system

Abstract

We provide the system. [Solution] A means by which the user enters an inquiry into the terminal, The means by which the terminal sends a query to the server, A server receives an inquiry and generates an answer using a generative artificial intelligence system. A means by which the server sends the generated response to the terminal, The means by which the device displays the answer to the user, 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, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In a conventional chatbot system, it is difficult to provide an appropriate and natural answer to a user's inquiry, resulting in a problem of reduced customer satisfaction. In addition, it is impossible to accurately understand the intention of the inquiry content and take appropriate actions, and it is difficult to accurately grasp customer needs. Furthermore, the response to complex inquiries in these systems is limited, often compromising the user experience. Therefore, there is a need for a system that can realize a more natural conversation with users and accurately grasp customer needs.

Means for Solving the Problems

[0005] To solve the above problems, the present invention provides the following means: a system including means for a user to input an inquiry into a terminal, means for the terminal to send the inquiry to a server, means for the server to receive the inquiry and generate an answer using generative artificial intelligence, means for the server to send the generated answer to the terminal, and means for the terminal to display the answer to the user. This system makes it possible to analyze a user's inquiry using natural language processing technology with generative artificial intelligence and generate an appropriate answer. Furthermore, by having the server generate an answer based on the inquiry using the generative artificial intelligence API, more appropriate and natural dialogue becomes possible, leading to improved customer satisfaction and accurate understanding of customer needs.

[0006] A "user" refers to a customer or user who makes an inquiry using the system.

[0007] "Terminal" refers to an electronic device used by a user (for example, a computer, smartphone, tablet, etc.) that is used for inputting and displaying inquiries.

[0008] "Inquiry" refers to a question, request, or confirmation that a user enters through their device.

[0009] A "server" refers to a computer system that receives user inquiries, generates answers using generative artificial intelligence, and sends them to the terminal.

[0010] "Generative artificial intelligence" refers to artificial intelligence technology used to generate appropriate answers based on user inquiries, and specifically refers to large-scale natural language processing models (e.g., GPT-3®).

[0011] "Natural language processing technology" refers to algorithms and methods that enable computers to understand, generate, and respond to human language.

[0012] "API" stands for Application Programming Interface, and refers to an interface for exchanging functions and data between different software programs.

[0013] "Answer" refers to the content of the response that generative artificial intelligence generates in response to a user's inquiry.

[0014] "Customer satisfaction" refers to an indicator of the level of satisfaction and convenience that users experience when using a system.

[0015] "Customer needs" refer to the problems, demands, and expectations that users have. [Brief explanation of the drawing]

[0016] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Mode for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be described.

[0019] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0020] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] 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.

[0022] 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).

[0023] 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."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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".

[0037] This invention is a system in which a user inputs an inquiry, and a generative artificial intelligence provides a natural-sounding response. This system consists of three main elements: the user, the terminal, and the server.

[0038] User actions

[0039] Users first access the system via a device and enter their inquiry. This device can be any internet-connected device, such as a computer, smartphone, or tablet. Inquiries cover a wide range of topics, including product details, service usage instructions, and return policy questions.

[0040] Specific example

[0041] The user enters "Please tell me about returning the product" into the terminal's interface.

[0042] Terminal operation

[0043] The terminal receives the inquiry entered by the user and sends it to the server. The terminal uses communication protocols such as HTTP requests to transfer the user's input to the server in an appropriate manner.

[0044] Specific example

[0045] After the terminal receives user input, it sends the query details to the server as a POST request.

[0046] Server operation

[0047] The server analyzes the inquiry received from the terminal and generates a response using generative artificial intelligence. The generative AI uses, for example, a large-scale language model (such as GPT-3) to generate the optimal response to the user's inquiry.

[0048] The server uses a generative artificial intelligence API to analyze the inquiry and obtain an appropriate response. In this process, the generative AI generates a natural and appropriate response based on relevant information. The generated response is then sent back from the server to the terminal.

[0049] Specific example

[0050] The server uses generative artificial intelligence to generate an appropriate response to a user's inquiry, "Please tell me about product returns," such as "Returns are accepted within 30 days of purchase, along with the receipt."

[0051] Display by device

[0052] The terminal displays the response received from the server to the user. At this stage, the user can obtain the appropriate answer.

[0053] Specific example

[0054] The device displays a message to the user stating, "Returns are accepted within 30 days of purchase, along with the receipt."

[0055] User verification

[0056] The user reviews the answers displayed on the device and decides on the next action based on that information. If the user requires further detailed questions, they enter the inquiry again, and the process is repeated.

[0057] This system allows users to quickly obtain natural and appropriate answers, improving customer satisfaction and enabling a more accurate understanding of customer needs. This system will function effectively in industries such as retail, where inquiries are diverse, and will serve as an important tool for providing high-quality customer service.

[0058] The following describes the processing flow.

[0059] Step 1:

[0060] The user enters their inquiry. The user accesses the terminal interface and enters the inquiry as text. In this case, the inquiry may be entered in the format of "Please tell me about returning a product."

[0061] Step 2:

[0062] The device receives the user's inquiry. When the user clicks the submit button, the device checks the input and retrieves the inquiry text.

[0063] Step 3:

[0064] The terminal sends the query to the server. The terminal sends the retrieved query text to the server as an HTTP POST request. This request contains the query details.

[0065] Step 4:

[0066] The server receives the query. The server receives the request sent from the terminal and extracts the query content from the request body.

[0067] Step 5:

[0068] The server analyzes the query content. The server uses natural language processing techniques to analyze the extracted query content and understand its intent. This analysis includes extracting the query context and key keywords.

[0069] Step 6:

[0070] The server generates an answer using generative artificial intelligence. The server passes the analysis results to the generative AI's API, which then generates an appropriate answer. In this process, the generative AI is designed to generate the answer that is most suitable for the input query.

[0071] Step 7:

[0072] The server retrieves the generated response. The response returned by the generative artificial intelligence is retrieved, and its content is checked. For example, the generated response might be something specific like, "Returns are accepted within 30 days of purchase, along with the receipt."

[0073] Step 8:

[0074] The server sends the response to the device. The server returns the retrieved response to the device as an HTTP response. This response contains the generated response.

[0075] Step 9:

[0076] The terminal receives the response. The terminal receives the response sent from the server and extracts the response content from the response body.

[0077] Step 10:

[0078] The device displays the answer to the user. The device displays the extracted answer content on the user interface. At this time, the answer displayed is "Returns are accepted within 30 days of purchase, along with the receipt."

[0079] Step 11:

[0080] The user confirms the answer. The user checks the answer displayed on the device and understands that it is the correct answer to their inquiry. If necessary, the user enters further detailed questions, and the same process is repeated.

[0081] (Example 1)

[0082] 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."

[0083] Traditional inquiry handling systems struggled to provide timely and appropriate answers to user inquiries. This was particularly true when inquiries covered a wide range of topics, requiring complex analysis and response generation. As a result, customer satisfaction declined, and response costs increased.

[0084] 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.

[0085] In this invention, the server includes means for analyzing a query and calling a generative artificial intelligence API, means for the generative artificial intelligence to generate an appropriate answer, and means for transmitting the generated answer to a terminal. This makes it possible to provide a quick and appropriate answer to a user's query.

[0086] A "user" refers to a person who accesses the system and enters an inquiry.

[0087] A "terminal" refers to a device used by a user to input inquiries and communicate with a server. Examples include computers, smartphones, and tablets.

[0088] A "server" refers to a device that receives user inquiries, calls a generative artificial intelligence API to generate answers, and sends those answers to the terminal.

[0089] "Inquiry" refers to the content of questions or requests that users enter through their devices.

[0090] "Generative artificial intelligence" refers to a technology that uses large-scale language models and other tools to generate appropriate answers to input inquiries.

[0091] An "API" refers to a program interface provided by a generative artificial intelligence system, which a server uses to call the functions of the generative artificial intelligence system.

[0092] "Answer" refers to the response generated by a generative artificial intelligence system based on the user's inquiry.

[0093] "Analysis" refers to the process by which a server understands the content of a query received from a user and provides appropriate information to a generative artificial intelligence system based on that understanding.

[0094] "Display" refers to the act of a device visually providing the user with the response received from the server.

[0095] This invention is a system in which a user inputs an inquiry into a terminal, and a generative artificial intelligence provides a natural-sounding response. This system consists of three main elements: the user, the terminal, and the server.

[0096] User actions

[0097] Users first access the system via a device and enter their inquiry. This device can be any internet-connected device, such as a computer, smartphone, or tablet. Inquiries cover a wide range of topics, including product details, service usage instructions, and return policy questions.

[0098] Specific example

[0099] The user enters "Please tell me about returning the product" into the terminal's interface.

[0100] Terminal operation

[0101] The terminal receives the inquiry entered by the user and sends it to the server. The terminal uses communication protocols such as HTTP requests to transfer the user's input to the server in an appropriate manner.

[0102] Specific example

[0103] After the terminal receives user input, it sends the query details to the server as a POST request.

[0104] Server operation

[0105] The server analyzes the inquiry received from the terminal and generates a response using generative artificial intelligence (AI). The generative AI uses a large-scale language model (e.g., GPT-3) to generate the optimal response to the user's inquiry. The server uses the generative AI's API to analyze the inquiry and obtain an appropriate response. In this process, the generative AI generates a natural and appropriate response based on relevant information. The generated response is then sent back from the server to the terminal.

[0106] Specific example

[0107] The server uses generative artificial intelligence to generate an appropriate response to a user's inquiry, "Please tell me about product returns," such as "Returns are accepted within 30 days of purchase, along with the receipt."

[0108] Display by device

[0109] The terminal displays the response received from the server to the user. At this stage, the user can obtain the appropriate answer.

[0110] Specific example

[0111] The device displays a message to the user stating, "Returns are accepted within 30 days of purchase, along with the receipt."

[0112] User verification

[0113] The user reviews the answers displayed on the device and decides on the next action based on that information. If the user requires further detailed questions, they enter the inquiry again, and the process is repeated.

[0114] Example of a prompt

[0115] The following are specific examples of prompt statements used to query a generative artificial intelligence.

[0116] User: "Please tell me about returning products."

[0117] Through these processes, this system provides an efficient means for users to obtain quick and natural answers. By utilizing generative artificial intelligence, it is possible to respond to inquiries quickly and appropriately.

[0118] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0119] Step 1: The user enters their inquiry.

[0120] The user opens the terminal interface and enters their inquiry. For example, the user might enter "Please tell me about returning an item." This input is then provided as data for the next processing step.

[0121] Input: The content of the inquiry entered by the user on the device (e.g., "Please tell me about returning the product").

[0122] Output: Inquiry content entered into the terminal

[0123] Specific action: The user opens a browser on their smartphone, enters "Please tell me about product returns" into the inquiry form, and presses the submit button.

[0124] Step 2: The device sends the query to the server.

[0125] The terminal sends the inquiry received from the user to the server as an HTTP POST request. This request includes the content of the user's inquiry.

[0126] Input: Inquiry content entered by the user

[0127] Output: HTTP POST request sent to the server

[0128] Specific operation: The terminal sends a user inquiry to the server asking, "Please tell me about returning the product." HTTP is used as the communication protocol for transmission.

[0129] Step 3: The server analyzes the query and calls a generative artificial intelligence API.

[0130] The server analyzes the received query and, based on its content, calls a generative artificial intelligence API. It generates an input prompt for the API and sends the query content.

[0131] Input: HTTP POST request sent to the server (inquiry content)

[0132] Output: Prompt message sent to the generative artificial intelligence API

[0133] Specific operation: The server analyzes the inquiry "Please tell me about product returns" and converts its content into a prompt message to send to a generative artificial intelligence API.

[0134] Step 4: Generative artificial intelligence generates the answer.

[0135] Generative artificial intelligence receives input prompts and generates appropriate responses. For example, it can provide details about return conditions and procedures in a natural-sounding style.

[0136] Input: Prompt sent to a generative AI API

[0137] Output: Answer generated by a generative artificial intelligence system

[0138] Specific operation: The generative artificial intelligence generates the response, "Returns are accepted within 30 days of purchase, along with the receipt."

[0139] Step 5: The server sends the generated response to the terminal.

[0140] The server sends the response received from the generative artificial intelligence back to the terminal. This return is done as an HTTP response.

[0141] Input: Answer generated by a generative artificial intelligence system

[0142] Output: HTTP response sent to the terminal

[0143] Specific action: The server sends an HTTP response to the terminal stating, "Returns are accepted within 30 days of purchase, along with the receipt."

[0144] Step 6: The device displays the answer to the user.

[0145] The terminal displays the response received from the server on the user interface. The response content is displayed in an easy-to-read format so that the user can easily check it.

[0146] Input: HTTP response received from the server (response generated by a generative AI)

[0147] Output: Answer displayed in the user interface

[0148] Specific action: The device displays a message on the screen stating, "Returns are accepted within 30 days of purchase, along with the receipt."

[0149] Step 7: The user reviews the displayed response and makes further inquiries if necessary.

[0150] The user reviews the displayed answers and, if necessary, enters further detailed questions. If the answers are satisfactory, they then decide on their next course of action.

[0151] Input: Answer displayed in the user interface

[0152] Output: User's next action (entering a more detailed inquiry or deciding on the next course of action)

[0153] Specific operation: The user reviews the displayed answer and, if not satisfied, enters an additional question such as, "Where should I take this item for a return?" This process is repeated.

[0154] (Application Example 1)

[0155] 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."

[0156] Conventional security services have limited ways for users to easily check the security status of their homes or offices. Furthermore, checking the status often required the use of numerous applications and devices, making the process cumbersome. Moreover, in situations requiring real-time status checks and rapid response, there was a possibility of delays in information acquisition and response. This invention aims to solve these problems and provide a system that allows users to easily and quickly check their security status and take appropriate action.

[0157] 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.

[0158] In this invention, the server includes means for the server to check the status in conjunction with a monitoring system, means for a generative artificial intelligence to generate a response in real time based on data from the monitoring system, and means for the server to generate a response based on an inquiry using the API of the generative artificial intelligence and reflect the status in real time by referring to data from the monitoring system. As a result, users can easily check the security status through their terminals and take appropriate action quickly.

[0159] A "user" refers to an individual or legal entity that uses the system.

[0160] A "device" refers to a computer, smartphone, tablet, or any other device that can connect to the internet and is operated by a user.

[0161] "Inquiry" refers to the content of questions or requests entered by the user via their device.

[0162] A "server" refers to a remote computing system that receives inquiries from terminals, generates answers using generative artificial intelligence, and sends those answers back to the terminals.

[0163] "Generative artificial intelligence" refers to artificial intelligence technology that analyzes user inquiries and generates appropriate answers, such as systems that use large-scale language models.

[0164] "Natural language processing technology" refers to the technology used by generative artificial intelligence to analyze natural language queries input by users, understand their meaning, and generate appropriate responses.

[0165] "API" refers to an application programming interface for using generative artificial intelligence, and is a protocol for efficiently exchanging data between a server and artificial intelligence.

[0166] "Surveillance systems" refer to a general term for internet-connected devices used for security purposes, such as security cameras, door sensors, and smart locks.

[0167] "Status check" refers to understanding the current security status based on information obtained through the monitoring system.

[0168] "Real-time" refers to providing near-instantaneous responses to user inquiries.

[0169] This invention provides a system that allows users to easily check the security status of their home or office and take quick action. This system consists of four main elements: the user, the terminal, the server, and the monitoring system. The following describes each element and its operation in detail.

[0170] 1. User actions

[0171] The user first accesses the terminal and enters a security-related inquiry. The terminal can be a computer, smartphone, tablet, or other device connected to the internet. User inquiries cover a wide range of topics, such as checking security camera footage or the status of door locks.

[0172] Specific example

[0173] The user enters "Please tell me if the front door is currently locked" into the terminal's interface.

[0174] 2. Device operation

[0175] The terminal receives the inquiry entered by the user and sends it to the server. The terminal uses communication protocols such as HTTP requests to transfer the user's input to the server in an appropriate manner.

[0176] Specific example

[0177] After the terminal receives user input, it sends the query details to the server as a POST request.

[0178] 3. Server operation

[0179] The server analyzes inquiries received from terminals and generates responses using generative artificial intelligence (AI). The AI ​​uses, for example, large-scale language models (such as GPT-3) to generate the optimal response to user inquiries. The server also works in conjunction with a monitoring system, acquiring data from the monitoring system in real time and using the AI's API to generate appropriate responses.

[0180] Specific example

[0181] The server, in response to the query "Is the front door currently locked?", checks the status of the smart lock and generates the response "The front door is currently locked."

[0182] 4. Display by device

[0183] The terminal displays the response received from the server to the user. At this stage, the user can obtain the appropriate answer.

[0184] Specific example

[0185] The device displays the response to the user: "The front door is currently locked."

[0186] 5. Program Details

[0187] The system is implemented using programming languages ​​such as Python. User inquiries are sent to the server via HTTP requests, and the server, in conjunction with monitoring systems such as smart locks and security cameras, acquires data in real time and generates answers using a generative artificial intelligence (e.g., GPT-3) API. The generated answers are sent back to the user's device and displayed.

[0188] Example of a prompt

[0189] "Please tell me if the front door is currently locked."

[0190] "Show me the latest footage from the security camera in the living room."

[0191] This system allows users to intuitively check the security status of their home or office and take quick action, which is expected to improve security levels.

[0192] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0193] Step 1:

[0194] The user enters the inquiry into the device.

[0195] The user enters security-related questions into the terminal. For example, they might enter a question like, "Please tell me if the front door is currently locked."

[0196] Input: User inquiry text (e.g., "Please tell me if the front door is currently locked.")

[0197] Output: Query content as a string

[0198] Step 2:

[0199] The device sends the query to the server.

[0200] The terminal sends the user's input query to the server as an HTTP request. The POST method is used to transfer the data.

[0201] Input: User's query text, server URL

[0202] Output: HTTP POST request sent to the server

[0203] Step 3:

[0204] The server receives the query.

[0205] The server receives queries sent from terminals and analyzes their content. It converts the query content into data frames or dictionary formats for further analysis.

[0206] Input: HTTP POST request from terminal

[0207] Output: Analyzed query content (e.g., "Check the lock status of the front door")

[0208] Step 4:

[0209] The server uses generative artificial intelligence to generate the answer.

[0210] The server generates the optimal response based on the analyzed query content, using a generative artificial intelligence (e.g., GPT-3) API. The generative AI generates a natural-sounding response using a pre-trained language model.

[0211] Input: Analyzed query content

[0212] Output: Generated response text (e.g., "The front door is currently locked.")

[0213] Step 5:

[0214] The server works in conjunction with the monitoring system to check the status.

[0215] The server interacts with monitoring systems such as smart locks and security cameras via APIs to acquire data in real time. Based on this information, generative artificial intelligence generates responses that reflect the situation.

[0216] Input: Analyzed query content, monitoring system API response

[0217] Output: Real-time data from the monitoring system

[0218] Step 6:

[0219] The server sends the generated response to the terminal.

[0220] The server sends the generated response back to the terminal as an HTTP response. During this process, the data is transferred in JSON format or other formats as needed.

[0221] Input: Generated response text

[0222] Output: HTTP response sent to the terminal

[0223] Step 7:

[0224] The device displays the answer to the user.

[0225] The terminal displays the response received from the server to the user. At this stage, the user can obtain an appropriate answer to their inquiry.

[0226] Input: Response text from the server

[0227] Output: The response will be displayed in a format viewable by the user (e.g., "The front door is currently locked").

[0228] Through these specific processing steps, users can intuitively check the security status of their home or office and take quick action.

[0229] 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.

[0230] This invention is a system that combines a user inputting an inquiry with a generative artificial intelligence system providing a natural response, along with an emotion engine that recognizes the user's emotions. This system consists of four main elements: the user, the terminal, the server, and the emotion engine.

[0231] User actions

[0232] Users first access the system via a device and enter their inquiry. This device can be any internet-connected device, such as a computer, smartphone, or tablet. Inquiries cover a wide range of topics, including product details, service usage instructions, and return policy questions.

[0233] Specific example

[0234] The user enters "Please tell me about returning the product" into the terminal's interface.

[0235] Terminal operation

[0236] The terminal receives the query entered by the user and prepares to send it to the server. The terminal uses communication protocols such as HTTP requests to transfer the user's input to the server in an appropriate manner.

[0237] Specific example

[0238] After the terminal receives user input, it sends the query details to the server as an HTTP POST request.

[0239] Server operation

[0240] The server analyzes the inquiry received from the terminal and generates a response using generative artificial intelligence. For example, the generative AI uses a large-scale language model to generate the optimal response to the user's inquiry.

[0241] When the server analyzes the query, it uses an emotion engine to recognize the user's emotions. The emotion engine uses natural language processing techniques to identify emotions from the user's input. This emotion information is used as reference information when the generative artificial intelligence generates a response.

[0242] Specific example

[0243] The server, in response to a user's inquiry, "Please tell me about product returns," uses generative artificial intelligence to generate a specific answer such as, "Returns are accepted within 30 days of purchase, along with the receipt." If the emotion engine detects the user's impatience or dissatisfaction, it adds a reassuring phrase to the answer, such as, "Please feel free to contact us."

[0244] Server-side transmission and terminal-side reception.

[0245] The server sends the generated response to the terminal. The terminal displays the response received from the server to the user.

[0246] Specific example

[0247] The server generates a response saying, "Returns are accepted within 30 days of purchase, along with the receipt. Please feel free to contact us," and sends it to the device. The device then displays this message to the user.

[0248] User verification

[0249] The user reviews the answers displayed on the device and decides on the next action based on that information. If the user requires further detailed questions, they enter the inquiry again, and the process is repeated.

[0250] This system allows users to receive natural and appropriate answers quickly, and by recognizing their emotions, they can receive more personalized and satisfying service. This improves customer satisfaction and enables a more accurate understanding of customer needs. This system will function effectively in industries such as retail, where inquiries are diverse, and will be an important tool for providing advanced customer service.

[0251] The following describes the processing flow.

[0252] Step 1:

[0253] The user enters their inquiry. The user accesses the terminal interface and enters their inquiry into the text box. For example, they might enter, "Please tell me about returning a product."

[0254] Step 2:

[0255] The terminal receives the user's inquiry. When the user clicks the submit button, the terminal retrieves the input and stores the inquiry content in a variable.

[0256] Step 3:

[0257] The device sends a query to the server. The device generates an HTTP POST request containing the query details and sends it to the specified endpoint on the server.

[0258] Step 4:

[0259] The server receives the query. The server receives the HTTP POST request sent from the terminal and extracts the query content from the request body.

[0260] Step 5:

[0261] The server analyzes the query. Before passing the query to the generative artificial intelligence, the server uses an emotion engine to analyze the user's emotions. The emotion engine uses natural language processing techniques to identify emotional states (e.g., joy, anger, sadness, etc.) within the text.

[0262] Step 6:

[0263] The server passes the query to the generative artificial intelligence based on the emotion analysis results. The server considers the emotion information obtained by the emotion engine and provides the generative artificial intelligence with appropriate context.

[0264] Step 7:

[0265] Generative artificial intelligence generates the answer. Based on the inquiry content and emotional information, the generative AI generates the most suitable answer. For example, if the emotional engine recognizes that the user's emotion is "anxiety," the generative AI will select words that provide reassurance.

[0266] Step 8:

[0267] The server retrieves the generated response. The server receives the response returned by the generative artificial intelligence and verifies its contents.

[0268] Step 9:

[0269] The server sends the response to the device. The server then sends the received response back to the device as an HTTP response. This response contains the generated response.

[0270] Step 10:

[0271] The terminal receives the response. The terminal receives the HTTP response sent from the server and extracts the response content from the response body.

[0272] Step 11:

[0273] The device displays the response to the user. The device displays the extracted response content on the user interface. For example, the displayed response might be, "Returns are accepted within 30 days of purchase, along with the receipt. Please feel free to contact us."

[0274] Step 12:

[0275] The user confirms the answer. The user checks the answer displayed on their device and confirms that it is the correct answer to their inquiry. If necessary, the user re-enters the question, and the process is repeated.

[0276] This system allows users to quickly obtain natural and appropriate responses that take emotions into account, generated by generative artificial intelligence. As a result, the user experience will improve, and customer satisfaction will increase.

[0277] (Example 2)

[0278] 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".

[0279] Traditional user inquiry systems often provide mechanical responses without considering the user's feelings. This leads to decreased user satisfaction and a problem in that they cannot adequately address users who are particularly dissatisfied or anxious. Furthermore, it is difficult to respond quickly and individually to a wide range of inquiries, making it challenging to provide efficient customer service.

[0280] The specific processing by the specific processing unit 290 of the data processing apparatus 12 in Example 2 is realized by the following means. In this invention, the server includes means for recognizing the user's emotion using an emotion analysis engine, means for generating an answer using a generative artificial intelligence model, and means for transmitting the generated answer to the terminal. Thereby, it is possible to consider the user's emotion and provide a more personalized and satisfactory response. In addition, since it is possible to quickly and accurately respond to a wide variety of inquiries, it is possible to provide efficient customer service.

[0281] The "user" refers to a general user who inputs an inquiry using the system.

[0282] The "terminal" refers to a device (such as a computer, smartphone, tablet, etc.) used by the user to access the Internet and input an inquiry.

[0283] The "inquiry" refers to the content of a question or request input by the user to the system.

[0284] The "server" refers to a device or software that receives a user's inquiry, analyzes its content, generates an appropriate answer, and transmits it to the terminal.

[0285] The "emotion analysis engine" refers to an engine that uses natural language processing technology to recognize the emotion from the content of the user's inquiry.

[0286] The "generative artificial intelligence model" refers to a large-scale language model for generating an appropriate answer based on the content of the user's inquiry.

[0287] The "answer" refers to the information or guidance content generated by the server using the generative artificial intelligence model and presented to the user.

[0288] The "natural language processing technology" refers to a technology for analyzing the input text data, understanding its meaning, and generating an appropriate output.

[0289] "API" is an abbreviation for Application Programming Interface, and refers to an interface for using external functions such as generative artificial intelligence models.

[0290] This invention relates to a system in which a user inputs an inquiry, and a generative artificial intelligence provides a natural response, while also recognizing the user's emotions and providing a response appropriate to those emotions. This system consists of four main elements: the user, the terminal, the server, and the emotion analysis engine.

[0291] The user first accesses a terminal and enters their inquiry. Any internet-connected device can be used as the terminal, including computers, smartphones, and tablets. Once the user enters their inquiry, the terminal prepares to send it to the server. HTTP requests are the common communication protocol. For example, if the user enters "Please tell me about product returns," the terminal sends this inquiry to the server as an HTTP POST request.

[0292] The server analyzes inquiries received from terminals and generates responses using generative artificial intelligence (AI). It is desirable to use a large-scale language model (e.g., GPT-3) for this generative AI. When the server analyzes the inquiry, it uses an emotion analysis engine to recognize the user's emotions. The emotion analysis engine identifies emotions from the user's input using natural language processing techniques. This emotion information is used as reference information when the generative AI generates responses.

[0293] As a concrete example, when a server receives a user inquiry such as "Please tell me about product returns," it uses generative artificial intelligence to generate a specific response such as "Returns are accepted within 30 days of purchase, along with the receipt." In this case, if the sentiment analysis engine detects the user's impatience or dissatisfaction, it adds a reassuring phrase to the response, such as "Please feel free to contact us."

[0294] The generated response is sent from the server to the terminal, where it is displayed to the user. The user reviews the response displayed on the terminal and decides on the next action based on that information. For example, if the user is satisfied, they proceed with the action; if further detailed questions are needed, they enter the inquiry again, and the process is repeated.

[0295] This system allows users to receive natural and appropriate answers quickly, and by recognizing their emotions, they can receive more personalized and satisfying service. This improves customer satisfaction and enables a more accurate understanding of customer needs. This system is particularly effective in industries where a wide range of inquiries occur, and it becomes an important tool for providing high-quality customer service.

[0296] Hardware and software to be used

[0297] Hardware: Computers, smartphones, tablets

[0298] Software: Web browser, HTTP communication protocol, generative artificial intelligence (GPT-3, etc.), sentiment analysis engine

[0299] Examples of prompts for generative AI models

[0300] User inquiry: "Please tell me about returning products."

[0301] Emotional analysis results: "Impatience, dissatisfaction"

[0302] Prompt: "User inquiry: Please tell me about returning the product. User's emotional state: Anxious, dissatisfied. Generate a reassuring response for the user."

[0303] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0304] Step 1:

[0305] The user accesses the terminal and enters an inquiry. When the user enters the inquiry content (e.g., "Please tell me about product returns"), data is input into the terminal. The terminal receives this data and prepares for the next step. As a specific operation, the user opens a web browser and enters the inquiry content into a web form. The input at this time is string data.

[0306] Step 2:

[0307] The terminal processes the user's input and prepares to send it to the server. The input string data is converted into the format of an HTTP POST request. Specifically, JavaScript (registered trademark) etc. is used to capture the input data and construct an HTTP POST request. By sending the constructed request to the server, data flows to the next step. The input is string data and the output is an HTTP POST request.

[0308] Step 3:

[0309] The server analyzes the HTTP POST request received from the terminal. The inquiry content is extracted from the request body and taken out as string data. As a specific operation, the server receives the HTTP request and analyzes the user's inquiry "Please tell me about product returns" from its body part. Using this analysis result, a prompt for the generative artificial intelligence is prepared. The input is an HTTP POST request and the output is the analyzed string data.

[0310] Step 4:

[0311] The server sends the analyzed query to the sentiment analysis engine to recognize the user's emotions. The input string data is passed to the sentiment analysis engine, which processes it. Natural language processing techniques are used to identify the user's emotions (e.g., "anxiety, dissatisfaction"). Specifically, the query text is sent to the sentiment analysis engine, and emotion data such as "anxiety, dissatisfaction" is obtained as a result of the analysis. The input is the analyzed string data, and the output is the emotion data.

[0312] Step 5:

[0313] The server sends a prompt to a generative AI model (e.g., GPT-3) based on the inquiry content and sentiment data to generate a response. The inquiry content and sentiment information are combined to form the prompt. For example, the prompt "User inquiry: Please tell me about product returns. User sentiment state: Anxious, dissatisfied. Please generate a response that will reassure the user." is sent to the generative AI model. The generative AI model then generates a response based on this (e.g., "Returns are accepted within 30 days of purchase, along with the receipt. Please feel free to contact us."). The input is the prompt, and the output is the generated response.

[0314] Step 6:

[0315] The server sends the generated response to the terminal. It wraps the generated response as an HTTP response and sends it back to the terminal. Specifically, it sends the output of the generating AI model to the terminal in an HTTP response. Upon receiving this response, the terminal proceeds to the next step. The input is the generated response, and the output is the HTTP response.

[0316] Step 7:

[0317] The terminal displays the response received from the server to the user. It parses the HTTP response, extracts it as string data, and displays it in the browser's user interface. Specifically, the terminal receives the HTTP response and displays the response "Returns are accepted within 30 days of purchase, along with the receipt. Please feel free to contact us" in the inquiry form. The input is the HTTP response, and the output is the response displayed in the user interface.

[0318] Step 8:

[0319] The user reviews the answer displayed on their device and decides on their next action. If necessary, they re-enter their inquiry, and the process is repeated. Specifically, the user reads the displayed answer, takes action if they are satisfied, or enters a new question into the web form if further details are needed. The input is the displayed answer, and the output is the user's next action.

[0320] (Application Example 2)

[0321] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0322] Conventional inquiry systems using generative artificial intelligence have the problem of not recognizing user emotions and providing uniform answers, making it difficult to respond flexibly according to the user's emotions and circumstances. In particular, in situations where direct interaction with customers is important, such as in physical stores, it is necessary to understand the emotions of individual customers and provide appropriate support. Therefore, if a system that can recognize user emotions and provide appropriate responses is realized, it will be possible to significantly improve customer satisfaction.

[0323] 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.

[0324] In this invention, the server includes means for the user to input an inquiry into a terminal, means for the terminal to send the inquiry to the server, means for the server to receive the inquiry and generate an answer using generative artificial intelligence, means for the terminal to recognize the user's emotions through emotion analysis, means for adjusting the generative artificial intelligence's answer based on the emotion analysis, means for the server to send the generated answer to the terminal, and means for the terminal to display the answer to the user. This enables more personalized responses that respond to the user's emotions. Specifically, when a user makes an inquiry such as "Please tell me about returning a product," the emotion analysis means can be used to analyze the user's emotions and provide a reassuring answer based on the results. This is expected to improve the quality of the user experience and increase customer satisfaction in physical stores.

[0325] A "user" is an individual or organization that makes an inquiry using a terminal.

[0326] A "terminal" is an internet-connected device used to process user-inputted inquiries and communicate with a server.

[0327] A "server" is a central processing unit that receives inquiries from users, generates responses using generative artificial intelligence and sentiment analysis methods, and transmits them to terminals.

[0328] "Generative artificial intelligence" refers to an artificial intelligence system that uses natural language processing technology to analyze user inquiries and generate appropriate responses.

[0329] "Emotion analysis means" refers to analytical technology that recognizes emotions from user input and adjusts responses based on those emotions.

[0330] "Natural language processing technology" refers to technologies that enable computers to understand, interpret, and generate human language.

[0331] "Answer" refers to information or instructions provided by a generative artificial intelligence system in response to a user's inquiry.

[0332] "Adjustment" refers to the process of modifying the responses generated by generative artificial intelligence in accordance with the user's emotions.

[0333] "Inquiry" refers to the process by which a user enters information they want to know or a problem they want to solve in language.

[0334] "Display" refers to outputting the answers generated on the device in a format that allows the user to visually confirm them.

[0335] This invention is a system that combines a system in which a user inputs an inquiry and a generative artificial intelligence provides a natural response, with an emotion analysis means that recognizes the user's emotions. The system consists of four main elements: the user, the terminal, the server, and the emotion analysis means.

[0336] User actions

[0337] The user first accesses a terminal and enters their inquiry. The terminal can be any internet-connected device, such as a computer, smartphone, or tablet. Inquiries cover a wide range of topics, including product details, service usage instructions, and return policy. For example, the user might enter "What is the warranty period for this product?" into the terminal's interface.

[0338] Terminal operation

[0339] The terminal receives the user's input and prepares to send it to the server. The terminal uses a communication protocol such as an HTTP request to transfer the user's input to the server in an appropriate manner. For example, after the terminal receives the user's input, it sends the content of the inquiry to the server as an HTTP POST request.

[0340] Server operation

[0341] The server analyzes inquiries received from terminals and generates responses using generative artificial intelligence (AI). The generative AI, for example, uses a large-scale language model to generate the optimal response to the user's inquiry. When the server analyzes the inquiry, it uses sentiment analysis to recognize the user's emotions. Sentiment analysis uses natural language processing techniques to identify emotions from the user's input. This emotion information is used as reference information when the generative AI generates responses.

[0342] Specific example

[0343] The server uses generative artificial intelligence to generate a response to the user's inquiry, "What is the warranty period for this product?", stating, "The warranty period is one year." If the sentiment analysis system detects the user's impatience or dissatisfaction, it adds a reassuring phrase to the response, such as, "Please feel free to contact us if you have any concerns."

[0344] Server-side transmission and terminal-side reception.

[0345] The server sends the generated response to the terminal. The terminal displays the response received from the server to the user. For example, the server generates the response "The warranty period is one year. Please feel free to contact us if you have any concerns" and sends it to the terminal. The terminal then displays this content to the user.

[0346] User verification

[0347] The user reviews the answers displayed on the device and decides on the next action based on that information. If the user requires further detailed questions, they enter the inquiry again, and the process is repeated.

[0348] This system allows users to receive natural and appropriate answers quickly, and by recognizing their emotions, they can receive more personalized and satisfying service. This improves customer satisfaction and enables a more accurate understanding of customer needs. This system will function effectively in industries such as retail, where inquiries are diverse, and will be an important tool for providing advanced customer service.

[0349] Example of a prompt:

[0350] User: "What is the warranty period for this product?"

[0351] Answer: "The warranty period is one year. If you have any concerns, please feel free to contact us."

[0352] This invention is a system that improves customer support in physical stores by combining generative artificial intelligence and emotion analysis means. The specific program processing sequence and technical details will be explained by clearly outlining how each element interacts and describing how users utilize the system.

[0353] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0354] Step 1:

[0355] User input

[0356] The user accesses the terminal and enters their inquiry into the interface. For example, the user might ask, "What is the warranty period for this product?"

[0357] Input: User inquiry

[0358] Output: Inquiry data from the terminal

[0359] Specific action: The user enters text into an input field displayed on the device screen and presses the send button.

[0360] Step 2:

[0361] Data transfer via terminal

[0362] The terminal receives the query entered by the user and prepares to send it to the server. Specifically, it uses an HTTP POST request to forward the user's query to the server.

[0363] Input: User inquiry data

[0364] Output: HTTP request sent to the server

[0365] Specific operation: The terminal converts the query data into an HTTP request format and sends it to the specified endpoint on the server.

[0366] Step 3:

[0367] Server receives and analyzes queries.

[0368] The server analyzes the inquiries received from the terminal and generates the optimal answer using generative artificial intelligence. First, it analyzes the received data and inputs it into the generative artificial intelligence model.

[0369] Input: HTTP request received from the terminal

[0370] Output: Input data for the generative AI model

[0371] Specific operation: The server extracts the query content from the HTTP request and converts it into a format that can be input into the generating AI model.

[0372] Step 4:

[0373] Emotion recognition using emotion analysis methods

[0374] Before passing the query content to the generative artificial intelligence, the server uses sentiment analysis to recognize the user's emotions. Sentiment analysis uses natural language processing techniques to identify emotions from the user's input.

[0375] Input: Inquiry details

[0376] Output: Sentiment analysis results (e.g., positive, negative, etc.)

[0377] Specific operation: Analyze the question text and calculate sentiment polarity using a sentiment recognition algorithm.

[0378] Step 5:

[0379] Answer generation using generative artificial intelligence

[0380] The server feeds back the emotion analysis results to the generative artificial intelligence system, which then generates the optimal response tailored to the user's emotions.

[0381] Input: Inquiry details, sentiment analysis results

[0382] Output: Generated answer

[0383] Specific operation: The AI ​​model receives the inquiry content and sentiment analysis results, and generates an appropriate response.

[0384] Step 6:

[0385] Server-based response submission

[0386] The server sends the generated response to the terminal.

[0387] Input: Generated answer

[0388] Output: Answer sent to the terminal

[0389] Specific action: The generated response is sent back to the terminal as an HTTP response.

[0390] Step 7:

[0391] Display of answers via device

[0392] The terminal displays the response received from the server to the user.

[0393] Input: Response received from the server

[0394] Output: Answer displayed to the user

[0395] Specific action: Display the answer text on the device screen so the user can confirm it.

[0396] The user reviews the answer displayed on the device, and if further questions are needed, they enter their inquiry again, and the process is repeated.

[0397] Example of a prompt:

[0398] User: "What is the warranty period for this product?"

[0399] Answer: "The warranty period is one year. If you have any concerns, please feel free to contact us."

[0400] 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.

[0401] 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.

[0402] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0403] [Second Embodiment]

[0404] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0405] 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.

[0406] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0407] The 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.

[0408] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0409] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0410] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0411] Figure 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.

[0412] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0413] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0414] In the 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.

[0415] 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".

[0416] This invention is a system in which a user inputs an inquiry, and a generative artificial intelligence provides a natural-sounding response. This system consists of three main elements: the user, the terminal, and the server.

[0417] User actions

[0418] Users first access the system via a device and enter their inquiry. This device can be any internet-connected device, such as a computer, smartphone, or tablet. Inquiries cover a wide range of topics, including product details, service usage instructions, and return policy questions.

[0419] Specific example

[0420] The user enters "Please tell me about returning the product" into the terminal's interface.

[0421] Terminal operation

[0422] The terminal receives the inquiry entered by the user and sends it to the server. The terminal uses communication protocols such as HTTP requests to transfer the user's input to the server in an appropriate manner.

[0423] Specific example

[0424] After the terminal receives user input, it sends the query details to the server as a POST request.

[0425] Server operation

[0426] The server analyzes the inquiry received from the terminal and generates a response using generative artificial intelligence. The generative AI uses, for example, a large-scale language model (such as GPT-3) to generate the optimal response to the user's inquiry.

[0427] The server uses a generative artificial intelligence API to analyze the inquiry and obtain an appropriate response. In this process, the generative AI generates a natural and appropriate response based on relevant information. The generated response is then sent back from the server to the terminal.

[0428] Specific example

[0429] The server uses generative artificial intelligence to generate an appropriate response to a user's inquiry, "Please tell me about product returns," such as "Returns are accepted within 30 days of purchase, along with the receipt."

[0430] Display by device

[0431] The terminal displays the response received from the server to the user. At this stage, the user can obtain the appropriate answer.

[0432] Specific example

[0433] The device displays a message to the user stating, "Returns are accepted within 30 days of purchase, along with the receipt."

[0434] User verification

[0435] The user reviews the answers displayed on the device and decides on the next action based on that information. If the user requires further detailed questions, they enter the inquiry again, and the process is repeated.

[0436] This system allows users to quickly obtain natural and appropriate answers, improving customer satisfaction and enabling a more accurate understanding of customer needs. This system will function effectively in industries such as retail, where inquiries are diverse, and will serve as an important tool for providing high-quality customer service.

[0437] The following describes the processing flow.

[0438] Step 1:

[0439] The user enters their inquiry. The user accesses the terminal interface and enters the inquiry as text. In this case, the inquiry may be entered in the format of "Please tell me about returning a product."

[0440] Step 2:

[0441] The device receives the user's inquiry. When the user clicks the submit button, the device checks the input and retrieves the inquiry text.

[0442] Step 3:

[0443] The terminal sends the query to the server. The terminal sends the retrieved query text to the server as an HTTP POST request. This request contains the query details.

[0444] Step 4:

[0445] The server receives the query. The server receives the request sent from the terminal and extracts the query content from the request body.

[0446] Step 5:

[0447] The server analyzes the query content. The server uses natural language processing techniques to analyze the extracted query content and understand its intent. This analysis includes extracting the query context and key keywords.

[0448] Step 6:

[0449] The server generates an answer using generative artificial intelligence. The server passes the analysis results to the generative AI's API, which then generates an appropriate answer. In this process, the generative AI is designed to generate the answer that is most suitable for the input query.

[0450] Step 7:

[0451] The server retrieves the generated response. The response returned by the generative artificial intelligence is retrieved, and its content is checked. For example, the generated response might be something specific like, "Returns are accepted within 30 days of purchase, along with the receipt."

[0452] Step 8:

[0453] The server sends the response to the device. The server returns the retrieved response to the device as an HTTP response. This response contains the generated response.

[0454] Step 9:

[0455] The terminal receives the response. The terminal receives the response sent from the server and extracts the response content from the response body.

[0456] Step 10:

[0457] The device displays the answer to the user. The device displays the extracted answer content on the user interface. At this time, the answer displayed is "Returns are accepted within 30 days of purchase, along with the receipt."

[0458] Step 11:

[0459] The user confirms the answer. The user checks the answer displayed on the device and understands that it is the correct answer to their inquiry. If necessary, the user enters further detailed questions, and the same process is repeated.

[0460] (Example 1)

[0461] 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."

[0462] Traditional inquiry handling systems struggled to provide timely and appropriate answers to user inquiries. This was particularly true when inquiries covered a wide range of topics, requiring complex analysis and response generation. As a result, customer satisfaction declined, and response costs increased.

[0463] 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.

[0464] In this invention, the server includes means for analyzing a query and calling a generative artificial intelligence API, means for the generative artificial intelligence to generate an appropriate answer, and means for transmitting the generated answer to a terminal. This makes it possible to provide a quick and appropriate answer to a user's query.

[0465] A "user" refers to a person who accesses the system and enters an inquiry.

[0466] A "terminal" refers to a device used by a user to input inquiries and communicate with a server. Examples include computers, smartphones, and tablets.

[0467] A "server" refers to a device that receives user inquiries, calls a generative artificial intelligence API to generate answers, and sends those answers to the terminal.

[0468] "Inquiry" refers to the content of questions or requests that users enter through their devices.

[0469] "Generative artificial intelligence" refers to a technology that uses large-scale language models and other tools to generate appropriate answers to input inquiries.

[0470] An "API" refers to a program interface provided by a generative artificial intelligence system, which a server uses to call the functions of the generative artificial intelligence system.

[0471] "Answer" refers to the response generated by a generative artificial intelligence system based on the user's inquiry.

[0472] "Analysis" refers to the process by which a server understands the content of a query received from a user and provides appropriate information to a generative artificial intelligence system based on that understanding.

[0473] "Display" refers to the act of a device visually providing the user with the response received from the server.

[0474] This invention is a system in which a user inputs an inquiry into a terminal, and a generative artificial intelligence provides a natural-sounding response. This system consists of three main elements: the user, the terminal, and the server.

[0475] User actions

[0476] Users first access the system via a device and enter their inquiry. This device can be any internet-connected device, such as a computer, smartphone, or tablet. Inquiries cover a wide range of topics, including product details, service usage instructions, and return policy questions.

[0477] Specific example

[0478] The user enters "Please tell me about returning the product" into the terminal's interface.

[0479] Terminal operation

[0480] The terminal receives the inquiry entered by the user and sends it to the server. The terminal uses communication protocols such as HTTP requests to transfer the user's input to the server in an appropriate manner.

[0481] Specific example

[0482] After the terminal receives user input, it sends the query details to the server as a POST request.

[0483] Server operation

[0484] The server analyzes the inquiry received from the terminal and generates a response using generative artificial intelligence (AI). The generative AI uses a large-scale language model (e.g., GPT-3) to generate the optimal response to the user's inquiry. The server uses the generative AI's API to analyze the inquiry and obtain an appropriate response. In this process, the generative AI generates a natural and appropriate response based on relevant information. The generated response is then sent back from the server to the terminal.

[0485] Specific example

[0486] The server uses generative artificial intelligence to generate an appropriate response to a user's inquiry, "Please tell me about product returns," such as "Returns are accepted within 30 days of purchase, along with the receipt."

[0487] Display by device

[0488] The terminal displays the response received from the server to the user. At this stage, the user can obtain the appropriate answer.

[0489] Specific example

[0490] The device displays a message to the user stating, "Returns are accepted within 30 days of purchase, along with the receipt."

[0491] User verification

[0492] The user reviews the answers displayed on the device and decides on the next action based on that information. If the user requires further detailed questions, they enter the inquiry again, and the process is repeated.

[0493] Example of a prompt

[0494] The following are specific examples of prompt statements used to query a generative artificial intelligence.

[0495] User: "Please tell me about returning products."

[0496] Through these processes, this system provides an efficient means for users to obtain quick and natural answers. By utilizing generative artificial intelligence, it is possible to respond to inquiries quickly and appropriately.

[0497] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0498] Step 1: The user enters their inquiry.

[0499] The user opens the terminal interface and enters their inquiry. For example, the user might enter "Please tell me about returning an item." This input is then provided as data for the next processing step.

[0500] Input: The content of the inquiry entered by the user on the device (e.g., "Please tell me about returning the product").

[0501] Output: Inquiry content entered into the terminal

[0502] Specific action: The user opens a browser on their smartphone, enters "Please tell me about product returns" into the inquiry form, and presses the submit button.

[0503] Step 2: The device sends the query to the server.

[0504] The terminal sends the inquiry received from the user to the server as an HTTP POST request. This request includes the content of the user's inquiry.

[0505] Input: Inquiry content entered by the user

[0506] Output: HTTP POST request sent to the server

[0507] Specific operation: The terminal sends a user inquiry to the server asking, "Please tell me about returning the product." HTTP is used as the communication protocol for transmission.

[0508] Step 3: The server analyzes the query and calls a generative artificial intelligence API.

[0509] The server analyzes the received query and, based on its content, calls a generative artificial intelligence API. It generates an input prompt for the API and sends the query content.

[0510] Input: HTTP POST request sent to the server (inquiry content)

[0511] Output: Prompt message sent to the generative artificial intelligence API

[0512] Specific operation: The server analyzes the inquiry "Please tell me about product returns" and converts its content into a prompt message to send to a generative artificial intelligence API.

[0513] Step 4: Generative artificial intelligence generates the answer.

[0514] Generative artificial intelligence receives input prompts and generates appropriate responses. For example, it can provide details about return conditions and procedures in a natural-sounding style.

[0515] Input: Prompt sent to a generative AI API

[0516] Output: Answer generated by a generative artificial intelligence system

[0517] Specific operation: The generative artificial intelligence generates the response, "Returns are accepted within 30 days of purchase, along with the receipt."

[0518] Step 5: The server sends the generated response to the terminal.

[0519] The server sends the response received from the generative artificial intelligence back to the terminal. This return is done as an HTTP response.

[0520] Input: Answer generated by a generative artificial intelligence system

[0521] Output: HTTP response sent to the terminal

[0522] Specific action: The server sends an HTTP response to the terminal stating, "Returns are accepted within 30 days of purchase, along with the receipt."

[0523] Step 6: The device displays the answer to the user.

[0524] The terminal displays the response received from the server on the user interface. The response content is displayed in an easy-to-read format so that the user can easily check it.

[0525] Input: HTTP response received from the server (response generated by a generative AI)

[0526] Output: Answer displayed in the user interface

[0527] Specific action: The device displays a message on the screen stating, "Returns are accepted within 30 days of purchase, along with the receipt."

[0528] Step 7: The user reviews the displayed response and makes further inquiries if necessary.

[0529] The user reviews the displayed answers and, if necessary, enters further detailed questions. If the answers are satisfactory, they then decide on their next course of action.

[0530] Input: Answer displayed in the user interface

[0531] Output: User's next action (entering a more detailed inquiry or deciding on the next course of action)

[0532] Specific operation: The user reviews the displayed answer and, if not satisfied, enters an additional question such as, "Where should I take this item for a return?" This process is repeated.

[0533] (Application Example 1)

[0534] 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."

[0535] Conventional security services have limited ways for users to easily check the security status of their homes or offices. Furthermore, checking the status often required the use of numerous applications and devices, making the process cumbersome. Moreover, in situations requiring real-time status checks and rapid response, there was a possibility of delays in information acquisition and response. This invention aims to solve these problems and provide a system that allows users to easily and quickly check their security status and take appropriate action.

[0536] 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.

[0537] In this invention, the server includes means for the server to check the status in conjunction with a monitoring system, means for a generative artificial intelligence to generate a response in real time based on data from the monitoring system, and means for the server to generate a response based on an inquiry using the API of the generative artificial intelligence and reflect the status in real time by referring to data from the monitoring system. As a result, users can easily check the security status through their terminals and take appropriate action quickly.

[0538] A "user" refers to an individual or legal entity that uses the system.

[0539] A "device" refers to a computer, smartphone, tablet, or any other device that can connect to the internet and is operated by a user.

[0540] "Inquiry" refers to the content of questions or requests entered by the user via their device.

[0541] A "server" refers to a remote computing system that receives inquiries from terminals, generates answers using generative artificial intelligence, and sends those answers back to the terminals.

[0542] "Generative artificial intelligence" refers to artificial intelligence technology that analyzes user inquiries and generates appropriate answers, such as systems that use large-scale language models.

[0543] "Natural language processing technology" refers to the technology used by generative artificial intelligence to analyze natural language queries input by users, understand their meaning, and generate appropriate responses.

[0544] "API" refers to an application programming interface for using generative artificial intelligence, and is a protocol for efficiently exchanging data between a server and artificial intelligence.

[0545] "Surveillance systems" refer to a general term for internet-connected devices used for security purposes, such as security cameras, door sensors, and smart locks.

[0546] "Status check" refers to understanding the current security status based on information obtained through the monitoring system.

[0547] "Real-time" refers to providing near-instantaneous responses to user inquiries.

[0548] This invention provides a system that allows users to easily check the security status of their home or office and take quick action. This system consists of four main elements: the user, the terminal, the server, and the monitoring system. The following describes each element and its operation in detail.

[0549] 1. User actions

[0550] The user first accesses the terminal and enters a security-related inquiry. The terminal can be a computer, smartphone, tablet, or other device connected to the internet. User inquiries cover a wide range of topics, such as checking security camera footage or the status of door locks.

[0551] Specific example

[0552] The user enters "Please tell me if the front door is currently locked" into the terminal's interface.

[0553] 2. Device operation

[0554] The terminal receives the inquiry entered by the user and sends it to the server. The terminal uses communication protocols such as HTTP requests to transfer the user's input to the server in an appropriate manner.

[0555] Specific example

[0556] After the terminal receives user input, it sends the query details to the server as a POST request.

[0557] 3. Server operation

[0558] The server analyzes inquiries received from terminals and generates responses using generative artificial intelligence (AI). The AI ​​uses, for example, large-scale language models (such as GPT-3) to generate the optimal response to user inquiries. The server also works in conjunction with a monitoring system, acquiring data from the monitoring system in real time and using the AI's API to generate appropriate responses.

[0559] Specific example

[0560] The server, in response to the query "Is the front door currently locked?", checks the status of the smart lock and generates the response "The front door is currently locked."

[0561] 4. Display by device

[0562] The terminal displays the response received from the server to the user. At this stage, the user can obtain the appropriate answer.

[0563] Specific example

[0564] The device displays the response to the user: "The front door is currently locked."

[0565] 5. Program Details

[0566] The system is implemented using programming languages ​​such as Python. User inquiries are sent to the server via HTTP requests, and the server, in conjunction with monitoring systems such as smart locks and security cameras, acquires data in real time and generates answers using a generative artificial intelligence (e.g., GPT-3) API. The generated answers are sent back to the user's device and displayed.

[0567] Example of a prompt

[0568] "Please tell me if the front door is currently locked."

[0569] "Show me the latest footage from the security camera in the living room."

[0570] This system allows users to intuitively check the security status of their home or office and take quick action, which is expected to improve security levels.

[0571] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0572] Step 1:

[0573] The user enters the inquiry into the device.

[0574] The user enters security-related questions into the terminal. For example, they might enter a question like, "Please tell me if the front door is currently locked."

[0575] Input: User inquiry text (e.g., "Please tell me if the front door is currently locked.")

[0576] Output: Query content as a string

[0577] Step 2:

[0578] The device sends the query to the server.

[0579] The terminal sends the user's input query to the server as an HTTP request. The POST method is used to transfer the data.

[0580] Input: User's query text, server URL

[0581] Output: HTTP POST request sent to the server

[0582] Step 3:

[0583] The server receives the query.

[0584] The server receives queries sent from terminals and analyzes their content. It converts the query content into data frames or dictionary formats for further analysis.

[0585] Input: HTTP POST request from terminal

[0586] Output: Analyzed query content (e.g., "Check the lock status of the front door")

[0587] Step 4:

[0588] The server uses generative artificial intelligence to generate the answer.

[0589] The server generates the optimal response based on the analyzed query content, using a generative artificial intelligence (e.g., GPT-3) API. The generative AI generates a natural-sounding response using a pre-trained language model.

[0590] Input: Analyzed query content

[0591] Output: Generated response text (e.g., "The front door is currently locked.")

[0592] Step 5:

[0593] The server works in conjunction with the monitoring system to check the status.

[0594] The server interacts with monitoring systems such as smart locks and security cameras via APIs to acquire data in real time. Based on this information, generative artificial intelligence generates responses that reflect the situation.

[0595] Input: Analyzed query content, monitoring system API response

[0596] Output: Real-time data from the monitoring system

[0597] Step 6:

[0598] The server sends the generated response to the terminal.

[0599] The server sends the generated response back to the terminal as an HTTP response. During this process, the data is transferred in JSON format or other formats as needed.

[0600] Input: Generated response text

[0601] Output: HTTP response sent to the terminal

[0602] Step 7:

[0603] The device displays the answer to the user.

[0604] The terminal displays the response received from the server to the user. At this stage, the user can obtain an appropriate answer to their inquiry.

[0605] Input: Response text from the server

[0606] Output: The response will be displayed in a format viewable by the user (e.g., "The front door is currently locked").

[0607] Through these specific processing steps, users can intuitively check the security status of their home or office and take quick action.

[0608] 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.

[0609] This invention is a system that combines a user inputting an inquiry with a generative artificial intelligence system providing a natural response, along with an emotion engine that recognizes the user's emotions. This system consists of four main elements: the user, the terminal, the server, and the emotion engine.

[0610] User actions

[0611] Users first access the system via a device and enter their inquiry. This device can be any internet-connected device, such as a computer, smartphone, or tablet. Inquiries cover a wide range of topics, including product details, service usage instructions, and return policy questions.

[0612] Specific example

[0613] The user enters "Please tell me about returning the product" into the terminal's interface.

[0614] Terminal operation

[0615] The terminal receives the query entered by the user and prepares to send it to the server. The terminal uses communication protocols such as HTTP requests to transfer the user's input to the server in an appropriate manner.

[0616] Specific example

[0617] After the terminal receives user input, it sends the query details to the server as an HTTP POST request.

[0618] Server operation

[0619] The server analyzes the inquiry received from the terminal and generates a response using generative artificial intelligence. For example, the generative AI uses a large-scale language model to generate the optimal response to the user's inquiry.

[0620] When the server analyzes the query, it uses an emotion engine to recognize the user's emotions. The emotion engine uses natural language processing techniques to identify emotions from the user's input. This emotion information is used as reference information when the generative artificial intelligence generates a response.

[0621] Specific example

[0622] The server, in response to a user's inquiry, "Please tell me about product returns," uses generative artificial intelligence to generate a specific answer such as, "Returns are accepted within 30 days of purchase, along with the receipt." If the emotion engine detects the user's impatience or dissatisfaction, it adds a reassuring phrase to the answer, such as, "Please feel free to contact us."

[0623] Server-side transmission and terminal-side reception.

[0624] The server sends the generated response to the terminal. The terminal displays the response received from the server to the user.

[0625] Specific example

[0626] The server generates a response saying, "Returns are accepted within 30 days of purchase, along with the receipt. Please feel free to contact us," and sends it to the device. The device then displays this message to the user.

[0627] User verification

[0628] The user reviews the answers displayed on the device and decides on the next action based on that information. If the user requires further detailed questions, they enter the inquiry again, and the process is repeated.

[0629] This system allows users to receive natural and appropriate answers quickly, and by recognizing their emotions, they can receive more personalized and satisfying service. This improves customer satisfaction and enables a more accurate understanding of customer needs. This system will function effectively in industries such as retail, where inquiries are diverse, and will be an important tool for providing advanced customer service.

[0630] The following describes the processing flow.

[0631] Step 1:

[0632] The user enters their inquiry. The user accesses the terminal interface and enters their inquiry into the text box. For example, they might enter, "Please tell me about returning a product."

[0633] Step 2:

[0634] The terminal receives the user's inquiry. When the user clicks the submit button, the terminal retrieves the input and stores the inquiry content in a variable.

[0635] Step 3:

[0636] The device sends a query to the server. The device generates an HTTP POST request containing the query details and sends it to the specified endpoint on the server.

[0637] Step 4:

[0638] The server receives the query. The server receives the HTTP POST request sent from the terminal and extracts the query content from the request body.

[0639] Step 5:

[0640] The server analyzes the query. Before passing the query to the generative artificial intelligence, the server uses an emotion engine to analyze the user's emotions. The emotion engine uses natural language processing techniques to identify emotional states (e.g., joy, anger, sadness, etc.) within the text.

[0641] Step 6:

[0642] The server passes the query to the generative artificial intelligence based on the emotion analysis results. The server considers the emotion information obtained by the emotion engine and provides the generative artificial intelligence with appropriate context.

[0643] Step 7:

[0644] Generative artificial intelligence generates the answer. Based on the inquiry content and emotional information, the generative AI generates the most suitable answer. For example, if the emotional engine recognizes that the user's emotion is "anxiety," the generative AI will select words that provide reassurance.

[0645] Step 8:

[0646] The server retrieves the generated response. The server receives the response returned by the generative artificial intelligence and verifies its contents.

[0647] Step 9:

[0648] The server sends the response to the device. The server then sends the received response back to the device as an HTTP response. This response contains the generated response.

[0649] Step 10:

[0650] The terminal receives the response. The terminal receives the HTTP response sent from the server and extracts the response content from the response body.

[0651] Step 11:

[0652] The device displays the response to the user. The device displays the extracted response content on the user interface. For example, the displayed response might be, "Returns are accepted within 30 days of purchase, along with the receipt. Please feel free to contact us."

[0653] Step 12:

[0654] The user confirms the answer. The user checks the answer displayed on their device and confirms that it is the correct answer to their inquiry. If necessary, the user re-enters the question, and the process is repeated.

[0655] This system allows users to quickly obtain natural and appropriate responses that take emotions into account, generated by generative artificial intelligence. As a result, the user experience will improve, and customer satisfaction will increase.

[0656] (Example 2)

[0657] 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".

[0658] Traditional user inquiry systems often provide mechanical responses without considering the user's feelings. This leads to decreased user satisfaction and a problem in that they cannot adequately address users who are particularly dissatisfied or anxious. Furthermore, it is difficult to respond quickly and individually to a wide range of inquiries, making it challenging to provide efficient customer service.

[0659] 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 recognizing the user's emotions using an emotion analysis engine, means for generating a response using a generative artificial intelligence model, and means for transmitting the generated response to a terminal. This makes it possible to provide more individualized and satisfying responses that take the user's emotions into consideration. Furthermore, it enables the provision of efficient customer service by allowing for quick and accurate responses to a wide range of inquiries.

[0660] "User" refers to a typical user who uses the system to enter inquiries.

[0661] A "terminal" refers to a device (such as a computer, smartphone, or tablet) that a user uses to access the internet and enter inquiries.

[0662] "Inquiry" refers to the content of questions or requests that users enter into the system.

[0663] A "server" refers to a device or software that receives user inquiries, analyzes their content, generates appropriate responses, and sends them to the user's terminal.

[0664] A "sentiment analysis engine" refers to an engine that uses natural language processing technology to recognize the emotions expressed in a user's inquiry.

[0665] A "generative artificial intelligence model" refers to a large-scale language model designed to generate appropriate responses based on user inquiries.

[0666] "Answer" refers to the information and guidance that the server generates using a generative artificial intelligence model and presents to the user.

[0667] "Natural language processing technology" refers to the technology used to analyze input text data, understand its meaning, and generate appropriate output.

[0668] "API" is an abbreviation for Application Programming Interface, and refers to an interface for using external functions such as generative artificial intelligence models.

[0669] This invention relates to a system in which a user inputs an inquiry, and a generative artificial intelligence provides a natural response, while also recognizing the user's emotions and providing a response appropriate to those emotions. This system consists of four main elements: the user, the terminal, the server, and the emotion analysis engine.

[0670] The user first accesses a terminal and enters their inquiry. Any internet-connected device can be used as the terminal, including computers, smartphones, and tablets. Once the user enters their inquiry, the terminal prepares to send it to the server. HTTP requests are the common communication protocol. For example, if the user enters "Please tell me about product returns," the terminal sends this inquiry to the server as an HTTP POST request.

[0671] The server analyzes inquiries received from terminals and generates responses using generative artificial intelligence (AI). It is desirable to use a large-scale language model (e.g., GPT-3) for this generative AI. When the server analyzes the inquiry, it uses an emotion analysis engine to recognize the user's emotions. The emotion analysis engine identifies emotions from the user's input using natural language processing techniques. This emotion information is used as reference information when the generative AI generates responses.

[0672] As a concrete example, when a server receives a user inquiry such as "Please tell me about product returns," it uses generative artificial intelligence to generate a specific response such as "Returns are accepted within 30 days of purchase, along with the receipt." In this case, if the sentiment analysis engine detects the user's impatience or dissatisfaction, it adds a reassuring phrase to the response, such as "Please feel free to contact us."

[0673] The generated response is sent from the server to the terminal, where it is displayed to the user. The user reviews the response displayed on the terminal and decides on the next action based on that information. For example, if the user is satisfied, they proceed with the action; if further detailed questions are needed, they enter the inquiry again, and the process is repeated.

[0674] This system allows users to receive natural and appropriate answers quickly, and by recognizing their emotions, they can receive more personalized and satisfying service. This improves customer satisfaction and enables a more accurate understanding of customer needs. This system is particularly effective in industries where a wide range of inquiries occur, and it becomes an important tool for providing high-quality customer service.

[0675] Hardware and software to be used

[0676] Hardware: Computers, smartphones, tablets

[0677] Software: Web browser, HTTP communication protocol, generative artificial intelligence (GPT-3, etc.), sentiment analysis engine

[0678] Examples of prompts for generative AI models

[0679] User inquiry: "Please tell me about returning products."

[0680] Emotional analysis results: "Impatience, dissatisfaction"

[0681] Prompt: "User inquiry: Please tell me about returning the product. User's emotional state: Anxious, dissatisfied. Generate a reassuring response for the user."

[0682] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0683] Step 1:

[0684] The user accesses the terminal and enters their inquiry. When the user enters their inquiry (e.g., "Please tell me about returning a product"), data is entered into the terminal. The terminal receives this data and prepares for the next step. Specifically, the user opens a web browser and enters their inquiry into a web form. This input is in the form of text data.

[0685] Step 2:

[0686] The terminal processes user input and prepares it for transmission to the server. It converts the input string data into an HTTP POST request format. Specifically, it uses JavaScript or similar tools to capture the input data and construct the HTTP POST request. By sending the constructed request to the server, the data flows to the next step. The input is string data, and the output is an HTTP POST request.

[0687] Step 3:

[0688] The server parses the HTTP POST request received from the terminal. It extracts the inquiry content from the request body and takes it out as string data. Specifically, the server receives an HTTP request and parses the user's inquiry, "Please tell me about returning the product," from its body. Using this analysis result, it prepares a prompt for the generative artificial intelligence. The input is the HTTP POST request, and the output is the parsed string data.

[0689] Step 4:

[0690] The server sends the analyzed query to the sentiment analysis engine to recognize the user's emotions. The input string data is passed to the sentiment analysis engine, which processes it. Natural language processing techniques are used to identify the user's emotions (e.g., "anxiety, dissatisfaction"). Specifically, the query text is sent to the sentiment analysis engine, and emotion data such as "anxiety, dissatisfaction" is obtained as a result of the analysis. The input is the analyzed string data, and the output is the emotion data.

[0691] Step 5:

[0692] The server sends a prompt to a generative AI model (e.g., GPT-3) based on the inquiry content and sentiment data to generate a response. The inquiry content and sentiment information are combined to form the prompt. For example, the prompt "User inquiry: Please tell me about product returns. User sentiment state: Anxious, dissatisfied. Please generate a response that will reassure the user." is sent to the generative AI model. The generative AI model then generates a response based on this (e.g., "Returns are accepted within 30 days of purchase, along with the receipt. Please feel free to contact us."). The input is the prompt, and the output is the generated response.

[0693] Step 6:

[0694] The server sends the generated response to the terminal. It wraps the generated response as an HTTP response and sends it back to the terminal. Specifically, it sends the output of the generating AI model to the terminal in an HTTP response. Upon receiving this response, the terminal proceeds to the next step. The input is the generated response, and the output is the HTTP response.

[0695] Step 7:

[0696] The terminal displays the response received from the server to the user. It parses the HTTP response, extracts it as string data, and displays it in the browser's user interface. Specifically, the terminal receives the HTTP response and displays the response "Returns are accepted within 30 days of purchase, along with the receipt. Please feel free to contact us" in the inquiry form. The input is the HTTP response, and the output is the response displayed in the user interface.

[0697] Step 8:

[0698] The user reviews the answer displayed on their device and decides on their next action. If necessary, they re-enter their inquiry, and the process is repeated. Specifically, the user reads the displayed answer, takes action if they are satisfied, or enters a new question into the web form if further details are needed. The input is the displayed answer, and the output is the user's next action.

[0699] (Application Example 2)

[0700] 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."

[0701] Conventional inquiry systems using generative artificial intelligence have the problem of not recognizing user emotions and providing uniform answers, making it difficult to respond flexibly according to the user's emotions and circumstances. In particular, in situations where direct interaction with customers is important, such as in physical stores, it is necessary to understand the emotions of individual customers and provide appropriate support. Therefore, if a system that can recognize user emotions and provide appropriate responses is realized, it will be possible to significantly improve customer satisfaction.

[0702] 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.

[0703] In this invention, the server includes means for the user to input an inquiry into a terminal, means for the terminal to send the inquiry to the server, means for the server to receive the inquiry and generate an answer using generative artificial intelligence, means for the terminal to recognize the user's emotions through emotion analysis, means for adjusting the generative artificial intelligence's answer based on the emotion analysis, means for the server to send the generated answer to the terminal, and means for the terminal to display the answer to the user. This enables more personalized responses that respond to the user's emotions. Specifically, when a user makes an inquiry such as "Please tell me about returning a product," the emotion analysis means can be used to analyze the user's emotions and provide a reassuring answer based on the results. This is expected to improve the quality of the user experience and increase customer satisfaction in physical stores.

[0704] A "user" is an individual or organization that makes an inquiry using a terminal.

[0705] A "terminal" is an internet-connected device used to process user-inputted inquiries and communicate with a server.

[0706] A "server" is a central processing unit that receives inquiries from users, generates responses using generative artificial intelligence and sentiment analysis methods, and transmits them to terminals.

[0707] "Generative artificial intelligence" refers to an artificial intelligence system that uses natural language processing technology to analyze user inquiries and generate appropriate responses.

[0708] "Emotion analysis means" refers to analytical technology that recognizes emotions from user input and adjusts responses based on those emotions.

[0709] "Natural language processing technology" refers to technologies that enable computers to understand, interpret, and generate human language.

[0710] "Answer" refers to information or instructions provided by a generative artificial intelligence system in response to a user's inquiry.

[0711] "Adjustment" refers to the process of modifying the responses generated by generative artificial intelligence in accordance with the user's emotions.

[0712] "Inquiry" refers to the process by which a user enters information they want to know or a problem they want to solve in language.

[0713] "Display" refers to outputting the answers generated on the device in a format that allows the user to visually confirm them.

[0714] This invention is a system that combines a system in which a user inputs an inquiry and a generative artificial intelligence provides a natural response, with an emotion analysis means that recognizes the user's emotions. The system consists of four main elements: the user, the terminal, the server, and the emotion analysis means.

[0715] User actions

[0716] The user first accesses a terminal and enters their inquiry. The terminal can be any internet-connected device, such as a computer, smartphone, or tablet. Inquiries cover a wide range of topics, including product details, service usage instructions, and return policy. For example, the user might enter "What is the warranty period for this product?" into the terminal's interface.

[0717] Terminal operation

[0718] The terminal receives the user's input and prepares to send it to the server. The terminal uses a communication protocol such as an HTTP request to transfer the user's input to the server in an appropriate manner. For example, after the terminal receives the user's input, it sends the content of the inquiry to the server as an HTTP POST request.

[0719] Server operation

[0720] The server analyzes inquiries received from terminals and generates responses using generative artificial intelligence (AI). The generative AI, for example, uses a large-scale language model to generate the optimal response to the user's inquiry. When the server analyzes the inquiry, it uses sentiment analysis to recognize the user's emotions. Sentiment analysis uses natural language processing techniques to identify emotions from the user's input. This emotion information is used as reference information when the generative AI generates responses.

[0721] Specific example

[0722] The server uses generative artificial intelligence to generate a response to the user's inquiry, "What is the warranty period for this product?", stating, "The warranty period is one year." If the sentiment analysis system detects the user's impatience or dissatisfaction, it adds a reassuring phrase to the response, such as, "Please feel free to contact us if you have any concerns."

[0723] Server-side transmission and terminal-side reception.

[0724] The server sends the generated response to the terminal. The terminal displays the response received from the server to the user. For example, the server generates the response "The warranty period is one year. Please feel free to contact us if you have any concerns" and sends it to the terminal. The terminal then displays this content to the user.

[0725] User verification

[0726] The user reviews the answers displayed on the device and decides on the next action based on that information. If the user requires further detailed questions, they enter the inquiry again, and the process is repeated.

[0727] This system allows users to receive natural and appropriate answers quickly, and by recognizing their emotions, they can receive more personalized and satisfying service. This improves customer satisfaction and enables a more accurate understanding of customer needs. This system will function effectively in industries such as retail, where inquiries are diverse, and will be an important tool for providing advanced customer service.

[0728] Example of a prompt:

[0729] User: "What is the warranty period for this product?"

[0730] Answer: "The warranty period is one year. If you have any concerns, please feel free to contact us."

[0731] This invention is a system that improves customer support in physical stores by combining generative artificial intelligence and emotion analysis means. The specific program processing sequence and technical details will be explained by clearly outlining how each element interacts and describing how users utilize the system.

[0732] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0733] Step 1:

[0734] User input

[0735] The user accesses the terminal and enters their inquiry into the interface. For example, the user might ask, "What is the warranty period for this product?"

[0736] Input: User inquiry

[0737] Output: Inquiry data from the terminal

[0738] Specific action: The user enters text into an input field displayed on the device screen and presses the send button.

[0739] Step 2:

[0740] Data transfer by terminal

[0741] The terminal receives the query entered by the user and prepares to send it to the server. Specifically, it uses an HTTP POST request to forward the user's query to the server.

[0742] Input: User inquiry data

[0743] Output: HTTP request sent to the server

[0744] Specific operation: The terminal converts the query data into an HTTP request format and sends it to the specified endpoint on the server.

[0745] Step 3:

[0746] Server receives and analyzes queries.

[0747] The server analyzes the inquiries received from the terminal and generates the optimal answer using generative artificial intelligence. First, it analyzes the received data and inputs it into the generative artificial intelligence model.

[0748] Input: HTTP request received from the terminal

[0749] Output: Input data for the generative AI model

[0750] Specific operation: The server extracts the query content from the HTTP request and converts it into a format that can be input into the generating AI model.

[0751] Step 4:

[0752] Emotion recognition using emotion analysis methods

[0753] Before passing the query content to the generative artificial intelligence, the server uses sentiment analysis to recognize the user's emotions. Sentiment analysis uses natural language processing techniques to identify emotions from the user's input.

[0754] Input: Inquiry details

[0755] Output: Sentiment analysis results (e.g., positive, negative, etc.)

[0756] Specific operation: Analyze the question text and calculate sentiment polarity using a sentiment recognition algorithm.

[0757] Step 5:

[0758] Answer generation using generative artificial intelligence

[0759] The server feeds back the emotion analysis results to the generative artificial intelligence system, which then generates the optimal response tailored to the user's emotions.

[0760] Input: Inquiry details, sentiment analysis results

[0761] Output: Generated answer

[0762] Specific operation: The AI ​​model receives the inquiry content and sentiment analysis results, and generates an appropriate response.

[0763] Step 6:

[0764] Server-based response submission

[0765] The server sends the generated response to the terminal.

[0766] Input: Generated answer

[0767] Output: Answer sent to the terminal

[0768] Specific action: The generated response is sent back to the terminal as an HTTP response.

[0769] Step 7:

[0770] Display of answers via device

[0771] The terminal displays the response received from the server to the user.

[0772] Input: Response received from the server

[0773] Output: Answer displayed to the user

[0774] Specific action: Display the answer text on the device screen so the user can confirm it.

[0775] The user reviews the answers displayed on the device, and if further questions are needed, they re-enter their inquiry, and the process is repeated.

[0776] Example of a prompt:

[0777] User: "What is the warranty period for this product?"

[0778] Answer: "The warranty period is one year. If you have any concerns, please feel free to contact us."

[0779] 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.

[0780] 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.

[0781] 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.

[0782] [Third Embodiment]

[0783] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0784] 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.

[0785] 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).

[0786] 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.

[0787] 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.

[0788] 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).

[0789] 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.

[0790] 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.

[0791] 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.

[0792] 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.

[0793] 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.

[0794] 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".

[0795] This invention is a system in which a user inputs an inquiry, and a generative artificial intelligence provides a natural-sounding response. This system consists of three main elements: the user, the terminal, and the server.

[0796] User actions

[0797] Users first access the system via a device and enter their inquiry. This device can be any internet-connected device, such as a computer, smartphone, or tablet. Inquiries cover a wide range of topics, including product details, service usage instructions, and return policy questions.

[0798] Specific example

[0799] The user enters "Please tell me about returning the product" into the terminal's interface.

[0800] Terminal operation

[0801] The terminal receives the inquiry entered by the user and sends it to the server. The terminal uses communication protocols such as HTTP requests to transfer the user's input to the server in an appropriate manner.

[0802] Specific example

[0803] After the terminal receives user input, it sends the query details to the server as a POST request.

[0804] Server operation

[0805] The server analyzes the inquiry received from the terminal and generates a response using generative artificial intelligence. The generative AI uses, for example, a large-scale language model (such as GPT-3) to generate the optimal response to the user's inquiry.

[0806] The server uses a generative artificial intelligence API to analyze the inquiry and obtain an appropriate response. In this process, the generative AI generates a natural and appropriate response based on relevant information. The generated response is then sent back from the server to the terminal.

[0807] Specific example

[0808] The server uses generative artificial intelligence to generate an appropriate response to a user's inquiry, "Please tell me about product returns," such as "Returns are accepted within 30 days of purchase, along with the receipt."

[0809] Display by device

[0810] The terminal displays the response received from the server to the user. At this stage, the user can obtain the appropriate answer.

[0811] Specific example

[0812] The device displays a message to the user stating, "Returns are accepted within 30 days of purchase, along with the receipt."

[0813] User verification

[0814] The user reviews the answers displayed on the device and decides on the next action based on that information. If the user requires further detailed questions, they enter the inquiry again, and the process is repeated.

[0815] This system allows users to quickly obtain natural and appropriate answers, improving customer satisfaction and enabling a more accurate understanding of customer needs. This system will function effectively in industries such as retail, where inquiries are diverse, and will serve as an important tool for providing high-quality customer service.

[0816] The following describes the processing flow.

[0817] Step 1:

[0818] The user enters their inquiry. The user accesses the terminal interface and enters the inquiry as text. In this case, the inquiry may be entered in the format of "Please tell me about returning a product."

[0819] Step 2:

[0820] The device receives the user's inquiry. When the user clicks the submit button, the device checks the input and retrieves the inquiry text.

[0821] Step 3:

[0822] The terminal sends the query to the server. The terminal sends the retrieved query text to the server as an HTTP POST request. This request contains the query details.

[0823] Step 4:

[0824] The server receives the query. The server receives the request sent from the terminal and extracts the query content from the request body.

[0825] Step 5:

[0826] The server analyzes the query content. The server uses natural language processing techniques to analyze the extracted query content and understand its intent. This analysis includes extracting the query context and key keywords.

[0827] Step 6:

[0828] The server generates an answer using generative artificial intelligence. The server passes the analysis results to the generative AI's API, which then generates an appropriate answer. In this process, the generative AI is designed to generate the answer that is most suitable for the input query.

[0829] Step 7:

[0830] The server retrieves the generated response. The response returned by the generative artificial intelligence is retrieved, and its content is checked. For example, the generated response might be something specific like, "Returns are accepted within 30 days of purchase, along with the receipt."

[0831] Step 8:

[0832] The server sends the response to the device. The server returns the retrieved response to the device as an HTTP response. This response contains the generated response.

[0833] Step 9:

[0834] The terminal receives the response. The terminal receives the response sent from the server and extracts the response content from the response body.

[0835] Step 10:

[0836] The device displays the answer to the user. The device displays the extracted answer content on the user interface. At this time, the answer displayed is "Returns are accepted within 30 days of purchase, along with the receipt."

[0837] Step 11:

[0838] The user confirms the answer. The user checks the answer displayed on the device and understands that it is the correct answer to their inquiry. If necessary, the user enters further detailed questions, and the same process is repeated.

[0839] (Example 1)

[0840] 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."

[0841] Traditional inquiry handling systems struggled to provide timely and appropriate answers to user inquiries. This was particularly true when inquiries covered a wide range of topics, requiring complex analysis and response generation. As a result, customer satisfaction declined, and response costs increased.

[0842] 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.

[0843] In this invention, the server includes means for analyzing a query and calling a generative artificial intelligence API, means for the generative artificial intelligence to generate an appropriate answer, and means for transmitting the generated answer to a terminal. This makes it possible to provide a quick and appropriate answer to a user's query.

[0844] A "user" refers to a person who accesses the system and enters an inquiry.

[0845] A "terminal" refers to a device used by a user to input inquiries and communicate with a server. Examples include computers, smartphones, and tablets.

[0846] A "server" refers to a device that receives user inquiries, calls a generative artificial intelligence API to generate answers, and sends those answers to the terminal.

[0847] "Inquiry" refers to the content of questions or requests that users enter through their devices.

[0848] "Generative artificial intelligence" refers to a technology that uses large-scale language models and other tools to generate appropriate answers to input inquiries.

[0849] An "API" refers to a program interface provided by a generative artificial intelligence system, which a server uses to call the functions of the generative artificial intelligence system.

[0850] "Answer" refers to the response generated by a generative artificial intelligence system based on the user's inquiry.

[0851] "Analysis" refers to the process by which a server understands the content of a query received from a user and provides appropriate information to a generative artificial intelligence system based on that understanding.

[0852] "Display" refers to the act of a device visually providing the user with the response received from the server.

[0853] This invention is a system in which a user inputs an inquiry into a terminal, and a generative artificial intelligence provides a natural-sounding response. This system consists of three main elements: the user, the terminal, and the server.

[0854] User actions

[0855] Users first access the system via a device and enter their inquiry. This device can be any internet-connected device, such as a computer, smartphone, or tablet. Inquiries cover a wide range of topics, including product details, service usage instructions, and return policy questions.

[0856] Specific example

[0857] The user enters "Please tell me about returning the product" into the terminal's interface.

[0858] Terminal operation

[0859] The terminal receives the inquiry entered by the user and sends it to the server. The terminal uses communication protocols such as HTTP requests to transfer the user's input to the server in an appropriate manner.

[0860] Specific example

[0861] After the terminal receives user input, it sends the query details to the server as a POST request.

[0862] Server operation

[0863] The server analyzes the inquiry received from the terminal and generates a response using generative artificial intelligence (AI). The generative AI uses a large-scale language model (e.g., GPT-3) to generate the optimal response to the user's inquiry. The server uses the generative AI's API to analyze the inquiry and obtain an appropriate response. In this process, the generative AI generates a natural and appropriate response based on relevant information. The generated response is then sent back from the server to the terminal.

[0864] Specific example

[0865] The server uses generative artificial intelligence to generate an appropriate response to a user's inquiry, "Please tell me about product returns," such as "Returns are accepted within 30 days of purchase, along with the receipt."

[0866] Display by device

[0867] The terminal displays the response received from the server to the user. At this stage, the user can obtain the appropriate answer.

[0868] Specific example

[0869] The device displays a message to the user stating, "Returns are accepted within 30 days of purchase, along with the receipt."

[0870] User verification

[0871] The user reviews the answers displayed on the device and decides on the next action based on that information. If the user requires further detailed questions, they enter the inquiry again, and the process is repeated.

[0872] Example of a prompt

[0873] The following are specific examples of prompt statements used to query a generative artificial intelligence.

[0874] User: "Please tell me about returning products."

[0875] Through these processes, this system provides an efficient means for users to obtain quick and natural answers. By utilizing generative artificial intelligence, it is possible to respond to inquiries quickly and appropriately.

[0876] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0877] Step 1: The user enters their inquiry.

[0878] The user opens the terminal interface and enters their inquiry. For example, the user might enter "Please tell me about returning an item." This input is then provided as data for the next processing step.

[0879] Input: The content of the inquiry entered by the user on the device (e.g., "Please tell me about returning the product").

[0880] Output: Inquiry content entered into the terminal

[0881] Specific action: The user opens a browser on their smartphone, enters "Please tell me about product returns" into the inquiry form, and presses the submit button.

[0882] Step 2: The device sends the query to the server.

[0883] The terminal sends the inquiry received from the user to the server as an HTTP POST request. This request includes the content of the user's inquiry.

[0884] Input: Inquiry content entered by the user

[0885] Output: HTTP POST request sent to the server

[0886] Specific operation: The terminal sends a user inquiry to the server asking, "Please tell me about returning the product." HTTP is used as the communication protocol for transmission.

[0887] Step 3: The server analyzes the query and calls a generative artificial intelligence API.

[0888] The server analyzes the received query and, based on its content, calls a generative artificial intelligence API. It generates an input prompt for the API and sends the query content.

[0889] Input: HTTP POST request sent to the server (inquiry content)

[0890] Output: Prompt message sent to the generative artificial intelligence API

[0891] Specific operation: The server analyzes the inquiry "Please tell me about product returns" and converts its content into a prompt message to send to a generative artificial intelligence API.

[0892] Step 4: Generative artificial intelligence generates the answer.

[0893] Generative artificial intelligence receives input prompts and generates appropriate responses. For example, it can provide details about return conditions and procedures in a natural-sounding style.

[0894] Input: Prompt sent to a generative AI API

[0895] Output: Answer generated by a generative artificial intelligence system

[0896] Specific operation: The generative artificial intelligence generates the response, "Returns are accepted within 30 days of purchase, along with the receipt."

[0897] Step 5: The server sends the generated response to the terminal.

[0898] The server sends the response received from the generative artificial intelligence back to the terminal. This return is done as an HTTP response.

[0899] Input: Answer generated by a generative artificial intelligence system

[0900] Output: HTTP response sent to the terminal

[0901] Specific action: The server sends an HTTP response to the terminal stating, "Returns are accepted within 30 days of purchase, along with the receipt."

[0902] Step 6: The device displays the answer to the user.

[0903] The terminal displays the response received from the server on the user interface. The response content is displayed in an easy-to-read format so that the user can easily check it.

[0904] Input: HTTP response received from the server (response generated by a generative AI)

[0905] Output: Answer displayed in the user interface

[0906] Specific action: The device displays a message on the screen stating, "Returns are accepted within 30 days of purchase, along with the receipt."

[0907] Step 7: The user reviews the displayed response and makes further inquiries if necessary.

[0908] The user reviews the displayed answers and, if necessary, enters further detailed questions. If the answers are satisfactory, they then decide on their next course of action.

[0909] Input: Answer displayed in the user interface

[0910] Output: User's next action (entering a more detailed inquiry or deciding on the next course of action)

[0911] Specific operation: The user reviews the displayed answer and, if not satisfied, enters an additional question such as, "Where should I take this item for a return?" This process is repeated.

[0912] (Application Example 1)

[0913] 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."

[0914] Conventional security services have limited ways for users to easily check the security status of their homes or offices. Furthermore, checking the status often required the use of numerous applications and devices, making the process cumbersome. Moreover, in situations requiring real-time status checks and rapid response, there was a possibility of delays in information acquisition and response. This invention aims to solve these problems and provide a system that allows users to easily and quickly check their security status and take appropriate action.

[0915] 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.

[0916] In this invention, the server includes means for the server to check the status in conjunction with a monitoring system, means for a generative artificial intelligence to generate a response in real time based on data from the monitoring system, and means for the server to generate a response based on an inquiry using the API of the generative artificial intelligence and reflect the status in real time by referring to data from the monitoring system. As a result, users can easily check the security status through their terminals and take appropriate action quickly.

[0917] A "user" refers to an individual or legal entity that uses the system.

[0918] A "device" refers to a computer, smartphone, tablet, or any other device that can connect to the internet and is operated by a user.

[0919] "Inquiry" refers to the content of questions or requests entered by the user via their device.

[0920] A "server" refers to a remote computing system that receives inquiries from terminals, generates answers using generative artificial intelligence, and sends those answers back to the terminals.

[0921] "Generative artificial intelligence" refers to artificial intelligence technology that analyzes user inquiries and generates appropriate answers, such as systems that use large-scale language models.

[0922] "Natural language processing technology" refers to the technology used by generative artificial intelligence to analyze natural language queries input by users, understand their meaning, and generate appropriate responses.

[0923] "API" refers to an application programming interface for using generative artificial intelligence, and is a protocol for efficiently exchanging data between a server and artificial intelligence.

[0924] "Surveillance systems" refer to a general term for internet-connected devices used for security purposes, such as security cameras, door sensors, and smart locks.

[0925] "Status check" refers to understanding the current security status based on information obtained through the monitoring system.

[0926] "Real-time" refers to providing near-instantaneous responses to user inquiries.

[0927] This invention provides a system that allows users to easily check the security status of their home or office and take quick action. This system consists of four main elements: the user, the terminal, the server, and the monitoring system. The following describes each element and its operation in detail.

[0928] 1. User actions

[0929] The user first accesses the terminal and enters a security-related inquiry. The terminal can be a computer, smartphone, tablet, or other device connected to the internet. User inquiries cover a wide range of topics, such as checking security camera footage or the status of door locks.

[0930] Specific example

[0931] The user enters "Please tell me if the front door is currently locked" into the terminal's interface.

[0932] 2. Device operation

[0933] The terminal receives the inquiry entered by the user and sends it to the server. The terminal uses communication protocols such as HTTP requests to transfer the user's input to the server in an appropriate manner.

[0934] Specific example

[0935] After the terminal receives user input, it sends the query details to the server as a POST request.

[0936] 3. Server operation

[0937] The server analyzes inquiries received from terminals and generates responses using generative artificial intelligence (AI). The AI ​​uses, for example, large-scale language models (such as GPT-3) to generate the optimal response to user inquiries. The server also works in conjunction with a monitoring system, acquiring data from the monitoring system in real time and using the AI's API to generate appropriate responses.

[0938] Specific example

[0939] The server, in response to the query "Is the front door currently locked?", checks the status of the smart lock and generates the response "The front door is currently locked."

[0940] 4. Display by device

[0941] The terminal displays the response received from the server to the user. At this stage, the user can obtain the appropriate answer.

[0942] Specific example

[0943] The device displays the response to the user: "The front door is currently locked."

[0944] 5. Program Details

[0945] The system is implemented using programming languages ​​such as Python. User inquiries are sent to the server via HTTP requests, and the server, in conjunction with monitoring systems such as smart locks and security cameras, acquires data in real time and generates answers using a generative artificial intelligence (e.g., GPT-3) API. The generated answers are sent back to the user's device and displayed.

[0946] Example of a prompt

[0947] "Please tell me if the front door is currently locked."

[0948] "Show me the latest footage from the security camera in the living room."

[0949] This system allows users to intuitively check the security status of their home or office and take quick action, which is expected to improve security levels.

[0950] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0951] Step 1:

[0952] The user enters the inquiry into the device.

[0953] The user enters security-related questions into the terminal. For example, they might enter a question like, "Please tell me if the front door is currently locked."

[0954] Input: User inquiry text (e.g., "Please tell me if the front door is currently locked.")

[0955] Output: Query content as a string

[0956] Step 2:

[0957] The device sends the query to the server.

[0958] The terminal sends the user's input query to the server as an HTTP request. The POST method is used to transfer the data.

[0959] Input: User's query text, server URL

[0960] Output: HTTP POST request sent to the server

[0961] Step 3:

[0962] The server receives the query.

[0963] The server receives queries sent from terminals and analyzes their content. It converts the query content into data frames or dictionary formats for further analysis.

[0964] Input: HTTP POST request from terminal

[0965] Output: Analyzed query content (e.g., "Check the lock status of the front door")

[0966] Step 4:

[0967] The server uses generative artificial intelligence to generate the answer.

[0968] The server generates the optimal response based on the analyzed query content, using a generative artificial intelligence (e.g., GPT-3) API. The generative AI generates a natural-sounding response using a pre-trained language model.

[0969] Input: Analyzed query content

[0970] Output: Generated response text (e.g., "The front door is currently locked.")

[0971] Step 5:

[0972] The server works in conjunction with the monitoring system to check the status.

[0973] The server interacts with monitoring systems such as smart locks and security cameras via APIs to acquire data in real time. Based on this information, generative artificial intelligence generates responses that reflect the situation.

[0974] Input: Analyzed query content, monitoring system API response

[0975] Output: Real-time data from the monitoring system

[0976] Step 6:

[0977] The server sends the generated response to the terminal.

[0978] The server sends the generated response back to the terminal as an HTTP response. During this process, the data is transferred in JSON format or other formats as needed.

[0979] Input: Generated response text

[0980] Output: HTTP response sent to the terminal

[0981] Step 7:

[0982] The device displays the answer to the user.

[0983] The terminal displays the response received from the server to the user. At this stage, the user can obtain an appropriate answer to their inquiry.

[0984] Input: Response text from the server

[0985] Output: The response will be displayed in a format viewable by the user (e.g., "The front door is currently locked").

[0986] Through these specific processing steps, users can intuitively check the security status of their home or office and take quick action.

[0987] 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.

[0988] This invention is a system that combines a user inputting an inquiry with a generative artificial intelligence system providing a natural response, along with an emotion engine that recognizes the user's emotions. This system consists of four main elements: the user, the terminal, the server, and the emotion engine.

[0989] User actions

[0990] Users first access the system via a device and enter their inquiry. This device can be any internet-connected device, such as a computer, smartphone, or tablet. Inquiries cover a wide range of topics, including product details, service usage instructions, and return policy questions.

[0991] Specific example

[0992] The user enters "Please tell me about returning the product" into the terminal's interface.

[0993] Terminal operation

[0994] The terminal receives the query entered by the user and prepares to send it to the server. The terminal uses communication protocols such as HTTP requests to transfer the user's input to the server in an appropriate manner.

[0995] Specific example

[0996] After the terminal receives user input, it sends the query details to the server as an HTTP POST request.

[0997] Server operation

[0998] The server analyzes the inquiry received from the terminal and generates a response using generative artificial intelligence. For example, the generative AI uses a large-scale language model to generate the optimal response to the user's inquiry.

[0999] When the server analyzes the query, it uses an emotion engine to recognize the user's emotions. The emotion engine uses natural language processing techniques to identify emotions from the user's input. This emotion information is used as reference information when the generative artificial intelligence generates a response.

[1000] Specific example

[1001] The server, in response to a user's inquiry, "Please tell me about product returns," uses generative artificial intelligence to generate a specific answer such as, "Returns are accepted within 30 days of purchase, along with the receipt." If the emotion engine detects the user's impatience or dissatisfaction, it adds a reassuring phrase to the answer, such as, "Please feel free to contact us."

[1002] Server-side transmission and terminal-side reception.

[1003] The server sends the generated response to the terminal. The terminal displays the response received from the server to the user.

[1004] Specific example

[1005] The server generates a response saying, "Returns are accepted within 30 days of purchase, along with the receipt. Please feel free to contact us," and sends it to the device. The device then displays this message to the user.

[1006] User verification

[1007] The user reviews the answers displayed on the device and decides on the next action based on that information. If the user requires further detailed questions, they enter the inquiry again, and the process is repeated.

[1008] This system allows users to receive natural and appropriate answers quickly, and by recognizing their emotions, they can receive more personalized and satisfying service. This improves customer satisfaction and enables a more accurate understanding of customer needs. This system will function effectively in industries such as retail, where inquiries are diverse, and will be an important tool for providing advanced customer service.

[1009] The following describes the processing flow.

[1010] Step 1:

[1011] The user enters their inquiry. The user accesses the terminal interface and enters their inquiry into the text box. For example, they might enter, "Please tell me about returning a product."

[1012] Step 2:

[1013] The terminal receives the user's inquiry. When the user clicks the submit button, the terminal retrieves the input and stores the inquiry content in a variable.

[1014] Step 3:

[1015] The device sends a query to the server. The device generates an HTTP POST request containing the query details and sends it to the specified endpoint on the server.

[1016] Step 4:

[1017] The server receives the query. The server receives the HTTP POST request sent from the terminal and extracts the query content from the request body.

[1018] Step 5:

[1019] The server analyzes the query. Before passing the query to the generative artificial intelligence, the server uses an emotion engine to analyze the user's emotions. The emotion engine uses natural language processing techniques to identify emotional states (e.g., joy, anger, sadness, etc.) within the text.

[1020] Step 6:

[1021] The server passes the query to the generative artificial intelligence based on the emotion analysis results. The server considers the emotion information obtained by the emotion engine and provides the generative artificial intelligence with appropriate context.

[1022] Step 7:

[1023] Generative artificial intelligence generates the answer. Based on the inquiry content and emotional information, the generative AI generates the most suitable answer. For example, if the emotional engine recognizes that the user's emotion is "anxiety," the generative AI will select words that provide reassurance.

[1024] Step 8:

[1025] The server retrieves the generated response. The server receives the response returned by the generative artificial intelligence and verifies its contents.

[1026] Step 9:

[1027] The server sends the response to the device. The server then sends the received response back to the device as an HTTP response. This response contains the generated response.

[1028] Step 10:

[1029] The terminal receives the response. The terminal receives the HTTP response sent from the server and extracts the response content from the response body.

[1030] Step 11:

[1031] The device displays the response to the user. The device displays the extracted response content on the user interface. For example, the displayed response might be, "Returns are accepted within 30 days of purchase, along with the receipt. Please feel free to contact us."

[1032] Step 12:

[1033] The user confirms the answer. The user checks the answer displayed on their device and confirms that it is the correct answer to their inquiry. If necessary, the user re-enters the question, and the process is repeated.

[1034] This system allows users to quickly obtain natural and appropriate responses that take emotions into account, generated by generative artificial intelligence. As a result, the user experience will improve, and customer satisfaction will increase.

[1035] (Example 2)

[1036] 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."

[1037] Traditional user inquiry systems often provide mechanical responses without considering the user's feelings. This leads to decreased user satisfaction and a problem in that they cannot adequately address users who are particularly dissatisfied or anxious. Furthermore, it is difficult to respond quickly and individually to a wide range of inquiries, making it challenging to provide efficient customer service.

[1038] 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 recognizing the user's emotions using an emotion analysis engine, means for generating a response using a generative artificial intelligence model, and means for transmitting the generated response to a terminal. This makes it possible to provide more individualized and satisfying responses that take the user's emotions into consideration. Furthermore, it enables the provision of efficient customer service by allowing for quick and accurate responses to a wide range of inquiries.

[1039] "User" refers to a typical user who uses the system to enter inquiries.

[1040] A "terminal" refers to a device (such as a computer, smartphone, or tablet) that a user uses to access the internet and enter inquiries.

[1041] "Inquiry" refers to the content of questions or requests that users enter into the system.

[1042] A "server" refers to a device or software that receives user inquiries, analyzes their content, generates appropriate responses, and sends them to the user's terminal.

[1043] A "sentiment analysis engine" refers to an engine that uses natural language processing technology to recognize the emotions expressed in a user's inquiry.

[1044] A "generative artificial intelligence model" refers to a large-scale language model designed to generate appropriate responses based on user inquiries.

[1045] "Answer" refers to the information and guidance that the server generates using a generative artificial intelligence model and presents to the user.

[1046] "Natural language processing technology" refers to the technology used to analyze input text data, understand its meaning, and generate appropriate output.

[1047] "API" is an abbreviation for Application Programming Interface, and refers to an interface for using external functions such as generative artificial intelligence models.

[1048] This invention relates to a system in which a user inputs an inquiry, and a generative artificial intelligence provides a natural response, while also recognizing the user's emotions and providing a response appropriate to those emotions. This system consists of four main elements: the user, the terminal, the server, and the emotion analysis engine.

[1049] The user first accesses a terminal and enters their inquiry. Any internet-connected device can be used as the terminal, including computers, smartphones, and tablets. Once the user enters their inquiry, the terminal prepares to send it to the server. HTTP requests are the common communication protocol. For example, if the user enters "Please tell me about product returns," the terminal sends this inquiry to the server as an HTTP POST request.

[1050] The server analyzes inquiries received from terminals and generates responses using generative artificial intelligence (AI). It is desirable to use a large-scale language model (e.g., GPT-3) for this generative AI. When the server analyzes the inquiry, it uses an emotion analysis engine to recognize the user's emotions. The emotion analysis engine identifies emotions from the user's input using natural language processing techniques. This emotion information is used as reference information when the generative AI generates responses.

[1051] As a concrete example, when a server receives a user inquiry such as "Please tell me about product returns," it uses generative artificial intelligence to generate a specific response such as "Returns are accepted within 30 days of purchase, along with the receipt." In this case, if the sentiment analysis engine detects the user's impatience or dissatisfaction, it adds a reassuring phrase to the response, such as "Please feel free to contact us."

[1052] The generated response is sent from the server to the terminal, where it is displayed to the user. The user reviews the response displayed on the terminal and decides on the next action based on that information. For example, if the user is satisfied, they proceed with the action; if further detailed questions are needed, they enter the inquiry again, and the process is repeated.

[1053] This system allows users to receive natural and appropriate answers quickly, and by recognizing their emotions, they can receive more personalized and satisfying service. This improves customer satisfaction and enables a more accurate understanding of customer needs. This system is particularly effective in industries where a wide range of inquiries occur, and it becomes an important tool for providing high-quality customer service.

[1054] Hardware and software to be used

[1055] Hardware: Computers, smartphones, tablets

[1056] Software: Web browser, HTTP communication protocol, generative artificial intelligence (GPT-3, etc.), sentiment analysis engine

[1057] Examples of prompts for generative AI models

[1058] User inquiry: "Please tell me about returning products."

[1059] Emotional analysis results: "Impatience, dissatisfaction"

[1060] Prompt: "User inquiry: Please tell me about returning the product. User's emotional state: Anxious, dissatisfied. Generate a reassuring response for the user."

[1061] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1062] Step 1:

[1063] The user accesses the terminal and enters their inquiry. When the user enters their inquiry (e.g., "Please tell me about returning a product"), data is entered into the terminal. The terminal receives this data and prepares for the next step. Specifically, the user opens a web browser and enters their inquiry into a web form. This input is in the form of text data.

[1064] Step 2:

[1065] The terminal processes user input and prepares it for transmission to the server. It converts the input string data into an HTTP POST request format. Specifically, it uses JavaScript or similar tools to capture the input data and construct the HTTP POST request. By sending the constructed request to the server, the data flows to the next step. The input is string data, and the output is an HTTP POST request.

[1066] Step 3:

[1067] The server parses the HTTP POST request received from the terminal. It extracts the inquiry content from the request body and takes it out as string data. Specifically, the server receives an HTTP request and parses the user's inquiry, "Please tell me about returning the product," from its body. Using this analysis result, it prepares a prompt for the generative artificial intelligence. The input is the HTTP POST request, and the output is the parsed string data.

[1068] Step 4:

[1069] The server sends the analyzed query to the sentiment analysis engine to recognize the user's emotions. The input string data is passed to the sentiment analysis engine, which processes it. Natural language processing techniques are used to identify the user's emotions (e.g., "anxiety, dissatisfaction"). Specifically, the query text is sent to the sentiment analysis engine, and emotion data such as "anxiety, dissatisfaction" is obtained as a result of the analysis. The input is the analyzed string data, and the output is the emotion data.

[1070] Step 5:

[1071] The server sends a prompt to a generative AI model (e.g., GPT-3) based on the inquiry content and sentiment data to generate a response. The inquiry content and sentiment information are combined to form the prompt. For example, the prompt "User inquiry: Please tell me about product returns. User sentiment state: Anxious, dissatisfied. Please generate a response that will reassure the user." is sent to the generative AI model. The generative AI model then generates a response based on this (e.g., "Returns are accepted within 30 days of purchase, along with the receipt. Please feel free to contact us."). The input is the prompt, and the output is the generated response.

[1072] Step 6:

[1073] The server sends the generated response to the terminal. It wraps the generated response as an HTTP response and sends it back to the terminal. Specifically, it sends the output of the generating AI model to the terminal in an HTTP response. Upon receiving this response, the terminal proceeds to the next step. The input is the generated response, and the output is the HTTP response.

[1074] Step 7:

[1075] The terminal displays the response received from the server to the user. It parses the HTTP response, extracts it as string data, and displays it in the browser's user interface. Specifically, the terminal receives the HTTP response and displays the response "Returns are accepted within 30 days of purchase, along with the receipt. Please feel free to contact us" in the inquiry form. The input is the HTTP response, and the output is the response displayed in the user interface.

[1076] Step 8:

[1077] The user reviews the answer displayed on their device and decides on their next action. If necessary, they re-enter their inquiry, and the process is repeated. Specifically, the user reads the displayed answer, takes action if they are satisfied, or enters a new question into the web form if further details are needed. The input is the displayed answer, and the output is the user's next action.

[1078] (Application Example 2)

[1079] 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."

[1080] Conventional inquiry systems using generative artificial intelligence have the problem of not recognizing user emotions and providing uniform answers, making it difficult to respond flexibly according to the user's emotions and circumstances. In particular, in situations where direct interaction with customers is important, such as in physical stores, it is necessary to understand the emotions of individual customers and provide appropriate support. Therefore, if a system that can recognize user emotions and provide appropriate responses is realized, it will be possible to significantly improve customer satisfaction.

[1081] 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.

[1082] In this invention, the server includes means for the user to input an inquiry into a terminal, means for the terminal to send the inquiry to the server, means for the server to receive the inquiry and generate an answer using generative artificial intelligence, means for the terminal to recognize the user's emotions through emotion analysis, means for adjusting the generative artificial intelligence's answer based on the emotion analysis, means for the server to send the generated answer to the terminal, and means for the terminal to display the answer to the user. This enables more personalized responses that respond to the user's emotions. Specifically, when a user makes an inquiry such as "Please tell me about returning a product," the emotion analysis means can be used to analyze the user's emotions and provide a reassuring answer based on the results. This is expected to improve the quality of the user experience and increase customer satisfaction in physical stores.

[1083] A "user" is an individual or organization that makes an inquiry using a terminal.

[1084] A "terminal" is an internet-connected device used to process user-inputted inquiries and communicate with a server.

[1085] A "server" is a central processing unit that receives inquiries from users, generates responses using generative artificial intelligence and sentiment analysis methods, and transmits them to terminals.

[1086] "Generative artificial intelligence" refers to an artificial intelligence system that uses natural language processing technology to analyze user inquiries and generate appropriate responses.

[1087] "Emotion analysis means" refers to analytical technology that recognizes emotions from user input and adjusts responses based on those emotions.

[1088] "Natural language processing technology" refers to technologies that enable computers to understand, interpret, and generate human language.

[1089] "Answer" refers to information or instructions provided by a generative artificial intelligence system in response to a user's inquiry.

[1090] "Adjustment" refers to the process of modifying the responses generated by generative artificial intelligence in accordance with the user's emotions.

[1091] "Inquiry" refers to the process by which a user enters information they want to know or a problem they want to solve in language.

[1092] "Display" refers to outputting the answers generated on the device in a format that allows the user to visually confirm them.

[1093] This invention is a system that combines a system in which a user inputs an inquiry and a generative artificial intelligence provides a natural response, with an emotion analysis means that recognizes the user's emotions. The system consists of four main elements: the user, the terminal, the server, and the emotion analysis means.

[1094] User actions

[1095] The user first accesses a terminal and enters their inquiry. The terminal can be any internet-connected device, such as a computer, smartphone, or tablet. Inquiries cover a wide range of topics, including product details, service usage instructions, and return policy. For example, the user might enter "What is the warranty period for this product?" into the terminal's interface.

[1096] Terminal operation

[1097] The terminal receives the user's input and prepares to send it to the server. The terminal uses a communication protocol such as an HTTP request to transfer the user's input to the server in an appropriate manner. For example, after the terminal receives the user's input, it sends the content of the inquiry to the server as an HTTP POST request.

[1098] Server operation

[1099] The server analyzes inquiries received from terminals and generates responses using generative artificial intelligence (AI). The generative AI, for example, uses a large-scale language model to generate the optimal response to the user's inquiry. When the server analyzes the inquiry, it uses sentiment analysis to recognize the user's emotions. Sentiment analysis uses natural language processing techniques to identify emotions from the user's input. This emotion information is used as reference information when the generative AI generates responses.

[1100] Specific example

[1101] The server uses generative artificial intelligence to generate a response to the user's inquiry, "What is the warranty period for this product?", stating, "The warranty period is one year." If the sentiment analysis system detects the user's impatience or dissatisfaction, it adds a reassuring phrase to the response, such as, "Please feel free to contact us if you have any concerns."

[1102] Server-side transmission and terminal-side reception.

[1103] The server sends the generated response to the terminal. The terminal displays the response received from the server to the user. For example, the server generates the response "The warranty period is one year. Please feel free to contact us if you have any concerns" and sends it to the terminal. The terminal then displays this content to the user.

[1104] User verification

[1105] The user reviews the answers displayed on the device and decides on the next action based on that information. If the user requires further detailed questions, they enter the inquiry again, and the process is repeated.

[1106] This system allows users to receive natural and appropriate answers quickly, and by recognizing their emotions, they can receive more personalized and satisfying service. This improves customer satisfaction and enables a more accurate understanding of customer needs. This system will function effectively in industries such as retail, where inquiries are diverse, and will be an important tool for providing advanced customer service.

[1107] Example of a prompt:

[1108] User: "What is the warranty period for this product?"

[1109] Answer: "The warranty period is one year. If you have any concerns, please feel free to contact us."

[1110] This invention is a system that improves customer support in physical stores by combining generative artificial intelligence and emotion analysis means. The specific program processing sequence and technical details will be explained by clearly outlining how each element interacts and describing how users utilize the system.

[1111] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1112] Step 1:

[1113] User input

[1114] The user accesses the terminal and enters their inquiry into the interface. For example, the user might ask, "What is the warranty period for this product?"

[1115] Input: User inquiry

[1116] Output: Inquiry data from the terminal

[1117] Specific action: The user enters text into an input field displayed on the device screen and presses the send button.

[1118] Step 2:

[1119] Data transfer by terminal

[1120] The terminal receives the query entered by the user and prepares to send it to the server. Specifically, it uses an HTTP POST request to forward the user's query to the server.

[1121] Input: User inquiry data

[1122] Output: HTTP request sent to the server

[1123] Specific operation: The terminal converts the query data into an HTTP request format and sends it to the specified endpoint on the server.

[1124] Step 3:

[1125] Server receives and analyzes queries.

[1126] The server analyzes the inquiries received from the terminal and generates the optimal answer using generative artificial intelligence. First, it analyzes the received data and inputs it into the generative artificial intelligence model.

[1127] Input: HTTP request received from the terminal

[1128] Output: Input data for the generative AI model

[1129] Specific operation: The server extracts the query content from the HTTP request and converts it into a format that can be input into the generating AI model.

[1130] Step 4:

[1131] Emotion recognition using emotion analysis methods

[1132] Before passing the query content to the generative artificial intelligence, the server uses sentiment analysis to recognize the user's emotions. Sentiment analysis uses natural language processing techniques to identify emotions from the user's input.

[1133] Input: Inquiry details

[1134] Output: Sentiment analysis results (e.g., positive, negative, etc.)

[1135] Specific operation: Analyze the question text and calculate sentiment polarity using a sentiment recognition algorithm.

[1136] Step 5:

[1137] Answer generation using generative artificial intelligence

[1138] The server feeds back the emotion analysis results to the generative artificial intelligence system, which then generates the optimal response tailored to the user's emotions.

[1139] Input: Inquiry details, sentiment analysis results

[1140] Output: Generated answer

[1141] Specific operation: The AI ​​model receives the inquiry content and sentiment analysis results, and generates an appropriate response.

[1142] Step 6:

[1143] Server-based response submission

[1144] The server sends the generated response to the terminal.

[1145] Input: Generated answer

[1146] Output: Answer sent to the terminal

[1147] Specific action: The generated response is sent back to the terminal as an HTTP response.

[1148] Step 7:

[1149] Display of answers via device

[1150] The terminal displays the response received from the server to the user.

[1151] Input: Response received from the server

[1152] Output: Answer displayed to the user

[1153] Specific action: Display the answer text on the device screen so the user can confirm it.

[1154] The user reviews the answers displayed on the device, and if further questions are needed, they re-enter their inquiry, and the process is repeated.

[1155] Example of a prompt:

[1156] User: "What is the warranty period for this product?"

[1157] Answer: "The warranty period is one year. If you have any concerns, please feel free to contact us."

[1158] 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.

[1159] 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.

[1160] 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.

[1161] [Fourth Embodiment]

[1162] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1163] 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.

[1164] 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).

[1165] 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.

[1166] 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.

[1167] 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).

[1168] 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.

[1169] 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.

[1170] 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.

[1171] 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.

[1172] 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.

[1173] 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.

[1174] 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".

[1175] This invention is a system in which a user inputs an inquiry, and a generative artificial intelligence provides a natural-sounding response. This system consists of three main elements: the user, the terminal, and the server.

[1176] User actions

[1177] Users first access the system via a device and enter their inquiry. This device can be any internet-connected device, such as a computer, smartphone, or tablet. Inquiries cover a wide range of topics, including product details, service usage instructions, and return policy questions.

[1178] Specific example

[1179] The user enters "Please tell me about returning the product" into the terminal's interface.

[1180] Terminal operation

[1181] The terminal receives the inquiry entered by the user and sends it to the server. The terminal uses communication protocols such as HTTP requests to transfer the user's input to the server in an appropriate manner.

[1182] Specific example

[1183] After the terminal receives user input, it sends the query details to the server as a POST request.

[1184] Server operation

[1185] The server analyzes the inquiry received from the terminal and generates a response using generative artificial intelligence. The generative AI uses, for example, a large-scale language model (such as GPT-3) to generate the optimal response to the user's inquiry.

[1186] The server uses a generative artificial intelligence API to analyze the inquiry and obtain an appropriate response. In this process, the generative AI generates a natural and appropriate response based on relevant information. The generated response is then sent back from the server to the terminal.

[1187] Specific example

[1188] The server uses generative artificial intelligence to generate an appropriate response to a user's inquiry, "Please tell me about product returns," such as "Returns are accepted within 30 days of purchase, along with the receipt."

[1189] Display by device

[1190] The terminal displays the response received from the server to the user. At this stage, the user can obtain the appropriate answer.

[1191] Specific example

[1192] The device displays a message to the user stating, "Returns are accepted within 30 days of purchase, along with the receipt."

[1193] User verification

[1194] The user reviews the answers displayed on the device and decides on the next action based on that information. If the user requires further detailed questions, they enter the inquiry again, and the process is repeated.

[1195] This system allows users to quickly obtain natural and appropriate answers, improving customer satisfaction and enabling a more accurate understanding of customer needs. This system will function effectively in industries such as retail, where inquiries are diverse, and will serve as an important tool for providing high-quality customer service.

[1196] The following describes the processing flow.

[1197] Step 1:

[1198] The user enters their inquiry. The user accesses the terminal interface and enters the inquiry as text. In this case, the inquiry may be entered in the format of "Please tell me about returning a product."

[1199] Step 2:

[1200] The device receives the user's inquiry. When the user clicks the submit button, the device checks the input and retrieves the inquiry text.

[1201] Step 3:

[1202] The terminal sends the query to the server. The terminal sends the retrieved query text to the server as an HTTP POST request. This request contains the query details.

[1203] Step 4:

[1204] The server receives the query. The server receives the request sent from the terminal and extracts the query content from the request body.

[1205] Step 5:

[1206] The server analyzes the query content. The server uses natural language processing techniques to analyze the extracted query content and understand its intent. This analysis includes extracting the query context and key keywords.

[1207] Step 6:

[1208] The server generates an answer using generative artificial intelligence. The server passes the analysis results to the generative AI's API, which then generates an appropriate answer. In this process, the generative AI is designed to generate the answer that is most suitable for the input query.

[1209] Step 7:

[1210] The server retrieves the generated response. The response returned by the generative artificial intelligence is retrieved, and its content is checked. For example, the generated response might be something specific like, "Returns are accepted within 30 days of purchase, along with the receipt."

[1211] Step 8:

[1212] The server sends the response to the device. The server returns the retrieved response to the device as an HTTP response. This response contains the generated response.

[1213] Step 9:

[1214] The terminal receives the response. The terminal receives the response sent from the server and extracts the response content from the response body.

[1215] Step 10:

[1216] The device displays the answer to the user. The device displays the extracted answer content on the user interface. At this time, the answer displayed is "Returns are accepted within 30 days of purchase, along with the receipt."

[1217] Step 11:

[1218] The user confirms the answer. The user checks the answer displayed on the device and understands that it is the correct answer to their inquiry. If necessary, the user enters further detailed questions, and the same process is repeated.

[1219] (Example 1)

[1220] 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".

[1221] Traditional inquiry handling systems struggled to provide timely and appropriate answers to user inquiries. This was particularly true when inquiries covered a wide range of topics, requiring complex analysis and response generation. As a result, customer satisfaction declined, and response costs increased.

[1222] 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.

[1223] In this invention, the server includes means for analyzing a query and calling a generative artificial intelligence API, means for the generative artificial intelligence to generate an appropriate answer, and means for transmitting the generated answer to a terminal. This makes it possible to provide a quick and appropriate answer to a user's query.

[1224] A "user" refers to a person who accesses the system and enters an inquiry.

[1225] A "terminal" refers to a device used by a user to input inquiries and communicate with a server. Examples include computers, smartphones, and tablets.

[1226] A "server" refers to a device that receives user inquiries, calls a generative artificial intelligence API to generate answers, and sends those answers to the terminal.

[1227] "Inquiry" refers to the content of questions or requests that users enter through their devices.

[1228] "Generative artificial intelligence" refers to a technology that uses large-scale language models and other tools to generate appropriate answers to input inquiries.

[1229] An "API" refers to a program interface provided by a generative artificial intelligence system, which a server uses to call the functions of the generative artificial intelligence system.

[1230] "Answer" refers to the response generated by a generative artificial intelligence system based on the user's inquiry.

[1231] "Analysis" refers to the process by which a server understands the content of a query received from a user and provides appropriate information to a generative artificial intelligence system based on that understanding.

[1232] "Display" refers to the act of a device visually providing the user with the response received from the server.

[1233] This invention is a system in which a user inputs an inquiry into a terminal, and a generative artificial intelligence provides a natural-sounding response. This system consists of three main elements: the user, the terminal, and the server.

[1234] User actions

[1235] Users first access the system via a device and enter their inquiry. This device can be any internet-connected device, such as a computer, smartphone, or tablet. Inquiries cover a wide range of topics, including product details, service usage instructions, and return policy questions.

[1236] Specific example

[1237] The user enters "Please tell me about returning the product" into the terminal's interface.

[1238] Terminal operation

[1239] The terminal receives the inquiry entered by the user and sends it to the server. The terminal uses communication protocols such as HTTP requests to transfer the user's input to the server in an appropriate manner.

[1240] Specific example

[1241] After the terminal receives user input, it sends the query details to the server as a POST request.

[1242] Server operation

[1243] The server analyzes the inquiry received from the terminal and generates a response using generative artificial intelligence (AI). The generative AI uses a large-scale language model (e.g., GPT-3) to generate the optimal response to the user's inquiry. The server uses the generative AI's API to analyze the inquiry and obtain an appropriate response. In this process, the generative AI generates a natural and appropriate response based on relevant information. The generated response is then sent back from the server to the terminal.

[1244] Specific example

[1245] The server uses generative artificial intelligence to generate an appropriate response to a user's inquiry, "Please tell me about product returns," such as "Returns are accepted within 30 days of purchase, along with the receipt."

[1246] Display by device

[1247] The terminal displays the response received from the server to the user. At this stage, the user can obtain the appropriate answer.

[1248] Specific example

[1249] The device displays a message to the user stating, "Returns are accepted within 30 days of purchase, along with the receipt."

[1250] User verification

[1251] The user reviews the answers displayed on the device and decides on the next action based on that information. If the user requires further detailed questions, they enter the inquiry again, and the process is repeated.

[1252] Example of a prompt

[1253] The following are specific examples of prompt statements used to query a generative artificial intelligence.

[1254] User: "Please tell me about returning products."

[1255] Through these processes, this system provides an efficient means for users to obtain quick and natural answers. By utilizing generative artificial intelligence, it is possible to respond to inquiries quickly and appropriately.

[1256] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1257] Step 1: The user enters their inquiry.

[1258] The user opens the terminal interface and enters their inquiry. For example, the user might enter "Please tell me about returning an item." This input is then provided as data for the next processing step.

[1259] Input: The content of the inquiry entered by the user on the device (e.g., "Please tell me about returning the product").

[1260] Output: Inquiry content entered into the terminal

[1261] Specific action: The user opens a browser on their smartphone, enters "Please tell me about product returns" into the inquiry form, and presses the submit button.

[1262] Step 2: The device sends the query to the server.

[1263] The terminal sends the inquiry received from the user to the server as an HTTP POST request. This request includes the content of the user's inquiry.

[1264] Input: Inquiry content entered by the user

[1265] Output: HTTP POST request sent to the server

[1266] Specific operation: The terminal sends a user inquiry to the server asking, "Please tell me about returning the product." HTTP is used as the communication protocol for transmission.

[1267] Step 3: The server analyzes the query and calls a generative artificial intelligence API.

[1268] The server analyzes the received query and, based on its content, calls a generative artificial intelligence API. It generates an input prompt for the API and sends the query content.

[1269] Input: HTTP POST request sent to the server (inquiry content)

[1270] Output: Prompt message sent to the generative artificial intelligence API

[1271] Specific operation: The server analyzes the inquiry "Please tell me about product returns" and converts its content into a prompt message to send to a generative artificial intelligence API.

[1272] Step 4: Generative artificial intelligence generates the answer.

[1273] Generative artificial intelligence receives input prompts and generates appropriate responses. For example, it can provide details about return conditions and procedures in a natural-sounding style.

[1274] Input: Prompt sent to a generative AI API

[1275] Output: Answer generated by a generative artificial intelligence system

[1276] Specific operation: The generative artificial intelligence generates the response, "Returns are accepted within 30 days of purchase, along with the receipt."

[1277] Step 5: The server sends the generated response to the terminal.

[1278] The server sends the response received from the generative artificial intelligence back to the terminal. This return is done as an HTTP response.

[1279] Input: Answer generated by a generative artificial intelligence system

[1280] Output: HTTP response sent to the terminal

[1281] Specific action: The server sends an HTTP response to the terminal stating, "Returns are accepted within 30 days of purchase, along with the receipt."

[1282] Step 6: The device displays the answer to the user.

[1283] The terminal displays the response received from the server on the user interface. The response content is displayed in an easy-to-read format so that the user can easily check it.

[1284] Input: HTTP response received from the server (response generated by a generative AI)

[1285] Output: Answer displayed in the user interface

[1286] Specific action: The device displays a message on the screen stating, "Returns are accepted within 30 days of purchase, along with the receipt."

[1287] Step 7: The user reviews the displayed response and makes further inquiries if necessary.

[1288] The user reviews the displayed answers and, if necessary, enters further detailed questions. If the answers are satisfactory, they then decide on their next course of action.

[1289] Input: Answer displayed in the user interface

[1290] Output: User's next action (entering a more detailed inquiry or deciding on the next course of action)

[1291] Specific operation: The user reviews the displayed answer and, if not satisfied, enters an additional question such as, "Where should I take this item for a return?" This process is repeated.

[1292] (Application Example 1)

[1293] 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".

[1294] Conventional security services have limited ways for users to easily check the security status of their homes or offices. Furthermore, checking the status often required the use of numerous applications and devices, making the process cumbersome. Moreover, in situations requiring real-time status checks and rapid response, there was a possibility of delays in information acquisition and response. This invention aims to solve these problems and provide a system that allows users to easily and quickly check their security status and take appropriate action.

[1295] 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.

[1296] In this invention, the server includes means for the server to check the status in conjunction with a monitoring system, means for a generative artificial intelligence to generate a response in real time based on data from the monitoring system, and means for the server to generate a response based on an inquiry using the API of the generative artificial intelligence and reflect the status in real time by referring to data from the monitoring system. As a result, users can easily check the security status through their terminals and take appropriate action quickly.

[1297] A "user" refers to an individual or legal entity that uses the system.

[1298] A "device" refers to a computer, smartphone, tablet, or any other device that can connect to the internet and is operated by a user.

[1299] "Inquiry" refers to the content of questions or requests entered by the user via their device.

[1300] A "server" refers to a remote computing system that receives inquiries from terminals, generates answers using generative artificial intelligence, and sends those answers back to the terminals.

[1301] "Generative artificial intelligence" refers to artificial intelligence technology that analyzes user inquiries and generates appropriate answers, such as systems that use large-scale language models.

[1302] "Natural language processing technology" refers to the technology used by generative artificial intelligence to analyze natural language queries input by users, understand their meaning, and generate appropriate responses.

[1303] "API" refers to an application programming interface for using generative artificial intelligence, and is a protocol for efficiently exchanging data between a server and artificial intelligence.

[1304] "Surveillance systems" refer to a general term for internet-connected devices used for security purposes, such as security cameras, door sensors, and smart locks.

[1305] "Status check" refers to understanding the current security status based on information obtained through the monitoring system.

[1306] "Real-time" refers to providing near-instantaneous responses to user inquiries.

[1307] This invention provides a system that allows users to easily check the security status of their home or office and take quick action. This system consists of four main elements: the user, the terminal, the server, and the monitoring system. The following describes each element and its operation in detail.

[1308] 1. User actions

[1309] The user first accesses the terminal and enters a security-related inquiry. The terminal can be a computer, smartphone, tablet, or other device connected to the internet. User inquiries cover a wide range of topics, such as checking security camera footage or the status of door locks.

[1310] Specific example

[1311] The user enters "Please tell me if the front door is currently locked" into the terminal's interface.

[1312] 2. Device operation

[1313] The terminal receives the inquiry entered by the user and sends it to the server. The terminal uses communication protocols such as HTTP requests to transfer the user's input to the server in an appropriate manner.

[1314] Specific example

[1315] After the terminal receives user input, it sends the query details to the server as a POST request.

[1316] 3. Server operation

[1317] The server analyzes inquiries received from terminals and generates responses using generative artificial intelligence (AI). The AI ​​uses, for example, large-scale language models (such as GPT-3) to generate the optimal response to user inquiries. The server also works in conjunction with a monitoring system, acquiring data from the monitoring system in real time and using the AI's API to generate appropriate responses.

[1318] Specific example

[1319] The server, in response to the query "Is the front door currently locked?", checks the status of the smart lock and generates the response "The front door is currently locked."

[1320] 4. Display by device

[1321] The terminal displays the response received from the server to the user. At this stage, the user can obtain the appropriate answer.

[1322] Specific example

[1323] The device displays the response to the user: "The front door is currently locked."

[1324] 5. Program Details

[1325] The system is implemented using programming languages ​​such as Python. User inquiries are sent to the server via HTTP requests, and the server, in conjunction with monitoring systems such as smart locks and security cameras, acquires data in real time and generates answers using a generative artificial intelligence (e.g., GPT-3) API. The generated answers are sent back to the user's device and displayed.

[1326] Example of a prompt

[1327] "Please tell me if the front door is currently locked."

[1328] "Show me the latest footage from the security camera in the living room."

[1329] This system allows users to intuitively check the security status of their home or office and take quick action, which is expected to improve security levels.

[1330] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1331] Step 1:

[1332] The user enters the inquiry into the device.

[1333] The user enters security-related questions into the terminal. For example, they might enter a question like, "Please tell me if the front door is currently locked."

[1334] Input: User inquiry text (e.g., "Please tell me if the front door is currently locked.")

[1335] Output: Query content as a string

[1336] Step 2:

[1337] The device sends the query to the server.

[1338] The terminal sends the user's input query to the server as an HTTP request. The POST method is used to transfer the data.

[1339] Input: User's query text, server URL

[1340] Output: HTTP POST request sent to the server

[1341] Step 3:

[1342] The server receives the query.

[1343] The server receives queries sent from terminals and analyzes their content. It converts the query content into data frames or dictionary formats for further analysis.

[1344] Input: HTTP POST request from terminal

[1345] Output: Analyzed query content (e.g., "Check the lock status of the front door")

[1346] Step 4:

[1347] The server uses generative artificial intelligence to generate the answer.

[1348] The server generates the optimal response based on the analyzed query content, using a generative artificial intelligence (e.g., GPT-3) API. The generative AI generates a natural-sounding response using a pre-trained language model.

[1349] Input: Analyzed query content

[1350] Output: Generated response text (e.g., "The front door is currently locked.")

[1351] Step 5:

[1352] The server works in conjunction with the monitoring system to check the status.

[1353] The server interacts with monitoring systems such as smart locks and security cameras via APIs to acquire data in real time. Based on this information, generative artificial intelligence generates responses that reflect the situation.

[1354] Input: Analyzed query content, monitoring system API response

[1355] Output: Real-time data from the monitoring system

[1356] Step 6:

[1357] The server sends the generated response to the terminal.

[1358] The server sends the generated response back to the terminal as an HTTP response. During this process, the data is transferred in JSON format or other formats as needed.

[1359] Input: Generated response text

[1360] Output: HTTP response sent to the terminal

[1361] Step 7:

[1362] The device displays the answer to the user.

[1363] The terminal displays the response received from the server to the user. At this stage, the user can obtain an appropriate answer to their inquiry.

[1364] Input: Response text from the server

[1365] Output: The response will be displayed in a format viewable by the user (e.g., "The front door is currently locked").

[1366] Through these specific processing steps, users can intuitively check the security status of their home or office and take quick action.

[1367] 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.

[1368] This invention is a system that combines a user inputting an inquiry with a generative artificial intelligence system providing a natural response, along with an emotion engine that recognizes the user's emotions. This system consists of four main elements: the user, the terminal, the server, and the emotion engine.

[1369] User actions

[1370] Users first access the system via a device and enter their inquiry. This device can be any internet-connected device, such as a computer, smartphone, or tablet. Inquiries cover a wide range of topics, including product details, service usage instructions, and return policy questions.

[1371] Specific example

[1372] The user enters "Please tell me about returning the product" into the terminal's interface.

[1373] Terminal operation

[1374] The terminal receives the query entered by the user and prepares to send it to the server. The terminal uses communication protocols such as HTTP requests to transfer the user's input to the server in an appropriate manner.

[1375] Specific example

[1376] After the terminal receives user input, it sends the query details to the server as an HTTP POST request.

[1377] Server operation

[1378] The server analyzes the inquiry received from the terminal and generates a response using generative artificial intelligence. For example, the generative AI uses a large-scale language model to generate the optimal response to the user's inquiry.

[1379] When the server analyzes the query, it uses an emotion engine to recognize the user's emotions. The emotion engine uses natural language processing techniques to identify emotions from the user's input. This emotion information is used as reference information when the generative artificial intelligence generates a response.

[1380] Specific example

[1381] The server, in response to a user's inquiry, "Please tell me about product returns," uses generative artificial intelligence to generate a specific answer such as, "Returns are accepted within 30 days of purchase, along with the receipt." If the emotion engine detects the user's impatience or dissatisfaction, it adds a reassuring phrase to the answer, such as, "Please feel free to contact us."

[1382] Server-side transmission and terminal-side reception.

[1383] The server sends the generated response to the terminal. The terminal displays the response received from the server to the user.

[1384] Specific example

[1385] The server generates a response saying, "Returns are accepted within 30 days of purchase, along with the receipt. Please feel free to contact us," and sends it to the device. The device then displays this message to the user.

[1386] User verification

[1387] The user reviews the answers displayed on the device and decides on the next action based on that information. If the user requires further detailed questions, they enter the inquiry again, and the process is repeated.

[1388] This system allows users to receive natural and appropriate answers quickly, and by recognizing their emotions, they can receive more personalized and satisfying service. This improves customer satisfaction and enables a more accurate understanding of customer needs. This system will function effectively in industries such as retail, where inquiries are diverse, and will be an important tool for providing advanced customer service.

[1389] The following describes the processing flow.

[1390] Step 1:

[1391] The user enters their inquiry. The user accesses the terminal interface and enters their inquiry into the text box. For example, they might enter, "Please tell me about returning a product."

[1392] Step 2:

[1393] The terminal receives the user's inquiry. When the user clicks the submit button, the terminal retrieves the input and stores the inquiry content in a variable.

[1394] Step 3:

[1395] The device sends a query to the server. The device generates an HTTP POST request containing the query details and sends it to the specified endpoint on the server.

[1396] Step 4:

[1397] The server receives the query. The server receives the HTTP POST request sent from the terminal and extracts the query content from the request body.

[1398] Step 5:

[1399] The server analyzes the query. Before passing the query to the generative artificial intelligence, the server uses an emotion engine to analyze the user's emotions. The emotion engine uses natural language processing techniques to identify emotional states (e.g., joy, anger, sadness, etc.) within the text.

[1400] Step 6:

[1401] The server passes the query to the generative artificial intelligence based on the emotion analysis results. The server considers the emotion information obtained by the emotion engine and provides the generative artificial intelligence with appropriate context.

[1402] Step 7:

[1403] Generative artificial intelligence generates the answer. Based on the inquiry content and emotional information, the generative AI generates the most suitable answer. For example, if the emotional engine recognizes that the user's emotion is "anxiety," the generative AI will select words that provide reassurance.

[1404] Step 8:

[1405] The server retrieves the generated response. The server receives the response returned by the generative artificial intelligence and verifies its contents.

[1406] Step 9:

[1407] The server sends the response to the device. The server then sends the received response back to the device as an HTTP response. This response contains the generated response.

[1408] Step 10:

[1409] The terminal receives the response. The terminal receives the HTTP response sent from the server and extracts the response content from the response body.

[1410] Step 11:

[1411] The device displays the response to the user. The device displays the extracted response content on the user interface. For example, the displayed response might be, "Returns are accepted within 30 days of purchase, along with the receipt. Please feel free to contact us."

[1412] Step 12:

[1413] The user confirms the answer. The user checks the answer displayed on their device and confirms that it is the correct answer to their inquiry. If necessary, the user re-enters the question, and the process is repeated.

[1414] This system allows users to quickly obtain natural and appropriate responses that take emotions into account, generated by generative artificial intelligence. As a result, the user experience will improve, and customer satisfaction will increase.

[1415] (Example 2)

[1416] 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".

[1417] Traditional user inquiry systems often provide mechanical responses without considering the user's feelings. This leads to decreased user satisfaction and a problem in that they cannot adequately address users who are particularly dissatisfied or anxious. Furthermore, it is difficult to respond quickly and individually to a wide range of inquiries, making it challenging to provide efficient customer service.

[1418] 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 recognizing the user's emotions using an emotion analysis engine, means for generating a response using a generative artificial intelligence model, and means for transmitting the generated response to a terminal. This makes it possible to provide more individualized and satisfying responses that take the user's emotions into consideration. Furthermore, it enables the provision of efficient customer service by allowing for quick and accurate responses to a wide range of inquiries.

[1419] "User" refers to a typical user who uses the system to enter inquiries.

[1420] A "terminal" refers to a device (such as a computer, smartphone, or tablet) that a user uses to access the internet and enter inquiries.

[1421] "Inquiry" refers to the content of questions or requests that users enter into the system.

[1422] A "server" refers to a device or software that receives user inquiries, analyzes their content, generates appropriate responses, and sends them to the user's terminal.

[1423] A "sentiment analysis engine" refers to an engine that uses natural language processing technology to recognize the emotions expressed in a user's inquiry.

[1424] A "generative artificial intelligence model" refers to a large-scale language model designed to generate appropriate responses based on user inquiries.

[1425] "Answer" refers to the information and guidance that the server generates using a generative artificial intelligence model and presents to the user.

[1426] "Natural language processing technology" refers to the technology used to analyze input text data, understand its meaning, and generate appropriate output.

[1427] "API" is an abbreviation for Application Programming Interface, and refers to an interface for using external functions such as generative artificial intelligence models.

[1428] This invention relates to a system in which a user inputs an inquiry, and a generative artificial intelligence provides a natural response, while also recognizing the user's emotions and providing a response appropriate to those emotions. This system consists of four main elements: the user, the terminal, the server, and the emotion analysis engine.

[1429] The user first accesses a terminal and enters their inquiry. Any internet-connected device can be used as the terminal, including computers, smartphones, and tablets. Once the user enters their inquiry, the terminal prepares to send it to the server. HTTP requests are the common communication protocol. For example, if the user enters "Please tell me about product returns," the terminal sends this inquiry to the server as an HTTP POST request.

[1430] The server analyzes inquiries received from terminals and generates responses using generative artificial intelligence (AI). It is desirable to use a large-scale language model (e.g., GPT-3) for this generative AI. When the server analyzes the inquiry, it uses an emotion analysis engine to recognize the user's emotions. The emotion analysis engine identifies emotions from the user's input using natural language processing techniques. This emotion information is used as reference information when the generative AI generates responses.

[1431] As a concrete example, when a server receives a user inquiry such as "Please tell me about product returns," it uses generative artificial intelligence to generate a specific response such as "Returns are accepted within 30 days of purchase, along with the receipt." In this case, if the sentiment analysis engine detects the user's impatience or dissatisfaction, it adds a reassuring phrase to the response, such as "Please feel free to contact us."

[1432] The generated response is sent from the server to the terminal, where it is displayed to the user. The user reviews the response displayed on the terminal and decides on the next action based on that information. For example, if the user is satisfied, they proceed with the action; if further detailed questions are needed, they enter the inquiry again, and the process is repeated.

[1433] This system allows users to receive natural and appropriate answers quickly, and by recognizing their emotions, they can receive more personalized and satisfying service. This improves customer satisfaction and enables a more accurate understanding of customer needs. This system is particularly effective in industries where a wide range of inquiries occur, and it becomes an important tool for providing high-quality customer service.

[1434] Hardware and software to be used

[1435] Hardware: Computers, smartphones, tablets

[1436] Software: Web browser, HTTP communication protocol, generative artificial intelligence (GPT-3, etc.), sentiment analysis engine

[1437] Examples of prompts for generative AI models

[1438] User inquiry: "Please tell me about returning products."

[1439] Emotional analysis results: "Impatience, dissatisfaction"

[1440] Prompt: "User inquiry: Please tell me about returning the product. User's emotional state: Anxious, dissatisfied. Generate a reassuring response for the user."

[1441] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1442] Step 1:

[1443] The user accesses the terminal and enters their inquiry. When the user enters their inquiry (e.g., "Please tell me about returning a product"), data is entered into the terminal. The terminal receives this data and prepares for the next step. Specifically, the user opens a web browser and enters their inquiry into a web form. This input is in the form of text data.

[1444] Step 2:

[1445] The terminal processes user input and prepares it for transmission to the server. It converts the input string data into an HTTP POST request format. Specifically, it uses JavaScript or similar tools to capture the input data and construct the HTTP POST request. By sending the constructed request to the server, the data flows to the next step. The input is string data, and the output is an HTTP POST request.

[1446] Step 3:

[1447] The server parses the HTTP POST request received from the terminal. It extracts the inquiry content from the request body and takes it out as string data. Specifically, the server receives an HTTP request and parses the user's inquiry, "Please tell me about returning the product," from its body. Using this analysis result, it prepares a prompt for the generative artificial intelligence. The input is the HTTP POST request, and the output is the parsed string data.

[1448] Step 4:

[1449] The server sends the analyzed query to the sentiment analysis engine to recognize the user's emotions. The input string data is passed to the sentiment analysis engine, which processes it. Natural language processing techniques are used to identify the user's emotions (e.g., "anxiety, dissatisfaction"). Specifically, the query text is sent to the sentiment analysis engine, and emotion data such as "anxiety, dissatisfaction" is obtained as a result of the analysis. The input is the analyzed string data, and the output is the emotion data.

[1450] Step 5:

[1451] The server sends a prompt to a generative AI model (e.g., GPT-3) based on the inquiry content and sentiment data to generate a response. The inquiry content and sentiment information are combined to form the prompt. For example, the prompt "User inquiry: Please tell me about product returns. User sentiment state: Anxious, dissatisfied. Please generate a response that will reassure the user." is sent to the generative AI model. The generative AI model then generates a response based on this (e.g., "Returns are accepted within 30 days of purchase, along with the receipt. Please feel free to contact us."). The input is the prompt, and the output is the generated response.

[1452] Step 6:

[1453] The server sends the generated response to the terminal. It wraps the generated response as an HTTP response and sends it back to the terminal. Specifically, it sends the output of the generating AI model to the terminal in an HTTP response. Upon receiving this response, the terminal proceeds to the next step. The input is the generated response, and the output is the HTTP response.

[1454] Step 7:

[1455] The terminal displays the response received from the server to the user. It parses the HTTP response, extracts it as string data, and displays it in the browser's user interface. Specifically, the terminal receives the HTTP response and displays the response "Returns are accepted within 30 days of purchase, along with the receipt. Please feel free to contact us" in the inquiry form. The input is the HTTP response, and the output is the response displayed in the user interface.

[1456] Step 8:

[1457] The user reviews the answer displayed on their device and decides on their next action. If necessary, they re-enter their inquiry, and the process is repeated. Specifically, the user reads the displayed answer, takes action if they are satisfied, or enters a new question into the web form if further details are needed. The input is the displayed answer, and the output is the user's next action.

[1458] (Application Example 2)

[1459] 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".

[1460] Conventional inquiry systems using generative artificial intelligence have the problem of not recognizing user emotions and providing uniform answers, making it difficult to respond flexibly according to the user's emotions and circumstances. In particular, in situations where direct interaction with customers is important, such as in physical stores, it is necessary to understand the emotions of individual customers and provide appropriate support. Therefore, if a system that can recognize user emotions and provide appropriate responses is realized, it will be possible to significantly improve customer satisfaction.

[1461] 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.

[1462] In this invention, the server includes means for the user to input an inquiry into a terminal, means for the terminal to send the inquiry to the server, means for the server to receive the inquiry and generate an answer using generative artificial intelligence, means for the terminal to recognize the user's emotions through emotion analysis, means for adjusting the generative artificial intelligence's answer based on the emotion analysis, means for the server to send the generated answer to the terminal, and means for the terminal to display the answer to the user. This enables more personalized responses that respond to the user's emotions. Specifically, when a user makes an inquiry such as "Please tell me about returning a product," the emotion analysis means can be used to analyze the user's emotions and provide a reassuring answer based on the results. This is expected to improve the quality of the user experience and increase customer satisfaction in physical stores.

[1463] A "user" is an individual or organization that makes an inquiry using a terminal.

[1464] A "terminal" is an internet-connected device used to process user-inputted inquiries and communicate with a server.

[1465] A "server" is a central processing unit that receives inquiries from users, generates responses using generative artificial intelligence and sentiment analysis methods, and transmits them to terminals.

[1466] "Generative artificial intelligence" refers to an artificial intelligence system that uses natural language processing technology to analyze user inquiries and generate appropriate responses.

[1467] "Emotion analysis means" refers to analytical technology that recognizes emotions from user input and adjusts responses based on those emotions.

[1468] "Natural language processing technology" refers to technologies that enable computers to understand, interpret, and generate human language.

[1469] "Answer" refers to information or instructions provided by a generative artificial intelligence system in response to a user's inquiry.

[1470] "Adjustment" refers to the process of modifying the responses generated by generative artificial intelligence in accordance with the user's emotions.

[1471] "Inquiry" refers to the process by which a user enters information they want to know or a problem they want to solve in language.

[1472] "Display" refers to outputting the answers generated on the device in a format that allows the user to visually confirm them.

[1473] This invention is a system that combines a system in which a user inputs an inquiry and a generative artificial intelligence provides a natural response, with an emotion analysis means that recognizes the user's emotions. The system consists of four main elements: the user, the terminal, the server, and the emotion analysis means.

[1474] User actions

[1475] The user first accesses a terminal and enters their inquiry. The terminal can be any internet-connected device, such as a computer, smartphone, or tablet. Inquiries cover a wide range of topics, including product details, service usage instructions, and return policy. For example, the user might enter "What is the warranty period for this product?" into the terminal's interface.

[1476] Terminal operation

[1477] The terminal receives the user's input and prepares to send it to the server. The terminal uses a communication protocol such as an HTTP request to transfer the user's input to the server in an appropriate manner. For example, after the terminal receives the user's input, it sends the content of the inquiry to the server as an HTTP POST request.

[1478] Server operation

[1479] The server analyzes inquiries received from terminals and generates responses using generative artificial intelligence (AI). The generative AI, for example, uses a large-scale language model to generate the optimal response to the user's inquiry. When the server analyzes the inquiry, it uses sentiment analysis to recognize the user's emotions. Sentiment analysis uses natural language processing techniques to identify emotions from the user's input. This emotion information is used as reference information when the generative AI generates responses.

[1480] Specific example

[1481] The server uses generative artificial intelligence to generate a response to the user's inquiry, "What is the warranty period for this product?", stating, "The warranty period is one year." If the sentiment analysis system detects the user's impatience or dissatisfaction, it adds a reassuring phrase to the response, such as, "Please feel free to contact us if you have any concerns."

[1482] Server-side transmission and terminal-side reception.

[1483] The server sends the generated response to the terminal. The terminal displays the response received from the server to the user. For example, the server generates the response "The warranty period is one year. Please feel free to contact us if you have any concerns" and sends it to the terminal. The terminal then displays this content to the user.

[1484] User verification

[1485] The user reviews the answers displayed on the device and decides on the next action based on that information. If the user requires further detailed questions, they enter the inquiry again, and the process is repeated.

[1486] This system allows users to receive natural and appropriate answers quickly, and by recognizing their emotions, they can receive more personalized and satisfying service. This improves customer satisfaction and enables a more accurate understanding of customer needs. This system will function effectively in industries such as retail, where inquiries are diverse, and will be an important tool for providing advanced customer service.

[1487] Example of a prompt:

[1488] User: "What is the warranty period for this product?"

[1489] Answer: "The warranty period is one year. If you have any concerns, please feel free to contact us."

[1490] This invention is a system that improves customer support in physical stores by combining generative artificial intelligence and emotion analysis means. The specific program processing sequence and technical details will be explained by clearly outlining how each element interacts and describing how users utilize the system.

[1491] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1492] Step 1:

[1493] User input

[1494] The user accesses the terminal and enters their inquiry into the interface. For example, the user might ask, "What is the warranty period for this product?"

[1495] Input: User inquiry

[1496] Output: Inquiry data from the terminal

[1497] Specific action: The user enters text into an input field displayed on the device screen and presses the send button.

[1498] Step 2:

[1499] Data transfer by terminal

[1500] The terminal receives the query entered by the user and prepares to send it to the server. Specifically, it uses an HTTP POST request to forward the user's query to the server.

[1501] Input: User inquiry data

[1502] Output: HTTP request sent to the server

[1503] Specific operation: The terminal converts the query data into an HTTP request format and sends it to the specified endpoint on the server.

[1504] Step 3:

[1505] Server receives and analyzes queries.

[1506] The server analyzes the inquiries received from the terminal and generates the optimal answer using generative artificial intelligence. First, it analyzes the received data and inputs it into the generative artificial intelligence model.

[1507] Input: HTTP request received from the terminal

[1508] Output: Input data for the generative AI model

[1509] Specific operation: The server extracts the query content from the HTTP request and converts it into a format that can be input into the generating AI model.

[1510] Step 4:

[1511] Emotion recognition using emotion analysis methods

[1512] Before passing the query content to the generative artificial intelligence, the server uses sentiment analysis to recognize the user's emotions. Sentiment analysis uses natural language processing techniques to identify emotions from the user's input.

[1513] Input: Inquiry details

[1514] Output: Sentiment analysis results (e.g., positive, negative, etc.)

[1515] Specific operation: Analyze the question text and calculate sentiment polarity using a sentiment recognition algorithm.

[1516] Step 5:

[1517] Answer generation using generative artificial intelligence

[1518] The server feeds back the emotion analysis results to the generative artificial intelligence system, which then generates the optimal response tailored to the user's emotions.

[1519] Input: Inquiry details, sentiment analysis results

[1520] Output: Generated answer

[1521] Specific operation: The AI ​​model receives the inquiry content and sentiment analysis results, and generates an appropriate response.

[1522] Step 6:

[1523] Server-based response submission

[1524] The server sends the generated response to the terminal.

[1525] Input: Generated answer

[1526] Output: Answer sent to the terminal

[1527] Specific action: The generated response is sent back to the terminal as an HTTP response.

[1528] Step 7:

[1529] Display of answers via device

[1530] The terminal displays the response received from the server to the user.

[1531] Input: Response received from the server

[1532] Output: Answer displayed to the user

[1533] Specific action: Display the answer text on the device screen so the user can confirm it.

[1534] The user reviews the answers displayed on the device, and if further questions are needed, they re-enter their inquiry, and the process is repeated.

[1535] Example of a prompt:

[1536] User: "What is the warranty period for this product?"

[1537] Answer: "The warranty period is one year. If you have any concerns, please feel free to contact us."

[1538] 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.

[1539] 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.

[1540] 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.

[1541] 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.

[1542] 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.

[1543] 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.

[1544] 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.

[1545] 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.

[1546] 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."

[1547] 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.

[1548] 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.

[1549] 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.

[1550] 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.

[1551] 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.

[1552] 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.

[1553] 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.

[1554] 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.

[1555] 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.

[1556] 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.

[1557] 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.

[1558] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[1559] The following is further disclosed regarding the embodiments described above.

[1560] (Claim 1)

[1561] A means for the user to enter an inquiry into the terminal,

[1562] The means by which the terminal sends a query to the server,

[1563] A server receives an inquiry and generates an answer using a generative artificial intelligence system.

[1564] A means by which the server sends the generated response to the terminal,

[1565] The means by which the device displays the answer to the user,

[1566] A system that includes this.

[1567] (Claim 2)

[1568] The system according to claim 1, wherein a generative artificial intelligence analyzes a user's inquiry using natural language processing technology and generates an appropriate response.

[1569] (Claim 3)

[1570] The system according to claim 1, wherein the server uses a generative artificial intelligence API to generate an answer based on an inquiry.

[1571] "Example 1"

[1572] (Claim 1)

[1573] A means for the user to enter an inquiry into the terminal,

[1574] The means by which the terminal sends a query to the server,

[1575] A means by which the server analyzes the query and calls a generative artificial intelligence API,

[1576] A means by which generative artificial intelligence generates appropriate answers,

[1577] A means by which the server sends the generated response to the terminal,

[1578] The means by which the device displays the answer to the user,

[1579] A system that includes this.

[1580] (Claim 2)

[1581] The system according to claim 1, wherein a generative artificial intelligence analyzes a user's inquiry using natural language processing technology and generates an appropriate response.

[1582] (Claim 3)

[1583] The system according to claim 1, wherein the server uses a generative artificial intelligence API to generate an answer based on an inquiry.

[1584] "Application Example 1"

[1585] (Claim 1)

[1586] A means for the user to enter an inquiry into the terminal,

[1587] The means by which the terminal sends a query to the server,

[1588] A server receives an inquiry and generates an answer using a generative artificial intelligence system.

[1589] A means by which the server sends the generated response to the terminal,

[1590] The means by which the device displays the answer to the user,

[1591] A means by which the server can check the status in conjunction with a monitoring system,

[1592] A method by which generative artificial intelligence generates answers in real time based on data from a monitoring system,

[1593] A system that includes this.

[1594] (Claim 2)

[1595] The system according to claim 1, wherein a generative artificial intelligence analyzes a user's inquiry using natural language processing technology and generates an appropriate response based on data from a monitoring system.

[1596] (Claim 3)

[1597] The system according to claim 1, wherein the server generates a response based on an inquiry using a generative artificial intelligence API and reflects the situation in real time by referring to data from a monitoring system.

[1598] "Example 2 of combining an emotion engine"

[1599] (Claim 1)

[1600] A means for the user to enter an inquiry into the terminal,

[1601] A means by which the terminal sends the inquiry content to the server,

[1602] The server receives a query and uses an emotion analysis engine to recognize the user's emotions,

[1603] A means by which a server generates answers using a generative artificial intelligence model,

[1604] A means by which the server sends the generated response to the terminal,

[1605] The means by which the device displays the answer to the user,

[1606] A system that includes this.

[1607] (Claim 2)

[1608] The system according to claim 1, wherein a generative artificial intelligence analyzes a user's inquiry using natural language processing technology and generates an appropriate response.

[1609] (Claim 3)

[1610] The system according to claim 1, wherein the server uses an API of a generative artificial intelligence to generate a response based on an inquiry, and generates a response that reflects the results of sentiment analysis.

[1611] "Application example 2 when combining with an emotional engine"

[1612] (Claim 1)

[1613] A means for the user to enter an inquiry into the terminal,

[1614] The means by which the terminal sends a query to the server,

[1615] A server receives an inquiry and generates an answer using a generative artificial intelligence system.

[1616] A means by which the server sends the generated response to the terminal,

[1617] The means by which the device displays the answer to the user,

[1618] A device that recognizes the user's emotions through emotion analysis,

[1619] A means for adjusting the response of a generative artificial intelligence based on emotion analysis means,

[1620] A system that includes this.

[1621] (Claim 2)

[1622] The system according to claim 1, wherein a generative artificial intelligence analyzes a user's inquiry using natural language processing technology and generates an appropriate response.

[1623] (Claim 3)

[1624] The system according to claim 1, wherein the server generates a response based on an inquiry using a generative artificial intelligence API and adjusts it using emotion analysis means. [Explanation of Symbols]

[1625] 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 for the user to enter an inquiry into the terminal, The means by which the terminal sends a query to the server, A server receives an inquiry and generates an answer using a generative artificial intelligence system. A means by which the server sends the generated response to the terminal, The means by which the device displays the answer to the user, A system that includes this.

2. The system according to claim 1, wherein a generative artificial intelligence analyzes a user's inquiry using natural language processing technology and generates an appropriate response.

3. The system according to claim 1, wherein the server uses a generative artificial intelligence API to generate an answer based on an inquiry.

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