displays, or robots.

[1057] "Inquiry content" refers to the text information of questions and requests entered by the user through their device.

[1058] "Generative artificial intelligence" refers to an AI model that analyzes user-inputted inquiries and generates appropriate responses.

[1059] "Generated answers" refer to the answers to user questions generated by generative artificial intelligence based on analysis results.

[1060] A "content distribution service" refers to an online service that provides users with digital content such as movies, television programs, documentaries, and animation.

[1061] "Usage history" refers to a record of content that a user has previously viewed or downloaded from a content distribution service.

[1062] "Viewing history" refers to a record of the content that a user has actually viewed using a content distribution service.

[1063] "The suggested methods" refer to a function that uses generative artificial intelligence to analyze a user's usage history and viewing history, and then suggests content that the user should watch next.

[1064] The system implementing this invention consists of multiple elements, including a user terminal, a server, and a generative artificial intelligence system. This system is specifically designed for content distribution services and helps users easily find the next content they should watch.

[1065] First, the user terminal consists of devices such as smartphones, smart glasses, head-mounted displays, or robots. The user enters their inquiry through these terminals. For example, they might enter a question like, "Which movie should I watch next?"

[1066] The entered query content is sent to the server. The server receives this query content and passes it on to a generative artificial intelligence for appropriate analysis. HTTPS is used as the communication protocol by the server to ensure security.

[1067] Generative artificial intelligence analyzes user inquiries based on their viewing and usage history. This analysis extracts key keywords and context from the viewing history and generates responses that suggest the most suitable content. For example, it might generate a response like, "Based on your current viewing history and favorite genres, the recommended movie is 'Inception'."

[1068] The server receives the generated response and sends it to the user's device. The user's device displays the received response on its screen. The user can then review this response on their device and select the next content to view.

[1069] The specific software used will be the OpenAI API for generative artificial intelligence, and Python for program implementation. The requests module will be used for sending and receiving data, and the HTTPS protocol will be used to ensure secure communication.

[1070] Specific example:

[1071] When a user types "What movie should I watch next?" on their smartphone,

[1072] The generative artificial intelligence analyzes the user's viewing history and generates a response such as, "Based on your current viewing history and favorite genres, the recommended movie is 'Inception'."

[1073] Example of a prompt:

[1074] User: What movie should I watch next?

[1075] Generative AI Model: Based on your current viewing history and favorite genres, the recommended movie is "Inception".

[1076] This allows users to easily discover appropriate content, thus avoiding content fatigue. Using server-side and generative artificial intelligence, users can quickly and accurately find the next content they should watch.

[1077] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1078] Step 1:

[1079] The user enters their inquiry using a terminal. For example, they might enter, "Which movie should I see next?" The input data is retrieved in text format.

[1080] Step 2:

[1081] The terminal sends the entered query content to the server. The data sent is in text format and is transmitted securely using the HTTPS protocol.

[1082] Step 3:

[1083] The server receives the inquiry content sent by the user and prepares to pass that text data to the generative artificial intelligence. The input data here is the received inquiry content in text format.

[1084] Step 4:

[1085] A generative artificial intelligence analyzes the received inquiry content and the user's viewing history data. This analysis extracts keywords from the viewing history and generates a response appropriate to the inquiry. The input is the inquiry content and viewing history data, and the output is the generated response text.

[1086] Step 5:

[1087] The server receives the generated response and sends it to the user's terminal. The transmitted data is the generated response text. The server then uses the HTTPS protocol to send the data again.

[1088] Step 6:

[1089] The user's device receives the response sent from the server and displays it on the user interface. The displayed data is the generated response text. For example, the response "Based on your current viewing history and favorite genres, the recommended movie is 'Inception'" might be displayed on the user's device.

[1090] As described above, the overall processing flow of the system is as follows: User inputs inquiry content → Terminal sends it to the server → Server receives the inquiry and passes it to the generative AI → Generative AI analyzes and generates an answer → Server sends the answer to the user terminal → User terminal displays the answer.

[1091] 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.

[1092] This invention is a system that efficiently analyzes user-inputted inquiries using generative artificial intelligence (AI) and an emotion engine, and generates appropriate responses that reflect the user's emotions. This system consists of elements such as a server, a terminal, a generative AI, and an emotion engine.

[1093] 1. User input of inquiry

[1094] First, the user enters their inquiry using their device. The device provides a user interface via a web interface or application. For example, the user might type, "How do I reset my iPhone?"

[1095] 2. Submit your inquiry.

[1096] The terminal sends the entered query content to the server. The terminal uses a communication protocol (e.g., HTTP POST request) to send the query content to the server. At this point, the query content arrives at the server in text format.

[1097] 3. Receiving queries by the server

[1098] The server receives the query content sent by the user. The server receives the request through the API endpoint and passes the query content to its internal processing.

[1099] 4. Request for emotion recognition by the server

[1100] Next, the server passes the received query to the sentiment engine and asks it to recognize the user's emotions. The sentiment engine analyzes the query text and identifies the user's emotions. For example, the sentiment engine might recognize emotions such as "frustration," "anxiety," or "confusion."

[1101] 5. Analysis using generative artificial intelligence

[1102] The server then passes the emotion recognition results to a generative artificial intelligence (AI) and requests it to analyze the inquiry. The generative AI uses natural language processing techniques to understand the main keywords and context and grasp the intent of the inquiry. For example, it might extract keywords such as "iPhone," "initialization," and "method."

[1103] 6. Generating the answer

[1104] The generative AI generates appropriate responses based on the analysis results and emotion recognition. For example, if the AI ​​detects that the user is confused, it generates a response that includes more detailed and polite instructions. Specifically, it adjusts the tone and style, such as, "How to reset your iPhone: Settings > General > Reset > Erase All Content and Settings. Please note that this operation will erase all your data."

[1105] 7. Submit your response

[1106] The server receives the response text from the generative AI and sends the response to the user's device. In this process, the server again uses a communication protocol to send the response text to the user's device.

[1107] 8. Confirmation of user responses

[1108] The user's device receives the response from the server and displays it on the user interface. For example, the user can check the initialization procedure on their smartphone or computer screen.

[1109] Specific example

[1110] The following will explain this with specific examples.

[1111] Example 1: A user asks, "How do I use Corporate Concierge?", and the emotion engine recognizes the user's frustration.

[1112] The user enters a question into their device and sends it to the server.

[1113] The server receives the question and requests analysis from the emotion engine.

[1114] The emotion engine recognizes the user's "frustration."

[1115] The server sends the emotion recognition results and the question content to the generative AI.

[1116] The generative AI generates detailed and easy-to-understand information on "how to use Corporate Concierge."

[1117] The server sends the generated information to the user.

[1118] The user's device displays the following instructions: "How to use Corporate Concierge: First, launch the app and log in. Next..."

[1119] In this way, the system of the present invention allows users to obtain the necessary information quickly and accurately, as well as receive optimal support tailored to their emotional needs. This enables companies to significantly improve the efficiency of handling inquiries and enhance user satisfaction.

[1120] The following describes the processing flow.

[1121] Step 1:

[1122] The user enters their inquiry using a device. The device provides a user interface via a web interface or application, for example, the user might type, "How do I reset my iPhone?"

[1123] Step 2:

[1124] The terminal sends the entered query content to the server. The terminal uses a communication protocol (e.g., HTTP POST request) to send the query content to the server. At this point, the query content arrives at the server in text format.

[1125] Step 3:

[1126] The server receives the query content sent by the user. The server receives the request through the API endpoint and passes the query content to its internal processing.

[1127] Step 4:

[1128] The server passes the received query content to the sentiment engine for analysis. The sentiment engine uses natural language processing techniques to analyze the query text and recognize the user's emotions. For example, the sentiment engine might identify emotions such as "frustration" or "confusion" from the query text.

[1129] Step 5:

[1130] The emotion engine returns the user's recognized emotions to the server. The server receives these emotion recognition results and prepares to request the next step of processing from the generative artificial intelligence (AI).

[1131] Step 6:

[1132] The server passes the emotion recognition results and the inquiry content to a generative artificial intelligence (AI) system, requesting analysis and response generation. The generative AI analyzes the inquiry content while considering the emotion recognition results. Specifically, it uses automated text analysis technology to understand key keywords and context.

[1133] Step 7:

[1134] Generative artificial intelligence generates appropriate responses based on analysis results and the user's emotions. For example, if the user is perceived as "frustrated," it will provide a more helpful and polite response. Specifically, it will adjust the tone and style of a response such as, "How to reset your iPhone: Settings > General > Reset > Erase All Content and Settings. Please note that this operation will erase all your data."

[1135] Step 8:

[1136] The server receives the response text generated by the generative AI and sends the response to the user's terminal. The server uses a communication protocol to send the response text to the user's terminal.

[1137] Step 9:

[1138] The device receives a response from the server and displays it on the user interface. Specifically, the initialization procedure is displayed on the user's smartphone or computer screen.

[1139] Step 10:

[1140] The user checks the response displayed on the device and performs the necessary actions according to the instructions. For example, the user may perform a factory reset of their iPhone by following the displayed instructions.

[1141] As described above, the system of the present invention can respond to user inquiries quickly and accurately, and further provide optimal support tailored to the user's emotions. This allows companies to significantly improve the efficiency of inquiry handling and increase user satisfaction.

[1142] (Example 2)

[1143] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1144] Traditional inquiry systems often fail to provide satisfactory answers because they analyze only the content of the question without considering the user's emotions. Furthermore, inappropriate answers can exacerbate dissatisfaction, especially for users experiencing frustration or confusion. Therefore, there is a need for a system that provides appropriate and effective answers tailored to the user's emotions.

[1145] The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving inquiry content entered by the user, means for identifying the emotion of the received inquiry content using emotion analysis means, means for passing the emotion information obtained from the emotion analysis means and the inquiry content to a generative artificial intelligence means, means for analyzing the inquiry content using the generative artificial intelligence means and generating an appropriate answer based on the analysis results, and means for transmitting the generated answer to the user terminal. This makes it possible to provide an appropriate and highly satisfying answer that takes the user's emotions into consideration.

[1146] A "user" refers to a person who uses the system to input inquiry details and obtain information.

[1147] "Inquiry content" refers to the text data of questions and requests that users enter through the system.

[1148] A "terminal" refers to a hardware device used by a user to input and submit inquiry details. This includes smartphones, personal computers, tablets, and other similar devices.

[1149] A "server" refers to a central computing system that receives inquiries, analyzes them, performs emotion recognition and generative artificial intelligence processing, and ultimately generates and sends answers to terminals.

[1150] "Emotional analysis means" refers to a technology or program that analyzes the text data of an input inquiry to identify the user's emotional state.

[1151] "Generative artificial intelligence" refers to artificial intelligence that uses natural language processing technology to analyze input inquiries and generate appropriate responses.

[1152] "Analysis results" refer to the understanding of the query content as analyzed by generative artificial intelligence. This includes keyword extraction and contextual understanding.

[1153] "Answer" refers to an appropriate response to a user's inquiry generated by a generative artificial intelligence system.

[1154] This invention is a system that efficiently analyzes user-inputted inquiries using generative artificial intelligence (AI) and an emotion engine, and generates appropriate responses that reflect the user's emotions. The system consists of elements such as a server, a terminal, a generative AI, and an emotion engine.

[1155] First, the user enters their inquiry using their device. The device provides a user interface via a web interface or application. Let's take the example where the user enters "How do I reset my iPhone?"

[1156] Next, the terminal sends the entered query content to the server. The terminal uses a communication protocol such as an HTTP POST request to send the query content to the server. At this point, the query content arrives at the server in text format.

[1157] The server receives the query content sent by the user. The server receives the request through the API endpoint and passes the query content to its internal processing.

[1158] The server passes the received query to the emotion engine and requests it to recognize the user's emotions. The emotion engine analyzes the query text and identifies the user's emotions. For example, the emotion engine might recognize emotions such as "frustration," "anxiety," or "confusion."

[1159] The server then passes the emotion recognition results to a generative artificial intelligence (AI) and requests it to analyze the inquiry. The generative AI uses natural language processing techniques to understand the main keywords and context and grasp the intent of the inquiry. For example, it might extract keywords such as "iPhone," "initialization," and "method."

[1160] Generative AI generates appropriate responses based on analysis results and emotion recognition. For example, if the AI ​​detects that the user is confused, it will generate a response that includes more detailed and polite instructions. Specifically, it will adjust the tone and style, such as, "How to reset your iPhone: Settings > General > Reset > Erase All Content and Settings. Please note that this operation will erase all your data."

[1161] Next, the server receives the response text from the generative AI and sends the response to the user's device. In this process, the server again uses a communication protocol to send the response text to the user's device.

[1162] Finally, the user's device receives the response from the server and displays it on the user interface. For example, the user can check the initialization procedure on their smartphone or computer screen.

[1163] The following will explain this with specific examples.

[1164] Example 1: A user asks, "How do I use Corporate Concierge?", and the emotion engine recognizes the user's frustration.

[1165] The user enters a question into their device and sends it to the server.

[1166] The server receives the question and requests analysis from the emotion engine.

[1167] The emotion engine recognizes the user's "frustration."

[1168] The server sends the emotion recognition results and the question content to the generative AI.

[1169] The generative AI generates detailed and easy-to-understand information on "how to use Corporate Concierge."

[1170] The server sends the generated information to the user.

[1171] The user's device displays the following instructions: "How to use Corporate Concierge: First, launch the app and log in. Next..."

[1172] In this way, the system of the present invention allows users to obtain the necessary information quickly and accurately, as well as receive optimal support tailored to their emotional needs. This enables companies to significantly improve the efficiency of handling inquiries and enhance user satisfaction.

[1173] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1174] Step 1:

[1175] The user enters their inquiry using a device. The device provides a user interface through a web interface or application. Specifically, the user enters "How do I reset my iPhone?" into the text input field on the device and clicks the send button. The input is stored on the device as text data.

[1176] Step 2:

[1177] The device sends the entered query content to the server. The device uses a communication protocol such as an HTTP POST request to send the query content to the server. The data sent is the query content in text format and reaches the server. Specifically, the device sends the text "Please tell me how to reset my iPhone" via an HTTP POST request.

[1178] Step 3:

[1179] The server receives the inquiry sent by the user. The server receives the request through the API endpoint and adds the inquiry to a queue for internal processing. The input is text data sent from the device and received at the server's API endpoint. Specifically, the server parses the HTTP POST request to obtain the text "How do I reset my iPhone?" and adds it to the internal processing queue.

[1180] Step 4:

[1181] The server passes the received inquiry to the emotion engine and requests it to recognize the user's emotions. The input is the text data of the received inquiry, which the emotion engine receives. The emotion engine analyzes the text data and identifies the user's emotional state. Specifically, the server sends the text "How do I reset my iPhone?" to the emotion engine, and the emotion engine recognizes emotions such as frustration or confusion.

[1182] Step 5:

[1183] The server passes the emotion recognition results to the generative artificial intelligence and requests its analysis of the inquiry. The input consists of the emotion recognition results and the text data of the inquiry, which the generative AI receives. The generative AI uses natural language processing techniques to understand the main keywords and context and grasp the intent of the inquiry. Specifically, the generative AI extracts the keywords "iPhone," "initialization," and "method."

[1184] Step 6:

[1185] The generative AI generates an appropriate response based on the analysis results and emotion recognition results. The input is the analysis results and emotion recognition results from the generative AI, and the output is the specific response text for the user. Specifically, the generative AI generates a response that includes detailed instructions, such as "How to reset your iPhone: Settings > General > Reset > Erase All Content and Settings. Please note that this operation will erase all your data."

[1186] Step 7:

[1187] The server receives the response text from the generative AI and sends the response to the user's device. The input is the response text received from the generative AI, and the output is the text data sent to the user's device. Specifically, the server sends the response text to the device as an HTTP response.

[1188] Step 8:

[1189] The user's device receives a response from the server and displays it on the user interface. The input is the response text received from the server, and the output is the information displayed on the device's screen. Specifically, the device displays the received response and shows the steps "How to reset your iPhone: Settings > General > Reset > Erase All Content and Settings..." on the screen. The user can then reset their iPhone by following the displayed steps.

[1190] (Application Example 2)

[1191] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1192] Conventional customer support systems in virtual stores have suffered from low customer satisfaction because they provide mechanical responses without considering the user's emotions. In particular, when customers are feeling frustrated or anxious, appropriate responses may not be provided, potentially damaging customer trust. This invention aims to solve this problem and provide a system that provides appropriate and detailed support tailored to the customer's emotions.

[1193] 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. In this invention, the server includes means for receiving the inquiry content entered by the user, means for generating artificial intelligence for analyzing the received inquiry content, means for generating an appropriate answer based on the analysis results, means for analyzing the user's emotions, means for adjusting the answer according to the emotion analysis results, and means for transmitting the generated answer to the user terminal. This makes it possible to provide an optimized answer according to the emotions in response to the inquiry content entered by the user.

[1194] "Means for receiving user-entered inquiry content" refers to a function that receives text-formatted inquiry content entered by the user on their device.

[1195] "Generative artificial intelligence means for analyzing received inquiry content" refers to an AI model that analyzes received text-based inquiry content using natural language processing technology and extracts key keywords and context.

[1196] "Generative artificial intelligence means for generating appropriate answers based on analysis results" refers to an AI model that automatically generates appropriate answers to inquiries based on analysis results.

[1197] "Means for analyzing user emotions" refers to a function that recognizes and identifies emotional states (e.g., frustration, impatience, confusion, etc.) from text entered by the user.

[1198] "Means of adjusting responses according to sentiment analysis results" refers to a function that appropriately adjusts the tone and details of responses according to sentiment analysis results, thereby generating responses that take the user's emotions into consideration.

[1199] "Means for sending the generated response to the user's terminal" refers to a function that sends the generated response to the user's terminal using a communication protocol, allowing the user to confirm it.

[1200] This invention is applied to customer support systems in virtual stores. The system efficiently analyzes user inquiries and utilizes generative artificial intelligence (AI) and an emotion engine to generate appropriate responses tailored to the user's emotions. The system is configured as follows:

[1201] First, the user enters their inquiry using their device. The device provides the functionality to enter and send the inquiry via a web interface or application. For example, if the user enters "How do I return an item at the virtual store?", the device uses a communication protocol (e.g., an HTTP POST request) to send the inquiry to the server.

[1202] The server passes the received inquiry to the emotion engine, which analyzes the user's emotions. The emotion engine analyzes the inquiry text and identifies emotional states such as "frustration," "anxiety," or "confusion." The server then passes these emotion analysis results to the generative AI, which uses natural language processing techniques to understand key keywords and context, and grasp the intent of the inquiry.

[1203] Generative AI generates appropriate answers based on analysis results and emotion recognition. For example, if the AI ​​detects that the user is confused, it will generate an answer that includes detailed and polite instructions. A specific prompt might be: "The user asked this question while feeling frustrated: I don't know the size of this dress. Answer:" In this way, the tone and content of the answer are adjusted according to the user's emotions.

[1204] The generated answers are sent from the server to the user's device and displayed in the user interface. Users can check the answers on their own devices and obtain specific instructions and information. For example, if a user asks, "How do I return an item in the virtual store?", detailed return instructions will be displayed in an emotionally sensitive tone. If a user asks in frustration, "I don't know the size of this dress," they will receive a detailed answer such as, "The size of this dress is as follows: Size S: Shoulder width 34cm, bust 76cm, waist 60cm..."

[1205] The system uses the following hardware and software: a server (such as Amazon Web Services (AWS) EC2), the sentiment-analysis model from the transformers library for sentiment recognition, and GPT-2 for generative AI models. This enables the system to provide responses optimized according to the emotions expressed in user inquiries.

[1206] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1207] Step 1:

[1208] The user enters their inquiry on their device. The user uses a web interface or application to enter an inquiry, such as "How do I return an item at a virtual store?". The entered inquiry is stored on the device as text data.

[1209] Step 2:

[1210] The terminal sends the entered query content to the server. The terminal uses a communication protocol (e.g., HTTP POST request) to send the query content in text format to the server. The input data is sent to the endpoint and reaches the server.

[1211] Step 3:

[1212] The server passes the received query content to the emotion engine, which analyzes the user's emotions. The emotion engine analyzes the query text and identifies emotional states such as "frustration," "anxiety," or "confusion." The emotion engine takes text data as input and outputs emotional states.

[1213] Step 4:

[1214] The server passes the emotion recognition results to a generative artificial intelligence (AI) and requests its analysis of the inquiry. The generative AI uses natural language processing techniques to understand key keywords and context, grasping the intent of the inquiry. For example, it extracts keywords such as "virtual store," "how to return items," and "tell me." The analysis data, along with the emotion recognition results, is then passed to the generative AI.

[1215] Step 5:

[1216] The generative AI generates an appropriate response based on the analysis results and sentiment recognition results. The generative AI generates responses using prompt sentences. For example, it takes a prompt sentence such as "The user asked this question while frustrated: How do I return an item in a virtual store? Answer:" as input and outputs a response such as "The return procedure is as follows: Please prepare proof of purchase first, according to the return policy..."

[1217] Step 6:

[1218] The server receives the generated response and sends it to the user's device. The server uses a communication protocol (e.g., HTTP POST request) to send the generated response text to the user's device. The response data is sent to the endpoint and reaches the user's device.

[1219] Step 7:

[1220] The user's device receives the response from the server and displays it in the user interface. The response text is displayed in the device's UI component for the user to review. Specifically, detailed instructions such as, "The return procedure is as follows: Please follow our return policy and first prepare proof of purchase..." are displayed.

[1221] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1222] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1223] 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 robot 414.

[1224] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1225] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[1226] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[1227] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[1228] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[1229] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[1230] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[1231] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[1232] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[1233] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[1234] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1235] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[1236] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[1237] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[1238] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[1239] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[1240] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[1241] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[1242] The following is further disclosed regarding the embodiments described above.

[1243] (Claim 1)

[1244] A means of receiving the inquiry content entered by the user,

[1245] A generative artificial intelligence means for analyzing the content of received inquiries,

[1246] A generative artificial intelligence means that generates an appropriate answer based on the analysis results,

[1247] A means of sending the generated response to the user's terminal,

[1248] A system that includes this.

[1249] (Claim 2)

[1250] The system according to claim 1, wherein a user terminal inputs and transmits inquiry content via a web interface or application.

[1251] (Claim 3)

[1252] The system according to claim 1, comprising a generative artificial intelligence means that generates specific information according to the type of inquiry based on the analysis results.

[1253] "Example 1"

[1254] (Claim 1)

[1255] A means of receiving the inquiry content entered by the user,

[1256] A means of passing the received inquiry content to a generative artificial intelligence,

[1257] Generative artificial intelligence analyzes received content and identifies key keywords and context,

[1258] A generative artificial intelligence means that generates an appropriate answer based on the analysis results,

[1259] A means of sending the generated response to the user's terminal,

[1260] A means by which the user terminal receives a response from the server and displays it on the user interface,

[1261] A system that includes this.

[1262] (Claim 2)

[1263] The system according to claim 1, wherein a user terminal inputs and transmits inquiry content via a web interface or application.

[1264] (Claim 3)

[1265] The system according to claim 1, comprising a generative artificial intelligence means that generates specific information according to the type of inquiry based on the analysis results.

[1266] "Application Example 1"

[1267] (Claim 1)

[1268] A means of receiving the inquiry content entered by the user,

[1269] A generative artificial intelligence means for analyzing the content of received inquiries,

[1270] A generative artificial intelligence means that generates an appropriate answer based on the analysis results,

[1271] A means of sending the generated response to the user's terminal,

[1272] A means of suggesting the next content to watch based on the usage and viewing history of content distribution services,

[1273] A system that includes this.

[1274] (Claim 2)

[1275] The system according to claim 1, wherein a user terminal inputs and transmits inquiry content via a web interface or application.

[1276] (Claim 3)

[1277] The system according to claim 1, comprising a generative artificial intelligence means that generates specific information according to the type of inquiry based on the analysis results.

[1278] "Example 2 of combining an emotion engine"

[1279] (Claim 1)

[1280] A means of receiving the inquiry content entered by the user,

[1281] A means of identifying the emotions in the received inquiry content using emotion analysis,

[1282] A means for passing emotional information obtained from an emotional analysis means and the content of the inquiry to a generative artificial intelligence means,

[1283] A means for analyzing the content of an inquiry using generative artificial intelligence means and generating an appropriate answer based on the analysis results,

[1284] A means of sending the generated response to the user's terminal,

[1285] A system that includes this.

[1286] (Claim 2)

[1287] The system according to claim 1, wherein a user terminal inputs and transmits inquiry content via a web interface or application.

[1288] (Claim 3)

[1289] The system according to claim 1, comprising a generative artificial intelligence means that generates appropriate information according to the type of inquiry based on the analysis results and emotional information obtained by the emotion analysis means.

[1290] "Application example 2 when combining with an emotional engine"

[1291] (Claim 1)

[1292] A means of receiving the inquiry content entered by the user,

[1293] A generative artificial intelligence means for analyzing the content of received inquiries,

[1294] A generative artificial intelligence means that generates an appropriate answer based on the analysis results,

[1295] A means of analyzing user emotions,

[1296] A means of adjusting responses according to the results of sentiment analysis,

[1297] A means of sending the generated response to the user's terminal,

[1298] A system that includes this.

[1299] (Claim 2)

[1300] The system according to claim 1, wherein a user terminal inputs and transmits inquiry content via a web interface or application.

[1301] (Claim 3)

[1302] The system according to claim 1, comprising a generative artificial intelligence means that generates specific information according to the type of inquiry based on analysis results and sentiment analysis results. [Explanation of Symbols]

[1303] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of receiving the inquiry content entered by the user, A generative artificial intelligence means for analyzing the content of received inquiries, A generative artificial intelligence means that generates an appropriate answer based on the analysis results, A means of sending the generated response to the user's terminal, A system that includes this.

2. The system according to claim 1, wherein the user terminal inputs and transmits inquiry content via a web interface or application.

3. The system according to claim 1, comprising a generative artificial intelligence means that generates specific information according to the type of inquiry based on the analysis results.

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