System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving text input by a user; means for formatting the text into a specified prompt format; means for transmitting the formatted prompt to a server; means for the server to generate a response corresponding to the input text using a generative artificial intelligence model; and means for receiving the response from the server and displaying the response to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] In recent years, with the advancement of an aging society, there has been a demand for diversifying communication methods. In particular, the high hurdles faced by the elderly when using new communication tools and the decline of existing telephone culture have become issues. Furthermore, in order for users to enjoy natural conversations, response generation utilizing advanced natural language processing technology is required. The present invention aims to solve these issues and revive and revitalize existing telephone culture by providing an interactive chat platform using a generative AI model. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by providing a system including the following means: First, a means for receiving text entered by a user is provided; Next, a means for formatting the text into a specified prompt format is provided; A means for sending the formatted prompt to a server is also provided, and the server uses a generative artificial intelligence model to generate an appropriate response corresponding to the input text; Then, a means for receiving the generated response from the server and displaying it to the user is provided; Furthermore, the generative artificial intelligence model further includes means for generating an appropriate response to the user input using natural language processing technology and for terminating the dialogue when the user enters a specific end command. This system enables all users, including the elderly, to enjoy natural and effective dialogue, and is expected to revive and revitalize existing telephone culture.

[0006] "User" means a person or entity intended to use the system.

[0007] "Text" refers to character string information entered by the user.

[0008] A "prompt form" refers to the way in which the user's input text is converted into a particular format or structure.

[0009] "Server" refers to the computer system responsible for running generative AI models and processing data.

[0010] A "generative artificial intelligence model" refers to a machine learning model that generates appropriate responses to user input.

[0011] "Natural language processing technology" refers to computational technology for understanding and generating human language.

[0012] "Response" refers to the reply generated by the generative AI model in response to user input.

[0013] An "exit command" refers to a specific instruction that a user enters to end interaction with the system. [Brief explanation of the drawings]

[0014] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

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

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

[0017] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

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

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

[0020] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0022] [First embodiment]

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

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

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

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

[0027] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0031] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

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

[0033] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

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

[0035] The interactive chat platform of the present invention is a system that converts text entered by a user into natural-sounding responses using a generative AI model and provides the responses to the user. Specific embodiments of the present invention are described in detail below.

[0036] The system involves a user inputting text through a terminal, processing it on a server, and generating a response using a generative AI model.

[0037] First, consider the case where a user types "Hello" into a terminal. The user types text into the terminal's interface, and that text is received by the terminal. The terminal formats this user input into a specified prompt format, for example, "User: Hello."

[0038] The device then sends the formatted prompt to the server, where it uses the generative AI model's API to construct an HTTP request to send the prompt, including the user's input, to the server, including the API key for authentication.

[0039] The server receives the HTTP request sent from the device and sends a prompt to the generative AI model. The generative AI model uses natural language processing techniques to generate an appropriate response to the user input. For example, the AI ​​model can generate the response, "Hello! What would you like to talk about today?"

[0040] The generated response is received by the server, which returns the response to the terminal, which again includes the response text in an HTTP response and sends it to the terminal.

[0041] The device receives the HTTP response from the server and displays the response text in the chat interface. The user can see the response "AI: Hello! What would you like to talk about today?" through the device.

[0042] The system's functionality also includes a means to terminate the dialogue when the user inputs a specific command. For example, if the user inputs "end," the device detects this command and instructs the server to end the dialogue. This causes the server to stop making requests to the generative AI model and end the dialogue.

[0043] This system will enable users to enjoy natural and effective communication even in an aging society, and is expected to revive and revitalize existing telephone culture.

[0044] The processing flow will be explained below.

[0045] Step 1:

[0046] The user enters text into the terminal interface. Specifically, the user types "Hello" into the chat window and presses the Enter key to send it.

[0047] Step 2:

[0048] The terminal receives the user's input text. The terminal formats the input text, such as "User: Hello", and converts it into a prompt format.

[0049] Step 3:

[0050] The device sends the formatted prompt to the server as an HTTP request, which includes the API key along with the prompt.

[0051] Step 4:

[0052] The server receives the HTTP request sent from the device, extracts the prompt and API key from the request, and prepares to send them to the API for the generative AI model.

[0053] Step 5:

[0054] The server sends a prompt, specifically "User: Hello," to the generative AI model, requesting it to generate a response.

[0055] Step 6:

[0056] A generative AI model generates a response based on the prompt, for example, "Hello! What would you like to talk about today?"

[0057] Step 7:

[0058] The server receives the response from the generative AI model, formats it, and prepares it to be sent back to the device as an HTTP response.

[0059] Step 8:

[0060] The server sends an HTTP response to the device, which contains the text of the generated response.

[0061] Step 9:

[0062] The terminal receives the HTTP response from the server, extracts the response text from the response, and displays it to the user.

[0063] Step 10:

[0064] The user sees a response generated through the device, for example, "AI: Hello! What would you like to talk about today?" displayed in the chat window.

[0065] Step 11:

[0066] When the user inputs a specific end command, the terminal detects the command and instructs the server to end the conversation. Specifically, when the user inputs "end," the terminal sends the instruction to the server.

[0067] Step 12:

[0068] The server ends the conversation, which stops requests to the generative AI model and ends the conversation.

[0069] Example 1

[0070] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0071] Conventional dialogue systems face the challenge of generating intuitive and natural responses to user input. Furthermore, if proper authentication and formatting are not performed during the response generation process, the system may become unstable. Furthermore, if the user wishes to end the dialogue, the process can be cumbersome. To address these issues, a system is needed that can generate natural responses based on user input and process data efficiently and safely.

[0072] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0073] In this invention, the server includes: means for receiving text entered by a user; means for formatting the text into a specified prompt format; means for transmitting the formatted prompt to the server; means for the server to generate a response corresponding to the input text using a generative artificial intelligence model; means for receiving the response from the server and displaying it to the user; means for terminating the dialogue when the user enters a specific end command; and means for constructing an HTTP request including the formatted prompt and including an API key for authentication. This allows users to have an intuitive and natural dialogue, process data safely after appropriate authentication, and easily terminate the dialogue.

[0074] "User" refers to an individual or entity that interacts with the system.

[0075] "Input text" refers to character string data that a user sends to the dialogue system through a terminal.

[0076] A "specified prompt format" refers to the rules and formatting that are used to format text entered by a user into a particular format.

[0077] "Formatting means" refers to the hardware or software functionality for converting user-entered text into a specified prompt format.

[0078] A "server" refers to a computer system that performs a series of processes to process user requests and return generated responses.

[0079] "Generative artificial intelligence models" refer to algorithms or systems that use machine learning techniques to generate natural-looking responses.

[0080] "Response" refers to the dialogue content generated by the generative artificial intelligence model in response to a user's input.

[0081] The "means for displaying" refers to an interface or function for displaying the generated response on the terminal so that the user can check it.

[0082] An "exit command" refers to a specific string or signal that a user inputs into the system to end a dialogue.

[0083] An "HTTP request" refers to the message format of a data request sent from a client (terminal) to a server.

[0084] An "API key" is a unique string of characters used for authentication and access control when using a specific application program interface (API).

[0085] The present invention relates to an interactive chat platform that converts text entered by a user into natural-sounding responses using a generative AI model and provides the responses to the user. The present invention is implemented using specific hardware and software.

[0086] This system consists of the following main components: a terminal, a server, and a generative AI model. The role of each component is explained in detail below.

[0087] Hardware and Software Examples

[0088] Hardware: Devices used include smartphones, tablets, and PCs. Servers include cloud servers and on-premise servers.

[0089] Software: The generative AI model uses a model based on common natural language processing techniques (e.g., GPT-3), and also includes an API interface, HTTP protocol, and authentication functions.

[0090] A user first enters text into the terminal's interface. This text is received by the terminal. For example, if a user enters "Hello," the terminal formats this input into a specific prompt format, specifically, "User: Hello."

[0091] The device then sends the formatted prompt to the server, which in the process constructs an HTTP request and uses the API key for authentication. The device sends this request to the server, which then parses it.

[0092] The server sends the received prompt to the generative AI model, which generates an appropriate response to the user's input. In this case, the generative AI model uses natural language processing technology to generate a response such as, "Hello! What would you like to talk about today?"

[0093] The generated response is received by the server, which then sends it back to the device as an HTTP response. The device receives the response and displays the response text in the chat interface. The user can see the response "AI: Hello! What would you like to talk about today?" through the device.

[0094] Furthermore, if the user wants to end the dialogue, they can enter a specific end command (e.g., "Exit"). The device detects this command and instructs the server to end the dialogue. The server then stops making requests to the generative AI model and ends the dialogue.

[0095] Specific examples

[0096] Example prompt: "User: Hello."

[0097] This system allows users to enjoy intuitive and natural conversations, making effective communication possible even in an aging society.

[0098] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0099] Step 1:

[0100] The user enters text.

[0101] The user enters text into the device's interface to initiate a conversation, for example, "Hello." This text is sent to the device as is.

[0102] Input: The text entered by the user (e.g. "Hello")

[0103] Output: Text sent to the terminal

[0104] Step 2:

[0105] The terminal formats the text.

[0106] The device formats the received text into the specified prompt format, for example, "User: Hello" in order to provide input to the generative AI model in the appropriate format.

[0107] Input: The text entered by the user (e.g. "Hello")

[0108] Output: A formatted prompt (e.g. "User: Hello")

[0109] Step 3:

[0110] The terminal sends a prompt to the server.

[0111] The device composes the formatted prompt as an HTTP request, attaches an API key for authentication, and sends it to the server.

[0112] Input: A formatted prompt (e.g. "User: Hello"), API key

[0113] Output: HTTP request sent to server

[0114] Step 4:

[0115] The server sends a request to the generative AI model.

[0116] The server sends the prompt, possibly with additional configuration parameters, to the generative AI model, which receives the request and generates a natural-sounding response to the user's input.

[0117] Input: A formatted prompt (e.g. "User: Hello")

[0118] Output: The generated response (e.g., "Hello! What would you like to talk about today?")

[0119] Step 5:

[0120] The server receives the response from the generative AI model and sends it back to the device.

[0121] The server receives the generated response, composes it as an HTTP response, and sends it to the terminal.

[0122] Input: The generated response (e.g., "Hello! What would you like to talk about today?")

[0123] Output: The HTTP response is sent to the device

[0124] Step 6:

[0125] The terminal displays the response.

[0126] The terminal displays the response received from the server in the chat interface, allowing the user to check the displayed response.

[0127] Input: HTTP response from the server (e.g., "Hello! What would you like to talk about today?")

[0128] Output: The response displayed in the chat interface

[0129] Step 7:

[0130] The user ends the interaction.

[0131] When the user enters a specific command, such as "end," the device detects it and instructs the server to end the dialogue. The server then stops making requests to the generative AI model and ends the dialogue.

[0132] Input: A specific command entered by the user (e.g., "quit")

[0133] Output: Server ends conversation

[0134] (Application example 1)

[0135] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0136] In today's online shopping environment, it is difficult for users to find the products they want from the vast amount of information available. Furthermore, there are not enough support tools available for efficient shopping via mobile devices such as smartphones and head-mounted displays. This problem needs to be solved.

[0137] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0138] In this invention, the server includes means for receiving text entered by a user, means for formatting the text into a specified prompt format, means for transmitting the formatted prompt to the server, means for the server to generate a response corresponding to the input text using a generative artificial intelligence model, means for receiving the response from the server and displaying it to the user, and means for responding to questions from the user and supporting product recommendations and purchasing procedures, thereby enabling the user to search for products and complete purchasing procedures quickly and effectively through natural dialogue.

[0139] A "user" is someone who uses the system to input text and receive support.

[0140] "Text" refers to character information that a user inputs through a terminal.

[0141] "Prompt formatting" is a method for formatting user-entered text into a format suitable for a generative AI model.

[0142] The "server" is a central processing unit that processes the text entered by the user and generates a response using a generative AI model.

[0143] A "generative AI model" is an algorithm that uses natural language processing techniques to generate appropriate responses to user input.

[0144] A "response" is a response generated by a generative AI model based on user input.

[0145] A "display" is a method for visually indicating the server-generated response on the user's terminal.

[0146] "Means of responding to questions and supporting product recommendations and purchasing procedures" refers to a function in which a generative AI model recommends appropriate products in response to questions entered by the user and assists in the purchasing process.

[0147] An "exit command" is a specific keyword or phrase that indicates the user's intention to end the interaction.

[0148] This invention is a system that uses text entered by users in natural language and utilizes a generative AI model to recommend products and assist with purchasing procedures in online shopping.

[0149] System Program Overview

[0150] The system consists of the following components:

[0151] 1. User device: A device such as a smartphone or head-mounted display that allows the user to input text and receive responses.

[0152] 2. Server: The central processing unit that runs the generative AI model and processes user requests.

[0153] 3. Generative AI models: Algorithms that use natural language processing techniques to generate appropriate responses to user input (e.g., GPT-3 and GPT-4).

[0154] System operation procedure

[0155] 1. Text input:

[0156] The user types a question through the device's interface, for example, "What are the most popular smartphones?"

[0157] 2. Prompt formatting:

[0158] The device converts the text entered by the user into a specific prompt format that is easy for the generative AI model to understand, for example, "User: What are the most popular smartphones?"

[0159] 3. Send to server:

[0160] The formatted prompt is sent from the terminal to the server.

[0161] 4. Response Generation:

[0162] The server sends prompts and generates responses through the API of the generative AI model, which creates appropriate responses based on its extensive database.

[0163] 5. Receiving and displaying responses:

[0164] The server receives the generated response and sends it back to the terminal, which displays it to the user.

[0165] Specific processing flow

[0166] Consider a case where a user types, "What are the popular smartphones?" This text is formatted by the device into the format "User: What are the popular smartphones?" and sent to the server. The server uses a generative AI model to generate a response such as "Currently, the iPhone and Galaxy S series are very popular," and sends it back to the device. This response is displayed on the user's device, allowing the user to obtain appropriate product information.

[0167] The system enables users to quickly and effectively search for and purchase products through natural interactions. The hardware used is a smartphone and a head-mounted display, and the software uses generative AI model APIs (e.g., OpenAI's GPT-3 and GPT-4).

[0168] Example prompt sentence:

[0169] "User: What's the most popular smartphone?"

[0170] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0171] Step 1:

[0172] The user inputs text through the device interface. The specific text input is the question, "What are the most popular smartphones?" This input text is the first target for processing by the system.

[0173] Step 2:

[0174] The device formats the input text into a prompt that is easy for the generative AI model to understand. Specifically, the user's input, "What are the popular smartphones?", is converted to the format "User: What are the popular smartphones?". This formatted prompt is sent to the next step.

[0175] Step 3:

[0176] The formatted prompt is sent from the terminal to the server, where it is securely transmitted to the server using the HTTPS protocol, including the user session information along with the prompt.

[0177] Step 4:

[0178] The server sends the received prompt to the generative AI model. This involves constructing an API request and sending an HTTP POST request to the generative AI model's endpoint. The specific input sent to the API is "User: What are the popular smartphones?"

[0179] Step 5:

[0180] The generative AI model generates an appropriate response based on the input prompt. Here, natural language processing techniques are used to generate a text response such as, "The iPhone and Galaxy S series are very popular right now." After this response text is generated, it is returned to the server as an API response.

[0181] Step 6:

[0182] The server receives the response from the generative AI model and sends it to the user's device. Specifically, it sends the response text received as an HTTP response to the user's device. At this time, the generated response text "iPhone and Galaxy S series are currently very popular" is included as the response.

[0183] Step 7:

[0184] The device receives the response from the server and displays the response text to the user. Specifically, the text "iPhone and Galaxy S series are currently very popular" is displayed on the user's screen. This allows the user to obtain the information they were looking for.

[0185] Through the above steps, users can quickly and effectively obtain product information through natural dialogue, and enjoy a pleasant online shopping experience.

[0186] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0187] The interactive chat platform of the present invention is a system that converts text entered by a user into natural responses using a generative AI model and further combines it with an emotion engine to provide responses that take the user's emotions into consideration. Specific embodiments of the present invention are described in detail below.

[0188] The system involves a user inputting text via a terminal, processing it on a server, and generating a response using a generative AI model and an emotion engine.

[0189] First, consider the case where a user types "I'm very happy today" into a terminal. The user types text into their terminal's interface, and that text is received by the terminal. The terminal formats this user input into a specified prompt format, for example, "User: I'm very happy today."

[0190] The device then sends the formatted prompt to the server as an HTTP request, which includes the API key along with the prompt.

[0191] The server receives the HTTP request sent from the device, extracts the prompt and API key from the request, and sends the input text to the emotion engine to analyze the user's emotions.

[0192] The emotion engine recognizes emotions from the user's input text, and the emotion "joy" is identified. The analysis results are returned to the server.

[0193] The server sends the emotion engine's analysis results along with prompts to the generative AI model, which generates an appropriate response that takes emotion into account. The generative AI model uses natural language processing techniques to generate a response based on the user input and the recognized emotion. For example, the response generated might be, "That's great! Did something good happen?"

[0194] The generated response is received by the server, which returns the response to the terminal, which again includes the response text in an HTTP response and sends it to the terminal.

[0195] The device receives the HTTP response from the server and displays the response text in the chat interface. The user can see the response "AI: That's great! Did something good happen?" through the device.

[0196] The system's functionality also includes a means to terminate the dialogue when the user inputs a specific command. For example, if the user inputs "end," the device detects this command and instructs the server to end the dialogue. This causes the server to stop making requests to the generative AI model, and the dialogue ends.

[0197] This system will enable users to enjoy natural and effective communication even in an aging society, and is expected to revive and revitalize existing telephone culture. Furthermore, by combining it with an emotion engine, it will be possible to have more human-like conversations that take into account the user's emotions.

[0198] The processing flow will be explained below.

[0199] Step 1:

[0200] The user inputs text into the terminal interface. Specifically, for example, the user types "I'm very happy today" into the chat window and presses the enter key to send it.

[0201] Step 2:

[0202] The terminal receives the user's input text. The terminal formats the input text, such as "User: I'm very happy today," and converts it into a prompt format.

[0203] Step 3:

[0204] The device sends the formatted prompt to the server as an HTTP request, which includes the API key along with the prompt.

[0205] Step 4:

[0206] The server receives the HTTP request sent from the device and extracts the prompt and API key from the request.

[0207] Step 5:

[0208] The server sends prompts to the emotion engine, which analyzes the emotion from the user's input text and evaluates the emotional state.

[0209] Step 6:

[0210] The emotion engine recognizes emotions from the user's input text. Specifically, it identifies the emotion "joy" from the text "I'm so happy today."

[0211] Step 7:

[0212] The emotion engine returns the analysis results to the server, which receives the analysis results from the emotion engine and uses them for further processing.

[0213] Step 8:

[0214] The server sends the emotion engine's analysis results along with a prompt to the generative AI model, which generates a response based on the prompt and emotion information.

[0215] Step 9:

[0216] A generative AI model generates a response based on the prompt and emotional information, such as "That's great! Did something good happen?"

[0217] Step 10:

[0218] The server receives the response from the generative AI model, formats it, and prepares it to be sent back to the device as an HTTP response.

[0219] Step 11:

[0220] The server sends an HTTP response to the device, which contains the text of the generated response.

[0221] Step 12:

[0222] The device receives the HTTP response from the server, extracts the response text from the response, and displays it in the chat interface.

[0223] Step 13:

[0224] The user sees the response generated through the device, for example, "AI: That's great! What's going on?" displayed in the chat window.

[0225] Step 14:

[0226] When the user inputs a specific end command, the terminal detects the command and instructs the server to end the conversation. Specifically, when the user inputs "end," the terminal sends the instruction to the server.

[0227] Step 15:

[0228] The server ends the conversation, which stops requests to the generative AI model and ends the conversation.

[0229] Example 2

[0230] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0231] Conventional chat systems simply generate responses without considering the user's emotions, which often results in unnatural and robotic interactions. Furthermore, the end of a conversation is not clearly managed, which can lead to confusion when a user wants to end the conversation. To address these issues, a system is needed that generates natural responses that take emotions into account and clearly manages the end of a conversation.

[0232] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0233] In this invention, the server includes means for performing emotion analysis, means for taking the user's emotion into account in the generated response, and means for terminating the dialogue when the user inputs a specific end command, thereby providing a natural dialogue that takes the user's emotion into account and enabling a clear and smooth end to the dialogue.

[0234] "User-entered text" is a string of characters that a user enters into the chat interface using a terminal.

[0235] A "specified prompt format" is text entered by a user that has been converted into a specific format that is easy for a generative AI model to analyze.

[0236] A "formatted prompt" is a user-entered text that has been converted into a specified prompt format.

[0237] The "server" is the core computer system that uses generative AI models to generate responses to user input text and also performs sentiment analysis.

[0238] A "generative artificial intelligence model" is an algorithm or software that uses natural language processing techniques to generate appropriate responses based on a user's input text.

[0239] "Sentiment analysis" is the process of identifying emotions from user-entered text and analyzing those emotions.

[0240] "Display to user" means sending the generated response from the server to the terminal and making it visible to the user in the chat interface.

[0241] A "specific exit command" is a predefined string or keyword that the user enters to end the dialogue.

[0242] The "means for terminating the dialogue" is a mechanism by which the server stops requests to the generative AI model and ends the dialogue when it receives a specific termination command.

[0243] This invention relates to an interactive chat platform that converts text entered by a user into natural-sounding responses using a generative AI model and then combines it with an emotion analysis engine to provide responses that take the user's emotions into account. The following describes how to specifically implement the invention.

[0244] In this system, users input text via a terminal, which is then processed by the server, and a response is generated using a generative AI model and a sentiment analysis engine. Below, we will explain the specific equipment and software used.

[0245] Hardware and software:

[0246] Device: A device that allows a user to input text, such as a smartphone, tablet, or computer.

[0247] Server: A high-performance computing system that runs the generative AI model and sentiment analysis engine.

[0248] Generative AI models: AI platforms that use natural language processing techniques (e.g., open-source GPT-based models).

[0249] Sentiment analysis engine: Software for analyzing emotions from text (e.g., emotion recognition API).

[0250] Process flow:

[0251] 1. Receiving and formatting user input:

[0252] The user enters text into the device's chat interface. For example, they enter "I'm so happy today." This input text is formatted by the device to read "User: I'm so happy today."

[0253] 2. Sending the prompt to the server:

[0254] The formatted prompt is then sent to the server as an HTTP request, which includes the API key along with the prompt.

[0255] 3. Emotion analysis:

[0256] The server receives the HTTP request, extracts the prompt and API key, and then sends the prompt to a sentiment analysis engine to analyze the user's sentiment. The sentiment analysis engine identifies the sentiment "joy" from the text "I'm so happy today" and returns the result to the server.

[0257] 4. Generate response:

[0258] The server sends the emotion analysis results to the generative AI model, which generates an appropriate response that takes the emotion into account. The generative AI model generates a response such as "That's great! Did something good happen?" and returns the result to the server.

[0259] 5. Send and display the response:

[0260] The server sends the generated response as an HTTP response to the device. The device receives the response and displays "AI: That's great! What's going on?" in the chat interface.

[0261] 6. End the conversation (optional):

[0262] If the user wants to end the conversation, they input "end." The device notifies the server of this end command, and the server stops making requests to the generative AI model and ends the conversation.

[0263] Examples:

[0264] User input: "I'm very happy today."

[0265] Formatted prompt: "User: I'm very happy today."

[0266] Generated response: "That's great! Did something good happen?"

[0267] This system allows users to enjoy more natural and effective communication that takes emotions into account. Furthermore, even in an aging society, this system will enable conversations that are sensitive to the user's emotions, and is expected to revitalize existing means of communication.

[0268] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0269] Step 1:

[0270] Receiving User Input

[0271] The user enters text into the chat interface of the terminal.

[0272] Input: User types "I'm so happy today"

[0273] Action: The terminal receives the text entered by the user.

[0274] Output: The device receives the text "I'm so happy today."

[0275] Step 2:

[0276] Text formatting

[0277] The terminal formats the text entered by the user into the specified prompt format.

[0278] Input: User text input: "I'm so happy today"

[0279] What it does: The terminal converts the input text to the format "User: I'm so happy today."

[0280] Output: A formatted prompt: "User: I'm very happy today."

[0281] Step 3:

[0282] Sending an HTTP request

[0283] The formatted prompt is sent as an HTTP request to the server.

[0284] Input: The formatted prompt "User: I'm so happy today" and your API key

[0285] What it does: The device sends an HTTP request to the server, including a formatted prompt.

[0286] Output: HTTP request sent to server

[0287] Step 4:

[0288] Receiving and processing requests

[0289] The server receives the HTTP request and extracts the prompt and API key.

[0290] Input: HTTP request sent from the terminal

[0291] What happens: The server extracts the prompt and API key from the request body.

[0292] Output: The prompt "User: I'm so happy today" and the corresponding API key

[0293] Step 5:

[0294] Emotion analysis

[0295] The server sends the prompt to a sentiment analysis engine to analyze the user's sentiment.

[0296] Input: prompt "User: I'm so happy today"

[0297] Action: The server sends a prompt to the sentiment analysis engine to perform sentiment analysis.

[0298] Output: The sentiment analysis engine returns the result "joy."

[0299] Step 6:

[0300] Generating a response

[0301] The server sends the emotion analysis results to the generative AI model, which generates an appropriate response.

[0302] Input: prompt "User: I'm very happy today" and parsed result "joy"

[0303] How it works: The server sends prompts and sentiment analysis results to the generative AI model, which then generates a response.

[0304] Output: The generative AI model generates the response "That's great! What good happened?"

[0305] Step 7:

[0306] Sending a Response

[0307] The server sends the generated response to the terminal as an HTTP response.

[0308] Input: Generated response "That's great! Did something good happen?"

[0309] Action: The server sends this response to the device in the form of an HTTP response.

[0310] Output: The HTTP response is sent to the device

[0311] Step 8:

[0312] Viewing the response

[0313] The device receives the HTTP response from the server and displays it in the chat interface.

[0314] Input: HTTP response from the server: "That's great! What's the big deal?"

[0315] Behavior: The device displays the response in the chat interface.

[0316] Output: "AI: That's great! Did something good happen?"

[0317] Step 9:

[0318] Ending the conversation (optional)

[0319] If the user wishes to end the dialogue, he / she enters "Exit".

[0320] Input: User types "Exit"

[0321] Action: The device notifies the server of this termination command.

[0322] Output: The server stops making requests to the generative AI model and ends the conversation.

[0323] (Application example 2)

[0324] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0325] Conventional dialogue systems simply generate responses to user input, but are unable to provide responses that take the user's emotions into account. Furthermore, if the dialogue between customers and store clerks in a brick-and-mortar store is mechanical, customer satisfaction may decline. To solve these problems, a system is needed that can generate responses that take emotions into account while still allowing for natural dialogue.

[0326] The specification process by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving text entered by a user, means for formatting the text into a specified prompt format, means for transmitting the formatted prompt to the server, means for the server to generate a response corresponding to the input text using a generative artificial intelligence model, means for receiving the response from the server and displaying it to the user, emotion analysis means for recognizing the user's emotion, and means for generating a response based on the recognized emotion. This enables natural dialogue that takes the user's emotion into consideration.

[0327] The "means for receiving text input by the user" is an interface for obtaining text data input by the user through the terminal.

[0328] A "means for formatting into a specified prompt format" is a program that converts text entered by a user into a specific format and formats it as a prompt to be passed to a generative AI model.

[0329] The "means for transmitting the formatted prompt to the server" is a function for transferring the formatted prompt to the server through a communication network.

[0330] "Means for the server to use a generative AI model to generate a response corresponding to the input text" refers to a process in which the server uses a generative AI model to automatically generate a response based on the user's input text.

[0331] The "means for receiving the response from the server and displaying it to the user" is an interface for obtaining the response sent from the server and displaying the content of the response on the user's terminal.

[0332] The "emotion analysis means for recognizing user emotions" is an algorithm for analyzing emotions from text entered by the user and identifying those emotions.

[0333] The "means for generating a response based on the recognized emotion" is a program for generating a response that takes into account the emotion recognized by the emotion analysis means.

[0334] "Natural language processing technology" is a general term for technologies that allow computers to understand, analyze, and generate human language.

[0335] The "means for terminating the dialogue" is a function for terminating the dialogue session when the user inputs a specific end command.

[0336] This invention applies an interactive chat platform that provides emotion-aware responses to customer service support in brick-and-mortar stores. This system processes text entered by users via smartphones or tablets using a generative AI model and emotion engine to generate natural, emotion-aware responses.

[0337] The overall system configuration is as follows: a device such as a smartphone or tablet on which the user inputs text, a program that formats the text into a specific prompt format, a server that receives the formatted prompt and generates an appropriate response using a generative AI model and an emotion engine, and a program that displays the generated response on the user's device.

[0338] First, the user inputs text via a smartphone or tablet. For example, the user inputs a message such as "I'm not feeling so good today." This text is then formatted by the device's program into a specified prompt format, such as "User: I'm not feeling so good today."

[0339] The formatted prompt is sent to the server as an HTTP request, along with the required API key. The server receives the HTTP request and first sends the input text to an emotion engine to analyze the user's emotions. The emotion engine recognizes emotions from the text and identifies the emotion "sad," for example. The analysis results are returned to the server, which then sends them along with the prompt to a generative AI model to generate an appropriate response that takes the emotions into account.

[0340] The generative AI model uses natural language processing (NLP) technology to generate a response based on the user input and the recognized emotion. For example, a response such as "That's unfortunate. What's wrong?" is generated. This response is received by the server and sent to the device as an HTTP response. The device receives this HTTP response and displays the response in the chat interface. The user can see the response: "AI: That's unfortunate. What's wrong?"

[0341] As a concrete example, consider a scenario in which a store clerk is using a smartphone to interact with a customer. If the customer types, "I'm not feeling very good today," the clerk's smartphone sends this input to the server, and the emotion engine recognizes it as "sad." The generative AI model then generates a response, "That's unfortunate. What's wrong?", which is displayed on the clerk's smartphone, enabling more sympathetic customer service.

[0342] The hardware and software used include smartphones, tablets, servers, generative AI models (e.g., OpenAI's GPT-3), emotion engines (e.g., Emotion API), and HTTP communication libraries (e.g., Python requests).

[0343] An example prompt is:

[0344] User: I'm not feeling too great today

[0345] AI: That's unfortunate. What happened?

[0346] In this way, interactions with customers in physical stores become more natural and emotionally sensitive, which is expected to improve customer satisfaction.

[0347] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0348] Step 1:

[0349] A user inputs text via a smartphone or tablet, for example, "I'm not feeling very good today." This input text is sent to the device.

[0350] Step 2:

[0351] The terminal formats the received text into the specified prompt format, e.g., "User: I'm not feeling too great today." This formatted prompt is temporarily stored in the terminal.

[0352] Step 3:

[0353] The formatted prompt is sent from the terminal to the server. Specifically, it is sent as an HTTP request, and the request includes the prompt text and the API key. This request reaches the server.

[0354] Step 4:

[0355] The server receives the HTTP request sent from the device, extracts the prompt and API key from the request, and sends the extracted text to the emotion engine for sentiment analysis.

[0356] Step 5:

[0357] The emotion engine analyzes the received input text and recognizes the user's emotion. For example, the emotion "sad" is recognized. The emotion analysis result is returned to the server from the emotion engine.

[0358] Step 6:

[0359] The server sends the emotion analysis results and prompts returned by the emotion engine to the generative AI model, which uses natural language processing techniques to generate an appropriate response that takes emotion into account. For example, the generated response might be, "That's unfortunate. What's wrong?"

[0360] Step 7:

[0361] The server receives the generated response and sends it to the terminal as an HTTP response, which includes the generated response text.

[0362] Step 8:

[0363] The device receives the HTTP response from the server and displays the response text in the chat interface. For example, the response "AI: That's unfortunate. What's wrong?" is displayed on the user's device.

[0364] The above is the specific processing flow of the system for realizing an emotion-aware interactive chat platform in a physical store.

[0365] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0366] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0367] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0368] [Second embodiment]

[0369] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0370] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0371] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0372] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0373] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0374] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0375] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0376] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0377] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0378] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0379] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0380] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0381] The interactive chat platform of the present invention is a system that converts text entered by a user into natural-sounding responses using a generative AI model and provides the responses to the user. Specific embodiments of the present invention are described in detail below.

[0382] The system involves a user inputting text through a terminal, processing it on a server, and generating a response using a generative AI model.

[0383] First, consider the case where a user types "Hello" into a terminal. The user types text into the terminal's interface, and that text is received by the terminal. The terminal formats this user input into a specified prompt format, for example, "User: Hello."

[0384] The device then sends the formatted prompt to the server, where it uses the generative AI model's API to construct an HTTP request to send the prompt, including the user's input, to the server, including the API key for authentication.

[0385] The server receives the HTTP request sent from the device and sends a prompt to the generative AI model. The generative AI model uses natural language processing techniques to generate an appropriate response to the user input. For example, the AI ​​model can generate the response, "Hello! What would you like to talk about today?"

[0386] The generated response is received by the server, which returns the response to the terminal, which again includes the response text in an HTTP response and sends it to the terminal.

[0387] The device receives the HTTP response from the server and displays the response text in the chat interface. The user can see the response "AI: Hello! What would you like to talk about today?" through the device.

[0388] The system's functionality also includes a means to terminate the dialogue when the user inputs a specific command. For example, if the user inputs "end," the device detects this command and instructs the server to end the dialogue. This causes the server to stop making requests to the generative AI model and end the dialogue.

[0389] This system will enable users to enjoy natural and effective communication even in an aging society, and is expected to revive and revitalize existing telephone culture.

[0390] The processing flow will be explained below.

[0391] Step 1:

[0392] The user enters text into the terminal interface. Specifically, the user types "Hello" into the chat window and presses the Enter key to send it.

[0393] Step 2:

[0394] The terminal receives the user's input text. The terminal formats the input text, such as "User: Hello", and converts it into a prompt format.

[0395] Step 3:

[0396] The device sends the formatted prompt to the server as an HTTP request, which includes the API key along with the prompt.

[0397] Step 4:

[0398] The server receives the HTTP request sent from the device, extracts the prompt and API key from the request, and prepares to send them to the API for the generative AI model.

[0399] Step 5:

[0400] The server sends a prompt, specifically "User: Hello," to the generative AI model, requesting it to generate a response.

[0401] Step 6:

[0402] A generative AI model generates a response based on the prompt, for example, "Hello! What would you like to talk about today?"

[0403] Step 7:

[0404] The server receives the response from the generative AI model, formats it, and prepares it to be sent back to the device as an HTTP response.

[0405] Step 8:

[0406] The server sends an HTTP response to the device, which contains the text of the generated response.

[0407] Step 9:

[0408] The terminal receives the HTTP response from the server, extracts the response text from the response, and displays it to the user.

[0409] Step 10:

[0410] The user sees a response generated through the device, for example, "AI: Hello! What would you like to talk about today?" displayed in the chat window.

[0411] Step 11:

[0412] When the user inputs a specific end command, the terminal detects the command and instructs the server to end the conversation. Specifically, when the user inputs "end," the terminal sends the instruction to the server.

[0413] Step 12:

[0414] The server ends the conversation, which stops requests to the generative AI model and ends the conversation.

[0415] Example 1

[0416] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0417] Conventional dialogue systems face the challenge of generating intuitive and natural responses to user input. Furthermore, if proper authentication and formatting are not performed during the response generation process, the system may become unstable. Furthermore, if the user wishes to end the dialogue, the process can be cumbersome. To address these issues, a system is needed that can generate natural responses based on user input and process data efficiently and safely.

[0418] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0419] In this invention, the server includes: means for receiving text entered by a user; means for formatting the text into a specified prompt format; means for transmitting the formatted prompt to the server; means for the server to generate a response corresponding to the input text using a generative artificial intelligence model; means for receiving the response from the server and displaying it to the user; means for terminating the dialogue when the user enters a specific end command; and means for constructing an HTTP request including the formatted prompt and including an API key for authentication. This allows users to have an intuitive and natural dialogue, process data safely after appropriate authentication, and easily terminate the dialogue.

[0420] "User" refers to an individual or entity that interacts with the system.

[0421] "Input text" refers to character string data that a user sends to the dialogue system through a terminal.

[0422] A "specified prompt format" refers to the rules and formatting that are used to format text entered by a user into a particular format.

[0423] "Formatting means" refers to the hardware or software functionality for converting user-entered text into a specified prompt format.

[0424] A "server" refers to a computer system that performs a series of processes to process user requests and return generated responses.

[0425] "Generative artificial intelligence models" refer to algorithms or systems that use machine learning techniques to generate natural-looking responses.

[0426] "Response" refers to the dialogue content generated by the generative artificial intelligence model in response to a user's input.

[0427] The "means for displaying" refers to an interface or function for displaying the generated response on the terminal so that the user can check it.

[0428] An "exit command" refers to a specific string or signal that a user inputs into the system to end a dialogue.

[0429] An "HTTP request" refers to the message format of a data request sent from a client (terminal) to a server.

[0430] An "API key" is a unique string of characters used for authentication and access control when using a specific application program interface (API).

[0431] The present invention relates to an interactive chat platform that converts text entered by a user into natural-sounding responses using a generative AI model and provides the responses to the user. The present invention is implemented using specific hardware and software.

[0432] This system consists of the following main components: a terminal, a server, and a generative AI model. The role of each component is explained in detail below.

[0433] Hardware and Software Examples

[0434] Hardware: Devices used include smartphones, tablets, and PCs. Servers include cloud servers and on-premise servers.

[0435] Software: The generative AI model uses a model based on common natural language processing techniques (e.g., GPT-3), and also includes an API interface, HTTP protocol, and authentication functions.

[0436] A user first enters text into the terminal's interface. This text is received by the terminal. For example, if a user enters "Hello," the terminal formats this input into a specific prompt format, specifically, "User: Hello."

[0437] The device then sends the formatted prompt to the server, which in the process constructs an HTTP request and uses the API key for authentication. The device sends this request to the server, which then parses it.

[0438] The server sends the received prompt to the generative AI model, which generates an appropriate response to the user's input. In this case, the generative AI model uses natural language processing technology to generate a response such as, "Hello! What would you like to talk about today?"

[0439] The generated response is received by the server, which then sends it back to the device as an HTTP response. The device receives the response and displays the response text in the chat interface. The user can see the response "AI: Hello! What would you like to talk about today?" through the device.

[0440] Furthermore, if the user wants to end the dialogue, they can enter a specific end command (e.g., "Exit"). The device detects this command and instructs the server to end the dialogue. The server then stops making requests to the generative AI model and ends the dialogue.

[0441] Specific examples

[0442] Example prompt: "User: Hello."

[0443] This system allows users to enjoy intuitive and natural conversations, making effective communication possible even in an aging society.

[0444] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0445] Step 1:

[0446] The user enters text.

[0447] The user enters text into the device's interface to initiate a conversation, for example, "Hello." This text is sent to the device as is.

[0448] Input: The text entered by the user (e.g. "Hello")

[0449] Output: Text sent to the terminal

[0450] Step 2:

[0451] The terminal formats the text.

[0452] The device formats the received text into the specified prompt format, for example, "User: Hello" in order to provide input to the generative AI model in the appropriate format.

[0453] Input: The text entered by the user (e.g. "Hello")

[0454] Output: A formatted prompt (e.g. "User: Hello")

[0455] Step 3:

[0456] The terminal sends a prompt to the server.

[0457] The device composes the formatted prompt as an HTTP request, attaches an API key for authentication, and sends it to the server.

[0458] Input: A formatted prompt (e.g. "User: Hello"), API key

[0459] Output: HTTP request sent to server

[0460] Step 4:

[0461] The server sends a request to the generative AI model.

[0462] The server sends the prompt, possibly with additional configuration parameters, to the generative AI model, which receives the request and generates a natural-sounding response to the user's input.

[0463] Input: A formatted prompt (e.g. "User: Hello")

[0464] Output: The generated response (e.g., "Hello! What would you like to talk about today?")

[0465] Step 5:

[0466] The server receives the response from the generative AI model and sends it back to the device.

[0467] The server receives the generated response, composes it as an HTTP response, and sends it to the terminal.

[0468] Input: The generated response (e.g., "Hello! What would you like to talk about today?")

[0469] Output: The HTTP response is sent to the device

[0470] Step 6:

[0471] The terminal displays the response.

[0472] The terminal displays the response received from the server in the chat interface, allowing the user to check the displayed response.

[0473] Input: HTTP response from the server (e.g., "Hello! What would you like to talk about today?")

[0474] Output: The response displayed in the chat interface

[0475] Step 7:

[0476] The user ends the interaction.

[0477] When the user enters a specific command, such as "end," the device detects it and instructs the server to end the dialogue. The server then stops making requests to the generative AI model and ends the dialogue.

[0478] Input: A specific command entered by the user (e.g., "quit")

[0479] Output: Server ends conversation

[0480] (Application example 1)

[0481] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0482] In today's online shopping environment, it is difficult for users to find the products they want from the vast amount of information available. Furthermore, there are not enough support tools available for efficient shopping via mobile devices such as smartphones and head-mounted displays. This problem needs to be solved.

[0483] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0484] In this invention, the server includes means for receiving text entered by a user, means for formatting the text into a specified prompt format, means for transmitting the formatted prompt to the server, means for the server to generate a response corresponding to the input text using a generative artificial intelligence model, means for receiving the response from the server and displaying it to the user, and means for responding to questions from the user and supporting product recommendations and purchasing procedures, thereby enabling the user to search for products and complete purchasing procedures quickly and effectively through natural dialogue.

[0485] A "user" is someone who uses the system to input text and receive support.

[0486] "Text" refers to character information that a user inputs through a terminal.

[0487] "Prompt formatting" is a method for formatting user-entered text into a format suitable for a generative AI model.

[0488] The "server" is a central processing unit that processes the text entered by the user and generates a response using a generative AI model.

[0489] A "generative AI model" is an algorithm that uses natural language processing techniques to generate appropriate responses to user input.

[0490] A "response" is a response generated by a generative AI model based on user input.

[0491] A "display" is a method for visually indicating the server-generated response on the user's terminal.

[0492] "Means of responding to questions and supporting product recommendations and purchasing procedures" refers to a function in which a generative AI model recommends appropriate products in response to questions entered by the user and assists in the purchasing process.

[0493] An "exit command" is a specific keyword or phrase that indicates the user's intention to end the interaction.

[0494] This invention is a system that uses text entered by users in natural language and utilizes a generative AI model to recommend products and assist with purchasing procedures in online shopping.

[0495] System Program Overview

[0496] The system consists of the following components:

[0497] 1. User device: A device such as a smartphone or head-mounted display that allows the user to input text and receive responses.

[0498] 2. Server: The central processing unit that runs the generative AI model and processes user requests.

[0499] 3. Generative AI models: Algorithms that use natural language processing techniques to generate appropriate responses to user input (e.g., GPT-3 and GPT-4).

[0500] System operation procedure

[0501] 1. Text input:

[0502] The user types a question through the device's interface, for example, "What are the most popular smartphones?"

[0503] 2. Prompt formatting:

[0504] The device converts the text entered by the user into a specific prompt format that is easy for the generative AI model to understand, for example, "User: What are the most popular smartphones?"

[0505] 3. Send to server:

[0506] The formatted prompt is sent from the terminal to the server.

[0507] 4. Response Generation:

[0508] The server sends prompts and generates responses through the API of the generative AI model, which creates appropriate responses based on its extensive database.

[0509] 5. Receiving and displaying responses:

[0510] The server receives the generated response and sends it back to the terminal, which displays it to the user.

[0511] Specific processing flow

[0512] Consider a case where a user types, "What are the popular smartphones?" This text is formatted by the device into the format "User: What are the popular smartphones?" and sent to the server. The server uses a generative AI model to generate a response such as "Currently, the iPhone and Galaxy S series are very popular," and sends it back to the device. This response is displayed on the user's device, allowing the user to obtain appropriate product information.

[0513] The system enables users to quickly and effectively search for and purchase products through natural interactions. The hardware used is a smartphone and a head-mounted display, and the software uses generative AI model APIs (e.g., OpenAI's GPT-3 and GPT-4).

[0514] Example prompt sentence:

[0515] "User: What's the most popular smartphone?"

[0516] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0517] Step 1:

[0518] The user inputs text through the device interface. The specific text input is the question, "What are the most popular smartphones?" This input text is the first target for processing by the system.

[0519] Step 2:

[0520] The device formats the input text into a prompt that is easy for the generative AI model to understand. Specifically, the user's input, "What are the popular smartphones?", is converted to the format "User: What are the popular smartphones?". This formatted prompt is sent to the next step.

[0521] Step 3:

[0522] The formatted prompt is sent from the terminal to the server, where it is securely transmitted to the server using the HTTPS protocol, including the user session information along with the prompt.

[0523] Step 4:

[0524] The server sends the received prompt to the generative AI model. This involves constructing an API request and sending an HTTP POST request to the generative AI model's endpoint. The specific input sent to the API is "User: What are the popular smartphones?"

[0525] Step 5:

[0526] The generative AI model generates an appropriate response based on the input prompt. Here, natural language processing techniques are used to generate a text response such as, "The iPhone and Galaxy S series are very popular right now." After this response text is generated, it is returned to the server as an API response.

[0527] Step 6:

[0528] The server receives the response from the generative AI model and sends it to the user's device. Specifically, it sends the response text received as an HTTP response to the user's device. At this time, the generated response text "iPhone and Galaxy S series are currently very popular" is included as the response.

[0529] Step 7:

[0530] The device receives the response from the server and displays the response text to the user. Specifically, the text "iPhone and Galaxy S series are currently very popular" is displayed on the user's screen. This allows the user to obtain the information they were looking for.

[0531] Through the above steps, users can quickly and effectively obtain product information through natural dialogue, and enjoy a pleasant online shopping experience.

[0532] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0533] The interactive chat platform of the present invention is a system that converts text entered by a user into natural responses using a generative AI model and further combines it with an emotion engine to provide responses that take the user's emotions into consideration. Specific embodiments of the present invention are described in detail below.

[0534] The system involves a user inputting text via a terminal, processing it on a server, and generating a response using a generative AI model and an emotion engine.

[0535] First, consider the case where a user types "I'm very happy today" into a terminal. The user types text into their terminal's interface, and that text is received by the terminal. The terminal formats this user input into a specified prompt format, for example, "User: I'm very happy today."

[0536] The device then sends the formatted prompt to the server as an HTTP request, which includes the API key along with the prompt.

[0537] The server receives the HTTP request sent from the device, extracts the prompt and API key from the request, and sends the input text to the emotion engine to analyze the user's emotions.

[0538] The emotion engine recognizes emotions from the user's input text, and the emotion "joy" is identified. The analysis results are returned to the server.

[0539] The server sends the emotion engine's analysis results along with prompts to the generative AI model, which generates an appropriate response that takes emotion into account. The generative AI model uses natural language processing techniques to generate a response based on the user input and the recognized emotion. For example, the response generated might be, "That's great! Did something good happen?"

[0540] The generated response is received by the server, which returns the response to the terminal, which again includes the response text in an HTTP response and sends it to the terminal.

[0541] The device receives the HTTP response from the server and displays the response text in the chat interface. The user can see the response "AI: That's great! Did something good happen?" through the device.

[0542] The system's functionality also includes a means to terminate the dialogue when the user inputs a specific command. For example, if the user inputs "end," the device detects this command and instructs the server to end the dialogue. This causes the server to stop making requests to the generative AI model, and the dialogue ends.

[0543] This system will enable users to enjoy natural and effective communication even in an aging society, and is expected to revive and revitalize existing telephone culture. Furthermore, by combining it with an emotion engine, it will be possible to have more human-like conversations that take into account the user's emotions.

[0544] The processing flow will be explained below.

[0545] Step 1:

[0546] The user inputs text into the terminal interface. Specifically, for example, the user types "I'm very happy today" into the chat window and presses the enter key to send it.

[0547] Step 2:

[0548] The terminal receives the user's input text. The terminal formats the input text, such as "User: I'm very happy today," and converts it into a prompt format.

[0549] Step 3:

[0550] The device sends the formatted prompt to the server as an HTTP request, which includes the API key along with the prompt.

[0551] Step 4:

[0552] The server receives the HTTP request sent from the device and extracts the prompt and API key from the request.

[0553] Step 5:

[0554] The server sends prompts to the emotion engine, which analyzes the emotion from the user's input text and evaluates the emotional state.

[0555] Step 6:

[0556] The emotion engine recognizes emotions from the user's input text. Specifically, it identifies the emotion "joy" from the text "I'm so happy today."

[0557] Step 7:

[0558] The emotion engine returns the analysis results to the server, which receives the analysis results from the emotion engine and uses them for further processing.

[0559] Step 8:

[0560] The server sends the emotion engine's analysis results along with a prompt to the generative AI model, which generates a response based on the prompt and emotion information.

[0561] Step 9:

[0562] A generative AI model generates a response based on the prompt and emotional information, such as "That's great! Did something good happen?"

[0563] Step 10:

[0564] The server receives the response from the generative AI model, formats it, and prepares it to be sent back to the device as an HTTP response.

[0565] Step 11:

[0566] The server sends an HTTP response to the device, which contains the text of the generated response.

[0567] Step 12:

[0568] The device receives the HTTP response from the server, extracts the response text from the response, and displays it in the chat interface.

[0569] Step 13:

[0570] The user sees the response generated through the device, for example, "AI: That's great! What's going on?" displayed in the chat window.

[0571] Step 14:

[0572] When the user inputs a specific end command, the terminal detects the command and instructs the server to end the conversation. Specifically, when the user inputs "end," the terminal sends the instruction to the server.

[0573] Step 15:

[0574] The server ends the conversation, which stops requests to the generative AI model and ends the conversation.

[0575] Example 2

[0576] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0577] Conventional chat systems simply generate responses without considering the user's emotions, which often results in unnatural and robotic interactions. Furthermore, the end of a conversation is not clearly managed, which can lead to confusion when a user wants to end the conversation. To address these issues, a system is needed that generates natural responses that take emotions into account and clearly manages the end of a conversation.

[0578] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0579] In this invention, the server includes means for performing emotion analysis, means for taking the user's emotion into account in the generated response, and means for terminating the dialogue when the user inputs a specific end command, thereby providing a natural dialogue that takes the user's emotion into account and enabling a clear and smooth end to the dialogue.

[0580] "User-entered text" is a string of characters that a user enters into the chat interface using a terminal.

[0581] A "specified prompt format" is text entered by a user that has been converted into a specific format that is easy for a generative AI model to analyze.

[0582] A "formatted prompt" is a user-entered text that has been converted into a specified prompt format.

[0583] The "server" is the core computer system that uses generative AI models to generate responses to user input text and also performs sentiment analysis.

[0584] A "generative artificial intelligence model" is an algorithm or software that uses natural language processing techniques to generate appropriate responses based on a user's input text.

[0585] "Sentiment analysis" is the process of identifying emotions from user-entered text and analyzing those emotions.

[0586] "Display to user" means sending the generated response from the server to the terminal and making it visible to the user in the chat interface.

[0587] A "specific exit command" is a predefined string or keyword that the user enters to end the dialogue.

[0588] The "means for terminating the dialogue" is a mechanism by which the server stops requests to the generative AI model and ends the dialogue when it receives a specific termination command.

[0589] This invention relates to an interactive chat platform that converts text entered by a user into natural-sounding responses using a generative AI model and then combines it with an emotion analysis engine to provide responses that take the user's emotions into account. The following describes how to specifically implement the invention.

[0590] In this system, users input text via a terminal, which is then processed by the server, and a response is generated using a generative AI model and a sentiment analysis engine. Below, we will explain the specific equipment and software used.

[0591] Hardware and software:

[0592] Device: A device that allows a user to input text, such as a smartphone, tablet, or computer.

[0593] Server: A high-performance computing system that runs the generative AI model and sentiment analysis engine.

[0594] Generative AI models: AI platforms that use natural language processing techniques (e.g., open-source GPT-based models).

[0595] Sentiment analysis engine: Software for analyzing emotions from text (e.g., emotion recognition API).

[0596] Process flow:

[0597] 1. Receiving and formatting user input:

[0598] The user enters text into the device's chat interface. For example, they enter "I'm so happy today." This input text is formatted by the device to read "User: I'm so happy today."

[0599] 2. Sending the prompt to the server:

[0600] The formatted prompt is then sent to the server as an HTTP request, which includes the API key along with the prompt.

[0601] 3. Emotion analysis:

[0602] The server receives the HTTP request, extracts the prompt and API key, and then sends the prompt to a sentiment analysis engine to analyze the user's sentiment. The sentiment analysis engine identifies the sentiment "joy" from the text "I'm so happy today" and returns the result to the server.

[0603] 4. Generate response:

[0604] The server sends the emotion analysis results to the generative AI model, which generates an appropriate response that takes the emotion into account. The generative AI model generates a response such as "That's great! Did something good happen?" and returns the result to the server.

[0605] 5. Send and display the response:

[0606] The server sends the generated response as an HTTP response to the device. The device receives the response and displays "AI: That's great! What's going on?" in the chat interface.

[0607] 6. End the conversation (optional):

[0608] If the user wants to end the conversation, they input "end." The device notifies the server of this end command, and the server stops making requests to the generative AI model and ends the conversation.

[0609] Examples:

[0610] User input: "I'm very happy today."

[0611] Formatted prompt: "User: I'm very happy today."

[0612] Generated response: "That's great! Did something good happen?"

[0613] This system allows users to enjoy more natural and effective communication that takes emotions into account. Furthermore, even in an aging society, this system will enable conversations that are sensitive to the user's emotions, and is expected to revitalize existing means of communication.

[0614] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0615] Step 1:

[0616] Receiving User Input

[0617] The user enters text into the chat interface of the terminal.

[0618] Input: User types "I'm so happy today"

[0619] Action: The terminal receives the text entered by the user.

[0620] Output: The device receives the text "I'm so happy today."

[0621] Step 2:

[0622] Text formatting

[0623] The terminal formats the text entered by the user into the specified prompt format.

[0624] Input: User text input: "I'm so happy today"

[0625] What it does: The terminal converts the input text to the format "User: I'm so happy today."

[0626] Output: A formatted prompt: "User: I'm very happy today."

[0627] Step 3:

[0628] Sending an HTTP request

[0629] The formatted prompt is sent as an HTTP request to the server.

[0630] Input: The formatted prompt "User: I'm so happy today" and your API key

[0631] What it does: The device sends an HTTP request to the server, including a formatted prompt.

[0632] Output: HTTP request sent to server

[0633] Step 4:

[0634] Receiving and processing requests

[0635] The server receives the HTTP request and extracts the prompt and API key.

[0636] Input: HTTP request sent from the terminal

[0637] What happens: The server extracts the prompt and API key from the request body.

[0638] Output: The prompt "User: I'm so happy today" and the corresponding API key

[0639] Step 5:

[0640] Emotion analysis

[0641] The server sends the prompt to a sentiment analysis engine to analyze the user's sentiment.

[0642] Input: prompt "User: I'm so happy today"

[0643] Action: The server sends a prompt to the sentiment analysis engine to perform sentiment analysis.

[0644] Output: The sentiment analysis engine returns the result "joy."

[0645] Step 6:

[0646] Generating a response

[0647] The server sends the emotion analysis results to the generative AI model, which generates an appropriate response.

[0648] Input: prompt "User: I'm very happy today" and parsed result "joy"

[0649] How it works: The server sends prompts and sentiment analysis results to the generative AI model, which then generates a response.

[0650] Output: The generative AI model generates the response "That's great! What good happened?"

[0651] Step 7:

[0652] Sending a Response

[0653] The server sends the generated response to the terminal as an HTTP response.

[0654] Input: Generated response "That's great! Did something good happen?"

[0655] Action: The server sends this response to the device in the form of an HTTP response.

[0656] Output: The HTTP response is sent to the device

[0657] Step 8:

[0658] Viewing the response

[0659] The device receives the HTTP response from the server and displays it in the chat interface.

[0660] Input: HTTP response from the server: "That's great! What's the big deal?"

[0661] Behavior: The device displays the response in the chat interface.

[0662] Output: "AI: That's great! Did something good happen?"

[0663] Step 9:

[0664] Ending the conversation (optional)

[0665] If the user wishes to end the dialogue, he / she enters "Exit".

[0666] Input: User types "Exit"

[0667] Action: The device notifies the server of this termination command.

[0668] Output: The server stops making requests to the generative AI model and ends the conversation.

[0669] (Application example 2)

[0670] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0671] Conventional dialogue systems simply generate responses to user input, but are unable to provide responses that take the user's emotions into account. Furthermore, if the dialogue between customers and store clerks in a brick-and-mortar store is mechanical, customer satisfaction may decline. To solve these problems, a system is needed that can generate responses that take emotions into account while still allowing for natural dialogue.

[0672] The specification process by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving text entered by a user, means for formatting the text into a specified prompt format, means for transmitting the formatted prompt to the server, means for the server to generate a response corresponding to the input text using a generative artificial intelligence model, means for receiving the response from the server and displaying it to the user, emotion analysis means for recognizing the user's emotion, and means for generating a response based on the recognized emotion. This enables natural dialogue that takes the user's emotion into consideration.

[0673] The "means for receiving text input by the user" is an interface for obtaining text data input by the user through the terminal.

[0674] A "means for formatting into a specified prompt format" is a program that converts text entered by a user into a specific format and formats it as a prompt to be passed to a generative AI model.

[0675] The "means for transmitting the formatted prompt to the server" is a function for transferring the formatted prompt to the server through a communication network.

[0676] "Means for the server to use a generative AI model to generate a response corresponding to the input text" refers to a process in which the server uses a generative AI model to automatically generate a response based on the user's input text.

[0677] The "means for receiving the response from the server and displaying it to the user" is an interface for obtaining the response sent from the server and displaying the content of the response on the user's terminal.

[0678] The "emotion analysis means for recognizing user emotions" is an algorithm for analyzing emotions from text entered by the user and identifying those emotions.

[0679] The "means for generating a response based on the recognized emotion" is a program for generating a response that takes into account the emotion recognized by the emotion analysis means.

[0680] "Natural language processing technology" is a general term for technologies that allow computers to understand, analyze, and generate human language.

[0681] The "means for terminating the dialogue" is a function for terminating the dialogue session when the user inputs a specific end command.

[0682] This invention applies an interactive chat platform that provides emotion-aware responses to customer service support in brick-and-mortar stores. This system processes text entered by users via smartphones or tablets using a generative AI model and emotion engine to generate natural, emotion-aware responses.

[0683] The overall system configuration is as follows: a device such as a smartphone or tablet on which the user inputs text, a program that formats the text into a specific prompt format, a server that receives the formatted prompt and generates an appropriate response using a generative AI model and an emotion engine, and a program that displays the generated response on the user's device.

[0684] First, the user inputs text via a smartphone or tablet. For example, the user inputs a message such as "I'm not feeling so good today." This text is then formatted by the device's program into a specified prompt format, such as "User: I'm not feeling so good today."

[0685] The formatted prompt is sent to the server as an HTTP request, along with the required API key. The server receives the HTTP request and first sends the input text to an emotion engine to analyze the user's emotions. The emotion engine recognizes emotions from the text and identifies the emotion "sad," for example. The analysis results are returned to the server, which then sends them along with the prompt to a generative AI model to generate an appropriate response that takes the emotions into account.

[0686] The generative AI model uses natural language processing (NLP) technology to generate a response based on the user input and the recognized emotion. For example, a response such as "That's unfortunate. What's wrong?" is generated. This response is received by the server and sent to the device as an HTTP response. The device receives this HTTP response and displays the response in the chat interface. The user can see the response: "AI: That's unfortunate. What's wrong?"

[0687] As a concrete example, consider a scenario in which a store clerk is using a smartphone to interact with a customer. If the customer types, "I'm not feeling very good today," the clerk's smartphone sends this input to the server, and the emotion engine recognizes it as "sad." The generative AI model then generates a response, "That's unfortunate. What's wrong?", which is displayed on the clerk's smartphone, enabling more sympathetic customer service.

[0688] The hardware and software used include smartphones, tablets, servers, generative AI models (e.g., OpenAI's GPT-3), emotion engines (e.g., Emotion API), and HTTP communication libraries (e.g., Python requests).

[0689] An example prompt is:

[0690] User: I'm not feeling too great today

[0691] AI: That's unfortunate. What happened?

[0692] In this way, interactions with customers in physical stores become more natural and emotionally sensitive, which is expected to improve customer satisfaction.

[0693] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0694] Step 1:

[0695] A user inputs text via a smartphone or tablet, for example, "I'm not feeling very good today." This input text is sent to the device.

[0696] Step 2:

[0697] The terminal formats the received text into the specified prompt format, e.g., "User: I'm not feeling too great today." This formatted prompt is temporarily stored in the terminal.

[0698] Step 3:

[0699] The formatted prompt is sent from the terminal to the server. Specifically, it is sent as an HTTP request, and the request includes the prompt text and the API key. This request reaches the server.

[0700] Step 4:

[0701] The server receives the HTTP request sent from the device, extracts the prompt and API key from the request, and sends the extracted text to the emotion engine for sentiment analysis.

[0702] Step 5:

[0703] The emotion engine analyzes the received input text and recognizes the user's emotion. For example, the emotion "sad" is recognized. The emotion analysis result is returned to the server from the emotion engine.

[0704] Step 6:

[0705] The server sends the emotion analysis results and prompts returned by the emotion engine to the generative AI model, which uses natural language processing techniques to generate an appropriate response that takes emotion into account. For example, the generated response might be, "That's unfortunate. What's wrong?"

[0706] Step 7:

[0707] The server receives the generated response and sends it to the terminal as an HTTP response, which includes the generated response text.

[0708] Step 8:

[0709] The device receives the HTTP response from the server and displays the response text in the chat interface. For example, the response "AI: That's unfortunate. What's wrong?" is displayed on the user's device.

[0710] The above is the specific processing flow of the system for realizing an emotion-aware interactive chat platform in a physical store.

[0711] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0712] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0713] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0714] [Third embodiment]

[0715] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0716] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0717] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0718] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0719] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0720] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0721] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0722] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0723] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0724] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0725] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0726] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0727] The interactive chat platform of the present invention is a system that converts text entered by a user into natural-sounding responses using a generative AI model and provides the responses to the user. Specific embodiments of the present invention are described in detail below.

[0728] The system involves a user inputting text through a terminal, processing it on a server, and generating a response using a generative AI model.

[0729] First, consider the case where a user types "Hello" into a terminal. The user types text into the terminal's interface, and that text is received by the terminal. The terminal formats this user input into a specified prompt format, for example, "User: Hello."

[0730] The device then sends the formatted prompt to the server, where it uses the generative AI model's API to construct an HTTP request to send the prompt, including the user's input, to the server, including the API key for authentication.

[0731] The server receives the HTTP request sent from the device and sends a prompt to the generative AI model. The generative AI model uses natural language processing techniques to generate an appropriate response to the user input. For example, the AI ​​model can generate the response, "Hello! What would you like to talk about today?"

[0732] The generated response is received by the server, which returns the response to the terminal, which again includes the response text in an HTTP response and sends it to the terminal.

[0733] The device receives the HTTP response from the server and displays the response text in the chat interface. The user can see the response "AI: Hello! What would you like to talk about today?" through the device.

[0734] The system's functionality also includes a means to terminate the dialogue when the user inputs a specific command. For example, if the user inputs "end," the device detects this command and instructs the server to end the dialogue. This causes the server to stop making requests to the generative AI model and end the dialogue.

[0735] This system will enable users to enjoy natural and effective communication even in an aging society, and is expected to revive and revitalize existing telephone culture.

[0736] The processing flow will be explained below.

[0737] Step 1:

[0738] The user enters text into the terminal interface. Specifically, the user types "Hello" into the chat window and presses the Enter key to send it.

[0739] Step 2:

[0740] The terminal receives the user's input text. The terminal formats the input text, such as "User: Hello", and converts it into a prompt format.

[0741] Step 3:

[0742] The device sends the formatted prompt to the server as an HTTP request, which includes the API key along with the prompt.

[0743] Step 4:

[0744] The server receives the HTTP request sent from the device, extracts the prompt and API key from the request, and prepares to send them to the API for the generative AI model.

[0745] Step 5:

[0746] The server sends a prompt, specifically "User: Hello," to the generative AI model, requesting it to generate a response.

[0747] Step 6:

[0748] A generative AI model generates a response based on the prompt, for example, "Hello! What would you like to talk about today?"

[0749] Step 7:

[0750] The server receives the response from the generative AI model, formats it, and prepares it to be sent back to the device as an HTTP response.

[0751] Step 8:

[0752] The server sends an HTTP response to the device, which contains the text of the generated response.

[0753] Step 9:

[0754] The terminal receives the HTTP response from the server, extracts the response text from the response, and displays it to the user.

[0755] Step 10:

[0756] The user sees a response generated through the device, for example, "AI: Hello! What would you like to talk about today?" displayed in the chat window.

[0757] Step 11:

[0758] When the user inputs a specific end command, the terminal detects the command and instructs the server to end the conversation. Specifically, when the user inputs "end," the terminal sends the instruction to the server.

[0759] Step 12:

[0760] The server ends the conversation, which stops requests to the generative AI model and ends the conversation.

[0761] Example 1

[0762] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0763] Conventional dialogue systems face the challenge of generating intuitive and natural responses to user input. Furthermore, if proper authentication and formatting are not performed during the response generation process, the system may become unstable. Furthermore, if the user wishes to end the dialogue, the process can be cumbersome. To address these issues, a system is needed that can generate natural responses based on user input and process data efficiently and safely.

[0764] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0765] In this invention, the server includes: means for receiving text entered by a user; means for formatting the text into a specified prompt format; means for transmitting the formatted prompt to the server; means for the server to generate a response corresponding to the input text using a generative artificial intelligence model; means for receiving the response from the server and displaying it to the user; means for terminating the dialogue when the user enters a specific end command; and means for constructing an HTTP request including the formatted prompt and including an API key for authentication. This allows users to have an intuitive and natural dialogue, process data safely after appropriate authentication, and easily terminate the dialogue.

[0766] "User" refers to an individual or entity that interacts with the system.

[0767] "Input text" refers to character string data that a user sends to the dialogue system through a terminal.

[0768] A "specified prompt format" refers to the rules and formatting that are used to format text entered by a user into a particular format.

[0769] "Formatting means" refers to the hardware or software functionality for converting user-entered text into a specified prompt format.

[0770] A "server" refers to a computer system that performs a series of processes to process user requests and return generated responses.

[0771] "Generative artificial intelligence models" refer to algorithms or systems that use machine learning techniques to generate natural-looking responses.

[0772] "Response" refers to the dialogue content generated by the generative artificial intelligence model in response to a user's input.

[0773] The "means for displaying" refers to an interface or function for displaying the generated response on the terminal so that the user can check it.

[0774] An "exit command" refers to a specific string or signal that a user inputs into the system to end a dialogue.

[0775] An "HTTP request" refers to the message format of a data request sent from a client (terminal) to a server.

[0776] An "API key" is a unique string of characters used for authentication and access control when using a specific application program interface (API).

[0777] The present invention relates to an interactive chat platform that converts text entered by a user into natural-sounding responses using a generative AI model and provides the responses to the user. The present invention is implemented using specific hardware and software.

[0778] This system consists of the following main components: a terminal, a server, and a generative AI model. The role of each component is explained in detail below.

[0779] Hardware and Software Examples

[0780] Hardware: Devices used include smartphones, tablets, and PCs. Servers include cloud servers and on-premise servers.

[0781] Software: The generative AI model uses a model based on common natural language processing techniques (e.g., GPT-3), and also includes an API interface, HTTP protocol, and authentication functions.

[0782] A user first enters text into the terminal's interface. This text is received by the terminal. For example, if a user enters "Hello," the terminal formats this input into a specific prompt format, specifically, "User: Hello."

[0783] The device then sends the formatted prompt to the server, which in the process constructs an HTTP request and uses the API key for authentication. The device sends this request to the server, which then parses it.

[0784] The server sends the received prompt to the generative AI model, which generates an appropriate response to the user's input. In this case, the generative AI model uses natural language processing technology to generate a response such as, "Hello! What would you like to talk about today?"

[0785] The generated response is received by the server, which then sends it back to the device as an HTTP response. The device receives the response and displays the response text in the chat interface. The user can see the response "AI: Hello! What would you like to talk about today?" through the device.

[0786] Furthermore, if the user wants to end the dialogue, they can enter a specific end command (e.g., "Exit"). The device detects this command and instructs the server to end the dialogue. The server then stops making requests to the generative AI model and ends the dialogue.

[0787] Specific examples

[0788] Example prompt: "User: Hello."

[0789] This system allows users to enjoy intuitive and natural conversations, making effective communication possible even in an aging society.

[0790] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0791] Step 1:

[0792] The user enters text.

[0793] The user enters text into the device's interface to initiate a conversation, for example, "Hello." This text is sent to the device as is.

[0794] Input: The text entered by the user (e.g. "Hello")

[0795] Output: Text sent to the terminal

[0796] Step 2:

[0797] The terminal formats the text.

[0798] The device formats the received text into the specified prompt format, for example, "User: Hello" in order to provide input to the generative AI model in the appropriate format.

[0799] Input: The text entered by the user (e.g. "Hello")

[0800] Output: A formatted prompt (e.g. "User: Hello")

[0801] Step 3:

[0802] The terminal sends a prompt to the server.

[0803] The device composes the formatted prompt as an HTTP request, attaches an API key for authentication, and sends it to the server.

[0804] Input: A formatted prompt (e.g. "User: Hello"), API key

[0805] Output: HTTP request sent to server

[0806] Step 4:

[0807] The server sends a request to the generative AI model.

[0808] The server sends the prompt, possibly with additional configuration parameters, to the generative AI model, which receives the request and generates a natural-sounding response to the user's input.

[0809] Input: A formatted prompt (e.g. "User: Hello")

[0810] Output: The generated response (e.g., "Hello! What would you like to talk about today?")

[0811] Step 5:

[0812] The server receives the response from the generative AI model and sends it back to the device.

[0813] The server receives the generated response, composes it as an HTTP response, and sends it to the terminal.

[0814] Input: The generated response (e.g., "Hello! What would you like to talk about today?")

[0815] Output: The HTTP response is sent to the device

[0816] Step 6:

[0817] The terminal displays the response.

[0818] The terminal displays the response received from the server in the chat interface, allowing the user to check the displayed response.

[0819] Input: HTTP response from the server (e.g., "Hello! What would you like to talk about today?")

[0820] Output: The response displayed in the chat interface

[0821] Step 7:

[0822] The user ends the interaction.

[0823] When the user enters a specific command, such as "end," the device detects it and instructs the server to end the dialogue. The server then stops making requests to the generative AI model and ends the dialogue.

[0824] Input: A specific command entered by the user (e.g., "quit")

[0825] Output: Server ends conversation

[0826] (Application example 1)

[0827] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0828] In today's online shopping environment, it is difficult for users to find the products they want from the vast amount of information available. Furthermore, there are not enough support tools available for efficient shopping via mobile devices such as smartphones and head-mounted displays. This problem needs to be solved.

[0829] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0830] In this invention, the server includes means for receiving text entered by a user, means for formatting the text into a specified prompt format, means for transmitting the formatted prompt to the server, means for the server to generate a response corresponding to the input text using a generative artificial intelligence model, means for receiving the response from the server and displaying it to the user, and means for responding to questions from the user and supporting product recommendations and purchasing procedures, thereby enabling the user to search for products and complete purchasing procedures quickly and effectively through natural dialogue.

[0831] A "user" is someone who uses the system to input text and receive support.

[0832] "Text" refers to character information that a user inputs through a terminal.

[0833] "Prompt formatting" is a method for formatting user-entered text into a format suitable for a generative AI model.

[0834] The "server" is a central processing unit that processes the text entered by the user and generates a response using a generative AI model.

[0835] A "generative AI model" is an algorithm that uses natural language processing techniques to generate appropriate responses to user input.

[0836] A "response" is a response generated by a generative AI model based on user input.

[0837] A "display" is a method for visually indicating the server-generated response on the user's terminal.

[0838] "Means of responding to questions and supporting product recommendations and purchasing procedures" refers to a function in which a generative AI model recommends appropriate products in response to questions entered by the user and assists in the purchasing process.

[0839] An "exit command" is a specific keyword or phrase that indicates the user's intention to end the interaction.

[0840] This invention is a system that uses text entered by users in natural language and utilizes a generative AI model to recommend products and assist with purchasing procedures in online shopping.

[0841] System Program Overview

[0842] The system consists of the following components:

[0843] 1. User device: A device such as a smartphone or head-mounted display that allows the user to input text and receive responses.

[0844] 2. Server: The central processing unit that runs the generative AI model and processes user requests.

[0845] 3. Generative AI models: Algorithms that use natural language processing techniques to generate appropriate responses to user input (e.g., GPT-3 and GPT-4).

[0846] System operation procedure

[0847] 1. Text input:

[0848] The user types a question through the device's interface, for example, "What are the most popular smartphones?"

[0849] 2. Prompt formatting:

[0850] The device converts the text entered by the user into a specific prompt format that is easy for the generative AI model to understand, for example, "User: What are the most popular smartphones?"

[0851] 3. Send to server:

[0852] The formatted prompt is sent from the terminal to the server.

[0853] 4. Response Generation:

[0854] The server sends prompts and generates responses through the API of the generative AI model, which creates appropriate responses based on its extensive database.

[0855] 5. Receiving and displaying responses:

[0856] The server receives the generated response and sends it back to the terminal, which displays it to the user.

[0857] Specific processing flow

[0858] Consider a case where a user types, "What are the popular smartphones?" This text is formatted by the device into the format "User: What are the popular smartphones?" and sent to the server. The server uses a generative AI model to generate a response such as "Currently, the iPhone and Galaxy S series are very popular," and sends it back to the device. This response is displayed on the user's device, allowing the user to obtain appropriate product information.

[0859] The system enables users to quickly and effectively search for and purchase products through natural interactions. The hardware used is a smartphone and a head-mounted display, and the software uses generative AI model APIs (e.g., OpenAI's GPT-3 and GPT-4).

[0860] Example prompt sentence:

[0861] "User: What's the most popular smartphone?"

[0862] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0863] Step 1:

[0864] The user inputs text through the device interface. The specific text input is the question, "What are the most popular smartphones?" This input text is the first target for processing by the system.

[0865] Step 2:

[0866] The device formats the input text into a prompt that is easy for the generative AI model to understand. Specifically, the user's input, "What are the popular smartphones?", is converted to the format "User: What are the popular smartphones?". This formatted prompt is sent to the next step.

[0867] Step 3:

[0868] The formatted prompt is sent from the terminal to the server, where it is securely transmitted to the server using the HTTPS protocol, including the user session information along with the prompt.

[0869] Step 4:

[0870] The server sends the received prompt to the generative AI model. This involves constructing an API request and sending an HTTP POST request to the generative AI model's endpoint. The specific input sent to the API is "User: What are the popular smartphones?"

[0871] Step 5:

[0872] The generative AI model generates an appropriate response based on the input prompt. Here, natural language processing techniques are used to generate a text response such as, "The iPhone and Galaxy S series are very popular right now." After this response text is generated, it is returned to the server as an API response.

[0873] Step 6:

[0874] The server receives the response from the generative AI model and sends it to the user's device. Specifically, it sends the response text received as an HTTP response to the user's device. At this time, the generated response text "iPhone and Galaxy S series are currently very popular" is included as the response.

[0875] Step 7:

[0876] The device receives the response from the server and displays the response text to the user. Specifically, the text "iPhone and Galaxy S series are currently very popular" is displayed on the user's screen. This allows the user to obtain the information they were looking for.

[0877] Through the above steps, users can quickly and effectively obtain product information through natural dialogue, and enjoy a pleasant online shopping experience.

[0878] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0879] The interactive chat platform of the present invention is a system that converts text entered by a user into natural responses using a generative AI model and further combines it with an emotion engine to provide responses that take the user's emotions into consideration. Specific embodiments of the present invention are described in detail below.

[0880] The system involves a user inputting text via a terminal, processing it on a server, and generating a response using a generative AI model and an emotion engine.

[0881] First, consider the case where a user types "I'm very happy today" into a terminal. The user types text into their terminal's interface, and that text is received by the terminal. The terminal formats this user input into a specified prompt format, for example, "User: I'm very happy today."

[0882] The device then sends the formatted prompt to the server as an HTTP request, which includes the API key along with the prompt.

[0883] The server receives the HTTP request sent from the device, extracts the prompt and API key from the request, and sends the input text to the emotion engine to analyze the user's emotions.

[0884] The emotion engine recognizes emotions from the user's input text, and the emotion "joy" is identified. The analysis results are returned to the server.

[0885] The server sends the emotion engine's analysis results along with prompts to the generative AI model, which generates an appropriate response that takes emotion into account. The generative AI model uses natural language processing techniques to generate a response based on the user input and the recognized emotion. For example, the response generated might be, "That's great! Did something good happen?"

[0886] The generated response is received by the server, which returns the response to the terminal, which again includes the response text in an HTTP response and sends it to the terminal.

[0887] The device receives the HTTP response from the server and displays the response text in the chat interface. The user can see the response "AI: That's great! Did something good happen?" through the device.

[0888] The system's functionality also includes a means to terminate the dialogue when the user inputs a specific command. For example, if the user inputs "end," the device detects this command and instructs the server to end the dialogue. This causes the server to stop making requests to the generative AI model, and the dialogue ends.

[0889] This system will enable users to enjoy natural and effective communication even in an aging society, and is expected to revive and revitalize existing telephone culture. Furthermore, by combining it with an emotion engine, it will be possible to have more human-like conversations that take into account the user's emotions.

[0890] The processing flow will be explained below.

[0891] Step 1:

[0892] The user inputs text into the terminal interface. Specifically, for example, the user types "I'm very happy today" into the chat window and presses the enter key to send it.

[0893] Step 2:

[0894] The terminal receives the user's input text. The terminal formats the input text, such as "User: I'm very happy today," and converts it into a prompt format.

[0895] Step 3:

[0896] The device sends the formatted prompt to the server as an HTTP request, which includes the API key along with the prompt.

[0897] Step 4:

[0898] The server receives the HTTP request sent from the device and extracts the prompt and API key from the request.

[0899] Step 5:

[0900] The server sends prompts to the emotion engine, which analyzes the emotion from the user's input text and evaluates the emotional state.

[0901] Step 6:

[0902] The emotion engine recognizes emotions from the user's input text. Specifically, it identifies the emotion "joy" from the text "I'm so happy today."

[0903] Step 7:

[0904] The emotion engine returns the analysis results to the server, which receives the analysis results from the emotion engine and uses them for further processing.

[0905] Step 8:

[0906] The server sends the emotion engine's analysis results along with a prompt to the generative AI model, which generates a response based on the prompt and emotion information.

[0907] Step 9:

[0908] A generative AI model generates a response based on the prompt and emotional information, such as "That's great! Did something good happen?"

[0909] Step 10:

[0910] The server receives the response from the generative AI model, formats it, and prepares it to be sent back to the device as an HTTP response.

[0911] Step 11:

[0912] The server sends an HTTP response to the device, which contains the text of the generated response.

[0913] Step 12:

[0914] The device receives the HTTP response from the server, extracts the response text from the response, and displays it in the chat interface.

[0915] Step 13:

[0916] The user sees the response generated through the device, for example, "AI: That's great! What's going on?" displayed in the chat window.

[0917] Step 14:

[0918] When the user inputs a specific end command, the terminal detects the command and instructs the server to end the conversation. Specifically, when the user inputs "end," the terminal sends the instruction to the server.

[0919] Step 15:

[0920] The server ends the conversation, which stops requests to the generative AI model and ends the conversation.

[0921] Example 2

[0922] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0923] Conventional chat systems simply generate responses without considering the user's emotions, which often results in unnatural and robotic interactions. Furthermore, the end of a conversation is not clearly managed, which can lead to confusion when a user wants to end the conversation. To address these issues, a system is needed that generates natural responses that take emotions into account and clearly manages the end of a conversation.

[0924] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0925] In this invention, the server includes means for performing emotion analysis, means for taking the user's emotion into account in the generated response, and means for terminating the dialogue when the user inputs a specific end command, thereby providing a natural dialogue that takes the user's emotion into account and enabling a clear and smooth end to the dialogue.

[0926] "User-entered text" is a string of characters that a user enters into the chat interface using a terminal.

[0927] A "specified prompt format" is text entered by a user that has been converted into a specific format that is easy for a generative AI model to analyze.

[0928] A "formatted prompt" is a user-entered text that has been converted into a specified prompt format.

[0929] The "server" is the core computer system that uses generative AI models to generate responses to user input text and also performs sentiment analysis.

[0930] A "generative artificial intelligence model" is an algorithm or software that uses natural language processing techniques to generate appropriate responses based on a user's input text.

[0931] "Sentiment analysis" is the process of identifying emotions from user-entered text and analyzing those emotions.

[0932] "Display to user" means sending the generated response from the server to the terminal and making it visible to the user in the chat interface.

[0933] A "specific exit command" is a predefined string or keyword that the user enters to end the dialogue.

[0934] The "means for terminating the dialogue" is a mechanism by which the server stops requests to the generative AI model and ends the dialogue when it receives a specific termination command.

[0935] This invention relates to an interactive chat platform that converts text entered by a user into natural-sounding responses using a generative AI model and then combines it with an emotion analysis engine to provide responses that take the user's emotions into account. The following describes how to specifically implement the invention.

[0936] In this system, users input text via a terminal, which is then processed by the server, and a response is generated using a generative AI model and a sentiment analysis engine. Below, we will explain the specific equipment and software used.

[0937] Hardware and software:

[0938] Device: A device that allows a user to input text, such as a smartphone, tablet, or computer.

[0939] Server: A high-performance computing system that runs the generative AI model and sentiment analysis engine.

[0940] Generative AI models: AI platforms that use natural language processing techniques (e.g., open-source GPT-based models).

[0941] Sentiment analysis engine: Software for analyzing emotions from text (e.g., emotion recognition API).

[0942] Process flow:

[0943] 1. Receiving and formatting user input:

[0944] The user enters text into the device's chat interface. For example, they enter "I'm so happy today." This input text is formatted by the device to read "User: I'm so happy today."

[0945] 2. Sending the prompt to the server:

[0946] The formatted prompt is then sent to the server as an HTTP request, which includes the API key along with the prompt.

[0947] 3. Emotion analysis:

[0948] The server receives the HTTP request, extracts the prompt and API key, and then sends the prompt to a sentiment analysis engine to analyze the user's sentiment. The sentiment analysis engine identifies the sentiment "joy" from the text "I'm so happy today" and returns the result to the server.

[0949] 4. Generate response:

[0950] The server sends the emotion analysis results to the generative AI model, which generates an appropriate response that takes the emotion into account. The generative AI model generates a response such as "That's great! Did something good happen?" and returns the result to the server.

[0951] 5. Send and display the response:

[0952] The server sends the generated response as an HTTP response to the device. The device receives the response and displays "AI: That's great! What's going on?" in the chat interface.

[0953] 6. End the conversation (optional):

[0954] If the user wants to end the conversation, they input "end." The device notifies the server of this end command, and the server stops making requests to the generative AI model and ends the conversation.

[0955] Examples:

[0956] User input: "I'm very happy today."

[0957] Formatted prompt: "User: I'm very happy today."

[0958] Generated response: "That's great! Did something good happen?"

[0959] This system allows users to enjoy more natural and effective communication that takes emotions into account. Furthermore, even in an aging society, this system will enable conversations that are sensitive to the user's emotions, and is expected to revitalize existing means of communication.

[0960] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0961] Step 1:

[0962] Receiving User Input

[0963] The user enters text into the chat interface of the terminal.

[0964] Input: User types "I'm so happy today"

[0965] Action: The terminal receives the text entered by the user.

[0966] Output: The device receives the text "I'm so happy today."

[0967] Step 2:

[0968] Text formatting

[0969] The terminal formats the text entered by the user into the specified prompt format.

[0970] Input: User text input: "I'm so happy today"

[0971] What it does: The terminal converts the input text to the format "User: I'm so happy today."

[0972] Output: A formatted prompt: "User: I'm very happy today."

[0973] Step 3:

[0974] Sending an HTTP request

[0975] The formatted prompt is sent as an HTTP request to the server.

[0976] Input: The formatted prompt "User: I'm so happy today" and your API key

[0977] What it does: The device sends an HTTP request to the server, including a formatted prompt.

[0978] Output: HTTP request sent to server

[0979] Step 4:

[0980] Receiving and processing requests

[0981] The server receives the HTTP request and extracts the prompt and API key.

[0982] Input: HTTP request sent from the terminal

[0983] What happens: The server extracts the prompt and API key from the request body.

[0984] Output: The prompt "User: I'm so happy today" and the corresponding API key

[0985] Step 5:

[0986] Emotion analysis

[0987] The server sends the prompt to a sentiment analysis engine to analyze the user's sentiment.

[0988] Input: prompt "User: I'm so happy today"

[0989] Action: The server sends a prompt to the sentiment analysis engine to perform sentiment analysis.

[0990] Output: The sentiment analysis engine returns the result "joy."

[0991] Step 6:

[0992] Generating a response

[0993] The server sends the emotion analysis results to the generative AI model, which generates an appropriate response.

[0994] Input: prompt "User: I'm very happy today" and parsed result "joy"

[0995] How it works: The server sends prompts and sentiment analysis results to the generative AI model, which then generates a response.

[0996] Output: The generative AI model generates the response "That's great! What good happened?"

[0997] Step 7:

[0998] Sending a Response

[0999] The server sends the generated response to the terminal as an HTTP response.

[1000] Input: Generated response "That's great! Did something good happen?"

[1001] Action: The server sends this response to the device in the form of an HTTP response.

[1002] Output: The HTTP response is sent to the device

[1003] Step 8:

[1004] Viewing the response

[1005] The device receives the HTTP response from the server and displays it in the chat interface.

[1006] Input: HTTP response from the server: "That's great! What's the big deal?"

[1007] Behavior: The device displays the response in the chat interface.

[1008] Output: "AI: That's great! Did something good happen?"

[1009] Step 9:

[1010] Ending the conversation (optional)

[1011] If the user wishes to end the dialogue, he / she enters "Exit".

[1012] Input: User types "Exit"

[1013] Action: The device notifies the server of this termination command.

[1014] Output: The server stops making requests to the generative AI model and ends the conversation.

[1015] (Application example 2)

[1016] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1017] Conventional dialogue systems simply generate responses to user input, but are unable to provide responses that take the user's emotions into account. Furthermore, if the dialogue between customers and store clerks in a brick-and-mortar store is mechanical, customer satisfaction may decline. To solve these problems, a system is needed that can generate responses that take emotions into account while still allowing for natural dialogue.

[1018] The specification process by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving text entered by a user, means for formatting the text into a specified prompt format, means for transmitting the formatted prompt to the server, means for the server to generate a response corresponding to the input text using a generative artificial intelligence model, means for receiving the response from the server and displaying it to the user, emotion analysis means for recognizing the user's emotion, and means for generating a response based on the recognized emotion. This enables natural dialogue that takes the user's emotion into consideration.

[1019] The "means for receiving text input by the user" is an interface for obtaining text data input by the user through the terminal.

[1020] A "means for formatting into a specified prompt format" is a program that converts text entered by a user into a specific format and formats it as a prompt to be passed to a generative AI model.

[1021] The "means for transmitting the formatted prompt to the server" is a function for transferring the formatted prompt to the server through a communication network.

[1022] "Means for the server to use a generative AI model to generate a response corresponding to the input text" refers to a process in which the server uses a generative AI model to automatically generate a response based on the user's input text.

[1023] The "means for receiving the response from the server and displaying it to the user" is an interface for obtaining the response sent from the server and displaying the content of the response on the user's terminal.

[1024] The "emotion analysis means for recognizing user emotions" is an algorithm for analyzing emotions from text entered by the user and identifying those emotions.

[1025] The "means for generating a response based on the recognized emotion" is a program for generating a response that takes into account the emotion recognized by the emotion analysis means.

[1026] "Natural language processing technology" is a general term for technologies that allow computers to understand, analyze, and generate human language.

[1027] The "means for terminating the dialogue" is a function for terminating the dialogue session when the user inputs a specific end command.

[1028] This invention applies an interactive chat platform that provides emotion-aware responses to customer service support in brick-and-mortar stores. This system processes text entered by users via smartphones or tablets using a generative AI model and emotion engine to generate natural, emotion-aware responses.

[1029] The overall system configuration is as follows: a device such as a smartphone or tablet on which the user inputs text, a program that formats the text into a specific prompt format, a server that receives the formatted prompt and generates an appropriate response using a generative AI model and an emotion engine, and a program that displays the generated response on the user's device.

[1030] First, the user inputs text via a smartphone or tablet. For example, the user inputs a message such as "I'm not feeling so good today." This text is then formatted by the device's program into a specified prompt format, such as "User: I'm not feeling so good today."

[1031] The formatted prompt is sent to the server as an HTTP request, along with the required API key. The server receives the HTTP request and first sends the input text to an emotion engine to analyze the user's emotions. The emotion engine recognizes emotions from the text and identifies the emotion "sad," for example. The analysis results are returned to the server, which then sends them along with the prompt to a generative AI model to generate an appropriate response that takes the emotions into account.

[1032] The generative AI model uses natural language processing (NLP) technology to generate a response based on the user input and the recognized emotion. For example, a response such as "That's unfortunate. What's wrong?" is generated. This response is received by the server and sent to the device as an HTTP response. The device receives this HTTP response and displays the response in the chat interface. The user can see the response: "AI: That's unfortunate. What's wrong?"

[1033] As a concrete example, consider a scenario in which a store clerk is using a smartphone to interact with a customer. If the customer types, "I'm not feeling very good today," the clerk's smartphone sends this input to the server, and the emotion engine recognizes it as "sad." The generative AI model then generates a response, "That's unfortunate. What's wrong?", which is displayed on the clerk's smartphone, enabling more sympathetic customer service.

[1034] The hardware and software used include smartphones, tablets, servers, generative AI models (e.g., OpenAI's GPT-3), emotion engines (e.g., Emotion API), and HTTP communication libraries (e.g., Python requests).

[1035] An example prompt is:

[1036] User: I'm not feeling too great today

[1037] AI: That's unfortunate. What happened?

[1038] In this way, interactions with customers in physical stores become more natural and emotionally sensitive, which is expected to improve customer satisfaction.

[1039] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1040] Step 1:

[1041] A user inputs text via a smartphone or tablet, for example, "I'm not feeling very good today." This input text is sent to the device.

[1042] Step 2:

[1043] The terminal formats the received text into the specified prompt format, e.g., "User: I'm not feeling too great today." This formatted prompt is temporarily stored in the terminal.

[1044] Step 3:

[1045] The formatted prompt is sent from the terminal to the server. Specifically, it is sent as an HTTP request, and the request includes the prompt text and the API key. This request reaches the server.

[1046] Step 4:

[1047] The server receives the HTTP request sent from the device, extracts the prompt and API key from the request, and sends the extracted text to the emotion engine for sentiment analysis.

[1048] Step 5:

[1049] The emotion engine analyzes the received input text and recognizes the user's emotion. For example, the emotion "sad" is recognized. The emotion analysis result is returned to the server from the emotion engine.

[1050] Step 6:

[1051] The server sends the emotion analysis results and prompts returned by the emotion engine to the generative AI model, which uses natural language processing techniques to generate an appropriate response that takes emotion into account. For example, the generated response might be, "That's unfortunate. What's wrong?"

[1052] Step 7:

[1053] The server receives the generated response and sends it to the terminal as an HTTP response, which includes the generated response text.

[1054] Step 8:

[1055] The device receives the HTTP response from the server and displays the response text in the chat interface. For example, the response "AI: That's unfortunate. What's wrong?" is displayed on the user's device.

[1056] The above is the specific processing flow of the system for realizing an emotion-aware interactive chat platform in a physical store.

[1057] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1058] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1059] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1060] [Fourth embodiment]

[1061] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1062] 7, a 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.

[1063] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1064] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1065] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1066] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1067] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1068] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1069] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1070] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1071] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1072] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1073] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1074] The interactive chat platform of the present invention is a system that converts text entered by a user into natural-sounding responses using a generative AI model and provides the responses to the user. Specific embodiments of the present invention are described in detail below.

[1075] The system involves a user inputting text through a terminal, processing it on a server, and generating a response using a generative AI model.

[1076] First, consider the case where a user types "Hello" into a terminal. The user types text into the terminal's interface, and that text is received by the terminal. The terminal formats this user input into a specified prompt format, for example, "User: Hello."

[1077] The device then sends the formatted prompt to the server, where it uses the generative AI model's API to construct an HTTP request to send the prompt, including the user's input, to the server, including the API key for authentication.

[1078] The server receives the HTTP request sent from the device and sends a prompt to the generative AI model. The generative AI model uses natural language processing techniques to generate an appropriate response to the user input. For example, the AI ​​model can generate the response, "Hello! What would you like to talk about today?"

[1079] The generated response is received by the server, which returns the response to the terminal, which again includes the response text in an HTTP response and sends it to the terminal.

[1080] The device receives the HTTP response from the server and displays the response text in the chat interface. The user can see the response "AI: Hello! What would you like to talk about today?" through the device.

[1081] The system's functionality also includes a means to terminate the dialogue when the user inputs a specific command. For example, if the user inputs "end," the device detects this command and instructs the server to end the dialogue. This causes the server to stop making requests to the generative AI model and end the dialogue.

[1082] This system will enable users to enjoy natural and effective communication even in an aging society, and is expected to revive and revitalize existing telephone culture.

[1083] The processing flow will be explained below.

[1084] Step 1:

[1085] The user enters text into the terminal interface. Specifically, the user types "Hello" into the chat window and presses the Enter key to send it.

[1086] Step 2:

[1087] The terminal receives the user's input text. The terminal formats the input text, such as "User: Hello", and converts it into a prompt format.

[1088] Step 3:

[1089] The device sends the formatted prompt to the server as an HTTP request, which includes the API key along with the prompt.

[1090] Step 4:

[1091] The server receives the HTTP request sent from the device, extracts the prompt and API key from the request, and prepares to send them to the API for the generative AI model.

[1092] Step 5:

[1093] The server sends a prompt, specifically "User: Hello," to the generative AI model, requesting it to generate a response.

[1094] Step 6:

[1095] A generative AI model generates a response based on the prompt, for example, "Hello! What would you like to talk about today?"

[1096] Step 7:

[1097] The server receives the response from the generative AI model, formats it, and prepares it to be sent back to the device as an HTTP response.

[1098] Step 8:

[1099] The server sends an HTTP response to the device, which contains the text of the generated response.

[1100] Step 9:

[1101] The terminal receives the HTTP response from the server, extracts the response text from the response, and displays it to the user.

[1102] Step 10:

[1103] The user sees a response generated through the device, for example, "AI: Hello! What would you like to talk about today?" displayed in the chat window.

[1104] Step 11:

[1105] When the user inputs a specific end command, the terminal detects the command and instructs the server to end the conversation. Specifically, when the user inputs "end," the terminal sends the instruction to the server.

[1106] Step 12:

[1107] The server ends the conversation, which stops requests to the generative AI model and ends the conversation.

[1108] Example 1

[1109] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1110] Conventional dialogue systems face the challenge of generating intuitive and natural responses to user input. Furthermore, if proper authentication and formatting are not performed during the response generation process, the system may become unstable. Furthermore, if the user wishes to end the dialogue, the process can be cumbersome. To address these issues, a system is needed that can generate natural responses based on user input and process data efficiently and safely.

[1111] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1112] In this invention, the server includes: means for receiving text entered by a user; means for formatting the text into a specified prompt format; means for transmitting the formatted prompt to the server; means for the server to generate a response corresponding to the input text using a generative artificial intelligence model; means for receiving the response from the server and displaying it to the user; means for terminating the dialogue when the user enters a specific end command; and means for constructing an HTTP request including the formatted prompt and including an API key for authentication. This allows users to have an intuitive and natural dialogue, process data safely after appropriate authentication, and easily terminate the dialogue.

[1113] "User" refers to an individual or entity that interacts with the system.

[1114] "Input text" refers to character string data that a user sends to the dialogue system through a terminal.

[1115] A "specified prompt format" refers to the rules and formatting that are used to format text entered by a user into a particular format.

[1116] "Formatting means" refers to the hardware or software functionality for converting user-entered text into a specified prompt format.

[1117] A "server" refers to a computer system that performs a series of processes to process user requests and return generated responses.

[1118] "Generative artificial intelligence models" refer to algorithms or systems that use machine learning techniques to generate natural-looking responses.

[1119] "Response" refers to the dialogue content generated by the generative artificial intelligence model in response to a user's input.

[1120] The "means for displaying" refers to an interface or function for displaying the generated response on the terminal so that the user can check it.

[1121] An "exit command" refers to a specific string or signal that a user inputs into the system to end a dialogue.

[1122] An "HTTP request" refers to the message format of a data request sent from a client (terminal) to a server.

[1123] An "API key" is a unique string of characters used for authentication and access control when using a specific application program interface (API).

[1124] The present invention relates to an interactive chat platform that converts text entered by a user into natural-sounding responses using a generative AI model and provides the responses to the user. The present invention is implemented using specific hardware and software.

[1125] This system consists of the following main components: a terminal, a server, and a generative AI model. The role of each component is explained in detail below.

[1126] Hardware and Software Examples

[1127] Hardware: Devices used include smartphones, tablets, and PCs. Servers include cloud servers and on-premise servers.

[1128] Software: The generative AI model uses a model based on common natural language processing techniques (e.g., GPT-3), and also includes an API interface, HTTP protocol, and authentication functions.

[1129] A user first enters text into the terminal's interface. This text is received by the terminal. For example, if a user enters "Hello," the terminal formats this input into a specific prompt format, specifically, "User: Hello."

[1130] The device then sends the formatted prompt to the server, which in the process constructs an HTTP request and uses the API key for authentication. The device sends this request to the server, which then parses it.

[1131] The server sends the received prompt to the generative AI model, which generates an appropriate response to the user's input. In this case, the generative AI model uses natural language processing technology to generate a response such as, "Hello! What would you like to talk about today?"

[1132] The generated response is received by the server, which then sends it back to the device as an HTTP response. The device receives the response and displays the response text in the chat interface. The user can see the response "AI: Hello! What would you like to talk about today?" through the device.

[1133] Furthermore, if the user wants to end the dialogue, they can enter a specific end command (e.g., "Exit"). The device detects this command and instructs the server to end the dialogue. The server then stops making requests to the generative AI model and ends the dialogue.

[1134] Specific examples

[1135] Example prompt: "User: Hello."

[1136] This system allows users to enjoy intuitive and natural conversations, making effective communication possible even in an aging society.

[1137] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1138] Step 1:

[1139] The user enters text.

[1140] The user enters text into the device's interface to initiate a conversation, for example, "Hello." This text is sent to the device as is.

[1141] Input: The text entered by the user (e.g. "Hello")

[1142] Output: Text sent to the terminal

[1143] Step 2:

[1144] The terminal formats the text.

[1145] The device formats the received text into the specified prompt format, for example, "User: Hello" in order to provide input to the generative AI model in the appropriate format.

[1146] Input: The text entered by the user (e.g. "Hello")

[1147] Output: A formatted prompt (e.g. "User: Hello")

[1148] Step 3:

[1149] The terminal sends a prompt to the server.

[1150] The device composes the formatted prompt as an HTTP request, attaches an API key for authentication, and sends it to the server.

[1151] Input: A formatted prompt (e.g. "User: Hello"), API key

[1152] Output: HTTP request sent to server

[1153] Step 4:

[1154] The server sends a request to the generative AI model.

[1155] The server sends the prompt, possibly with additional configuration parameters, to the generative AI model, which receives the request and generates a natural-sounding response to the user's input.

[1156] Input: A formatted prompt (e.g. "User: Hello")

[1157] Output: The generated response (e.g., "Hello! What would you like to talk about today?")

[1158] Step 5:

[1159] The server receives the response from the generative AI model and sends it back to the device.

[1160] The server receives the generated response, composes it as an HTTP response, and sends it to the terminal.

[1161] Input: The generated response (e.g., "Hello! What would you like to talk about today?")

[1162] Output: The HTTP response is sent to the device

[1163] Step 6:

[1164] The terminal displays the response.

[1165] The terminal displays the response received from the server in the chat interface, allowing the user to check the displayed response.

[1166] Input: HTTP response from the server (e.g., "Hello! What would you like to talk about today?")

[1167] Output: The response displayed in the chat interface

[1168] Step 7:

[1169] The user ends the interaction.

[1170] When the user enters a specific command, such as "end," the device detects it and instructs the server to end the dialogue. The server then stops making requests to the generative AI model and ends the dialogue.

[1171] Input: A specific command entered by the user (e.g., "quit")

[1172] Output: Server ends conversation

[1173] (Application example 1)

[1174] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1175] In today's online shopping environment, it is difficult for users to find the products they want from the vast amount of information available. Furthermore, there are not enough support tools available for efficient shopping via mobile devices such as smartphones and head-mounted displays. This problem needs to be solved.

[1176] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1177] In this invention, the server includes means for receiving text entered by a user, means for formatting the text into a specified prompt format, means for transmitting the formatted prompt to the server, means for the server to generate a response corresponding to the input text using a generative artificial intelligence model, means for receiving the response from the server and displaying it to the user, and means for responding to questions from the user and supporting product recommendations and purchasing procedures, thereby enabling the user to search for products and complete purchasing procedures quickly and effectively through natural dialogue.

[1178] A "user" is someone who uses the system to input text and receive support.

[1179] "Text" refers to character information that a user inputs through a terminal.

[1180] "Prompt formatting" is a method for formatting user-entered text into a format suitable for a generative AI model.

[1181] The "server" is a central processing unit that processes the text entered by the user and generates a response using a generative AI model.

[1182] A "generative AI model" is an algorithm that uses natural language processing techniques to generate appropriate responses to user input.

[1183] A "response" is a response generated by a generative AI model based on user input.

[1184] A "display" is a method for visually indicating the server-generated response on the user's terminal.

[1185] "Means of responding to questions and supporting product recommendations and purchasing procedures" refers to a function in which a generative AI model recommends appropriate products in response to questions entered by the user and assists in the purchasing process.

[1186] An "exit command" is a specific keyword or phrase that indicates the user's intention to end the interaction.

[1187] This invention is a system that uses text entered by users in natural language and utilizes a generative AI model to recommend products and assist with purchasing procedures in online shopping.

[1188] System Program Overview

[1189] The system consists of the following components:

[1190] 1. User device: A device such as a smartphone or head-mounted display that allows the user to input text and receive responses.

[1191] 2. Server: The central processing unit that runs the generative AI model and processes user requests.

[1192] 3. Generative AI models: Algorithms that use natural language processing techniques to generate appropriate responses to user input (e.g., GPT-3 and GPT-4).

[1193] System operation procedure

[1194] 1. Text input:

[1195] The user types a question through the device's interface, for example, "What are the most popular smartphones?"

[1196] 2. Prompt formatting:

[1197] The device converts the text entered by the user into a specific prompt format that is easy for the generative AI model to understand, for example, "User: What are the most popular smartphones?"

[1198] 3. Send to server:

[1199] The formatted prompt is sent from the terminal to the server.

[1200] 4. Response Generation:

[1201] The server sends prompts and generates responses through the API of the generative AI model, which creates appropriate responses based on its extensive database.

[1202] 5. Receiving and displaying responses:

[1203] The server receives the generated response and sends it back to the terminal, which displays it to the user.

[1204] Specific processing flow

[1205] Consider a case where a user types, "What are the popular smartphones?" This text is formatted by the device into the format "User: What are the popular smartphones?" and sent to the server. The server uses a generative AI model to generate a response such as "Currently, the iPhone and Galaxy S series are very popular," and sends it back to the device. This response is displayed on the user's device, allowing the user to obtain appropriate product information.

[1206] The system enables users to quickly and effectively search for and purchase products through natural interactions. The hardware used is a smartphone and a head-mounted display, and the software uses generative AI model APIs (e.g., OpenAI's GPT-3 and GPT-4).

[1207] Example prompt sentence:

[1208] "User: What's the most popular smartphone?"

[1209] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1210] Step 1:

[1211] The user inputs text through the device interface. The specific text input is the question, "What are the most popular smartphones?" This input text is the first target for processing by the system.

[1212] Step 2:

[1213] The device formats the input text into a prompt that is easy for the generative AI model to understand. Specifically, the user's input, "What are the popular smartphones?", is converted to the format "User: What are the popular smartphones?". This formatted prompt is sent to the next step.

[1214] Step 3:

[1215] The formatted prompt is sent from the terminal to the server, where it is securely transmitted to the server using the HTTPS protocol, including the user session information along with the prompt.

[1216] Step 4:

[1217] The server sends the received prompt to the generative AI model. This involves constructing an API request and sending an HTTP POST request to the generative AI model's endpoint. The specific input sent to the API is "User: What are the popular smartphones?"

[1218] Step 5:

[1219] The generative AI model generates an appropriate response based on the input prompt. Here, natural language processing techniques are used to generate a text response such as, "The iPhone and Galaxy S series are very popular right now." After this response text is generated, it is returned to the server as an API response.

[1220] Step 6:

[1221] The server receives the response from the generative AI model and sends it to the user's device. Specifically, it sends the response text received as an HTTP response to the user's device. At this time, the generated response text "iPhone and Galaxy S series are currently very popular" is included as the response.

[1222] Step 7:

[1223] The device receives the response from the server and displays the response text to the user. Specifically, the text "iPhone and Galaxy S series are currently very popular" is displayed on the user's screen. This allows the user to obtain the information they were looking for.

[1224] Through the above steps, users can quickly and effectively obtain product information through natural dialogue, and enjoy a pleasant online shopping experience.

[1225] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1226] The interactive chat platform of the present invention is a system that converts text entered by a user into natural responses using a generative AI model and further combines it with an emotion engine to provide responses that take the user's emotions into consideration. Specific embodiments of the present invention are described in detail below.

[1227] The system involves a user inputting text via a terminal, processing it on a server, and generating a response using a generative AI model and an emotion engine.

[1228] First, consider the case where a user types "I'm very happy today" into a terminal. The user types text into their terminal's interface, and that text is received by the terminal. The terminal formats this user input into a specified prompt format, for example, "User: I'm very happy today."

[1229] The device then sends the formatted prompt to the server as an HTTP request, which includes the API key along with the prompt.

[1230] The server receives the HTTP request sent from the device, extracts the prompt and API key from the request, and sends the input text to the emotion engine to analyze the user's emotions.

[1231] The emotion engine recognizes emotions from the user's input text, and the emotion "joy" is identified. The analysis results are returned to the server.

[1232] The server sends the emotion engine's analysis results along with prompts to the generative AI model, which generates an appropriate response that takes emotion into account. The generative AI model uses natural language processing techniques to generate a response based on the user input and the recognized emotion. For example, the response generated might be, "That's great! Did something good happen?"

[1233] The generated response is received by the server, which returns the response to the terminal, which again includes the response text in an HTTP response and sends it to the terminal.

[1234] The device receives the HTTP response from the server and displays the response text in the chat interface. The user can see the response "AI: That's great! Did something good happen?" through the device.

[1235] The system's functionality also includes a means to terminate the dialogue when the user inputs a specific command. For example, if the user inputs "end," the device detects this command and instructs the server to end the dialogue. This causes the server to stop making requests to the generative AI model, and the dialogue ends.

[1236] This system will enable users to enjoy natural and effective communication even in an aging society, and is expected to revive and revitalize existing telephone culture. Furthermore, by combining it with an emotion engine, it will be possible to have more human-like conversations that take into account the user's emotions.

[1237] The processing flow will be explained below.

[1238] Step 1:

[1239] The user inputs text into the terminal interface. Specifically, for example, the user types "I'm very happy today" into the chat window and presses the enter key to send it.

[1240] Step 2:

[1241] The terminal receives the user's input text. The terminal formats the input text, such as "User: I'm very happy today," and converts it into a prompt format.

[1242] Step 3:

[1243] The device sends the formatted prompt to the server as an HTTP request, which includes the API key along with the prompt.

[1244] Step 4:

[1245] The server receives the HTTP request sent from the device and extracts the prompt and API key from the request.

[1246] Step 5:

[1247] The server sends prompts to the emotion engine, which analyzes the emotion from the user's input text and evaluates the emotional state.

[1248] Step 6:

[1249] The emotion engine recognizes emotions from the user's input text. Specifically, it identifies the emotion "joy" from the text "I'm so happy today."

[1250] Step 7:

[1251] The emotion engine returns the analysis results to the server, which receives the analysis results from the emotion engine and uses them for further processing.

[1252] Step 8:

[1253] The server sends the emotion engine's analysis results along with a prompt to the generative AI model, which generates a response based on the prompt and emotion information.

[1254] Step 9:

[1255] A generative AI model generates a response based on the prompt and emotional information, such as "That's great! Did something good happen?"

[1256] Step 10:

[1257] The server receives the response from the generative AI model, formats it, and prepares it to be sent back to the device as an HTTP response.

[1258] Step 11:

[1259] The server sends an HTTP response to the device, which contains the text of the generated response.

[1260] Step 12:

[1261] The device receives the HTTP response from the server, extracts the response text from the response, and displays it in the chat interface.

[1262] Step 13:

[1263] The user sees the response generated through the device, for example, "AI: That's great! What's going on?" displayed in the chat window.

[1264] Step 14:

[1265] When the user inputs a specific end command, the terminal detects the command and instructs the server to end the conversation. Specifically, when the user inputs "end," the terminal sends the instruction to the server.

[1266] Step 15:

[1267] The server ends the conversation, which stops requests to the generative AI model and ends the conversation.

[1268] Example 2

[1269] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1270] Conventional chat systems simply generate responses without considering the user's emotions, which often results in unnatural and robotic interactions. Furthermore, the end of a conversation is not clearly managed, which can lead to confusion when a user wants to end the conversation. To address these issues, a system is needed that generates natural responses that take emotions into account and clearly manages the end of a conversation.

[1271] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1272] In this invention, the server includes means for performing emotion analysis, means for taking the user's emotion into account in the generated response, and means for terminating the dialogue when the user inputs a specific end command, thereby providing a natural dialogue that takes the user's emotion into account and enabling a clear and smooth end to the dialogue.

[1273] "User-entered text" is a string of characters that a user enters into the chat interface using a terminal.

[1274] A "specified prompt format" is text entered by a user that has been converted into a specific format that is easy for a generative AI model to analyze.

[1275] A "formatted prompt" is a user-entered text that has been converted into a specified prompt format.

[1276] The "server" is the core computer system that uses generative AI models to generate responses to user input text and also performs sentiment analysis.

[1277] A "generative artificial intelligence model" is an algorithm or software that uses natural language processing techniques to generate appropriate responses based on a user's input text.

[1278] "Sentiment analysis" is the process of identifying emotions from user-entered text and analyzing those emotions.

[1279] "Display to user" means sending the generated response from the server to the terminal and making it visible to the user in the chat interface.

[1280] A "specific exit command" is a predefined string or keyword that the user enters to end the dialogue.

[1281] The "means for terminating the dialogue" is a mechanism by which the server stops requests to the generative AI model and ends the dialogue when it receives a specific termination command.

[1282] This invention relates to an interactive chat platform that converts text entered by a user into natural-sounding responses using a generative AI model and then combines it with an emotion analysis engine to provide responses that take the user's emotions into account. The following describes how to specifically implement the invention.

[1283] In this system, users input text via a terminal, which is then processed by the server, and a response is generated using a generative AI model and a sentiment analysis engine. Below, we will explain the specific equipment and software used.

[1284] Hardware and software:

[1285] Device: A device that allows a user to input text, such as a smartphone, tablet, or computer.

[1286] Server: A high-performance computing system that runs the generative AI model and sentiment analysis engine.

[1287] Generative AI models: AI platforms that use natural language processing techniques (e.g., open-source GPT-based models).

[1288] Sentiment analysis engine: Software for analyzing emotions from text (e.g., emotion recognition API).

[1289] Process flow:

[1290] 1. Receiving and formatting user input:

[1291] The user enters text into the device's chat interface. For example, they enter "I'm so happy today." This input text is formatted by the device to read "User: I'm so happy today."

[1292] 2. Sending the prompt to the server:

[1293] The formatted prompt is then sent to the server as an HTTP request, which includes the API key along with the prompt.

[1294] 3. Emotion analysis:

[1295] The server receives the HTTP request, extracts the prompt and API key, and then sends the prompt to a sentiment analysis engine to analyze the user's sentiment. The sentiment analysis engine identifies the sentiment "joy" from the text "I'm so happy today" and returns the result to the server.

[1296] 4. Generate response:

[1297] The server sends the emotion analysis results to the generative AI model, which generates an appropriate response that takes the emotion into account. The generative AI model generates a response such as "That's great! Did something good happen?" and returns the result to the server.

[1298] 5. Send and display the response:

[1299] The server sends the generated response as an HTTP response to the device. The device receives the response and displays "AI: That's great! What's going on?" in the chat interface.

[1300] 6. End the conversation (optional):

[1301] If the user wants to end the conversation, they input "end." The device notifies the server of this end command, and the server stops making requests to the generative AI model and ends the conversation.

[1302] Examples:

[1303] User input: "I'm very happy today."

[1304] Formatted prompt: "User: I'm very happy today."

[1305] Generated response: "That's great! Did something good happen?"

[1306] This system allows users to enjoy more natural and effective communication that takes emotions into account. Furthermore, even in an aging society, this system will enable conversations that are sensitive to the user's emotions, and is expected to revitalize existing means of communication.

[1307] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1308] Step 1:

[1309] Receiving User Input

[1310] The user enters text into the chat interface of the terminal.

[1311] Input: User types "I'm so happy today"

[1312] Action: The terminal receives the text entered by the user.

[1313] Output: The device receives the text "I'm so happy today."

[1314] Step 2:

[1315] Text formatting

[1316] The terminal formats the text entered by the user into the specified prompt format.

[1317] Input: User text input: "I'm so happy today"

[1318] What it does: The terminal converts the input text to the format "User: I'm so happy today."

[1319] Output: A formatted prompt: "User: I'm very happy today."

[1320] Step 3:

[1321] Sending an HTTP request

[1322] The formatted prompt is sent as an HTTP request to the server.

[1323] Input: The formatted prompt "User: I'm so happy today" and your API key

[1324] What it does: The device sends an HTTP request to the server, including a formatted prompt.

[1325] Output: HTTP request sent to server

[1326] Step 4:

[1327] Receiving and processing requests

[1328] The server receives the HTTP request and extracts the prompt and API key.

[1329] Input: HTTP request sent from the terminal

[1330] What happens: The server extracts the prompt and API key from the request body.

[1331] Output: The prompt "User: I'm so happy today" and the corresponding API key

[1332] Step 5:

[1333] Emotion analysis

[1334] The server sends the prompt to a sentiment analysis engine to analyze the user's sentiment.

[1335] Input: prompt "User: I'm so happy today"

[1336] Action: The server sends a prompt to the sentiment analysis engine to perform sentiment analysis.

[1337] Output: The sentiment analysis engine returns the result "joy."

[1338] Step 6:

[1339] Generating a response

[1340] The server sends the emotion analysis results to the generative AI model, which generates an appropriate response.

[1341] Input: prompt "User: I'm very happy today" and parsed result "joy"

[1342] How it works: The server sends prompts and sentiment analysis results to the generative AI model, which then generates a response.

[1343] Output: The generative AI model generates the response "That's great! What good happened?"

[1344] Step 7:

[1345] Sending a Response

[1346] The server sends the generated response to the terminal as an HTTP response.

[1347] Input: Generated response "That's great! Did something good happen?"

[1348] Action: The server sends this response to the device in the form of an HTTP response.

[1349] Output: The HTTP response is sent to the device

[1350] Step 8:

[1351] Viewing the response

[1352] The device receives the HTTP response from the server and displays it in the chat interface.

[1353] Input: HTTP response from the server: "That's great! What's the big deal?"

[1354] Behavior: The device displays the response in the chat interface.

[1355] Output: "AI: That's great! Did something good happen?"

[1356] Step 9:

[1357] Ending the conversation (optional)

[1358] If the user wishes to end the dialogue, he / she enters "Exit".

[1359] Input: User types "Exit"

[1360] Action: The device notifies the server of this termination command.

[1361] Output: The server stops making requests to the generative AI model and ends the conversation.

[1362] (Application example 2)

[1363] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1364] Conventional dialogue systems simply generate responses to user input, but are unable to provide responses that take the user's emotions into account. Furthermore, if the dialogue between customers and store clerks in a brick-and-mortar store is mechanical, customer satisfaction may decline. To solve these problems, a system is needed that can generate responses that take emotions into account while still allowing for natural dialogue.

[1365] The specification process by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving text entered by a user, means for formatting the text into a specified prompt format, means for transmitting the formatted prompt to the server, means for the server to generate a response corresponding to the input text using a generative artificial intelligence model, means for receiving the response from the server and displaying it to the user, emotion analysis means for recognizing the user's emotion, and means for generating a response based on the recognized emotion. This enables natural dialogue that takes the user's emotion into consideration.

[1366] The "means for receiving text input by the user" is an interface for obtaining text data input by the user through the terminal.

[1367] A "means for formatting into a specified prompt format" is a program that converts text entered by a user into a specific format and formats it as a prompt to be passed to a generative AI model.

[1368] The "means for transmitting the formatted prompt to the server" is a function for transferring the formatted prompt to the server through a communication network.

[1369] "Means for the server to use a generative AI model to generate a response corresponding to the input text" refers to a process in which the server uses a generative AI model to automatically generate a response based on the user's input text.

[1370] The "means for receiving the response from the server and displaying it to the user" is an interface for obtaining the response sent from the server and displaying the content of the response on the user's terminal.

[1371] The "emotion analysis means for recognizing user emotions" is an algorithm for analyzing emotions from text entered by the user and identifying those emotions.

[1372] The "means for generating a response based on the recognized emotion" is a program for generating a response that takes into account the emotion recognized by the emotion analysis means.

[1373] "Natural language processing technology" is a general term for technologies that allow computers to understand, analyze, and generate human language.

[1374] The "means for terminating the dialogue" is a function for terminating the dialogue session when the user inputs a specific end command.

[1375] This invention applies an interactive chat platform that provides emotion-aware responses to customer service support in brick-and-mortar stores. This system processes text entered by users via smartphones or tablets using a generative AI model and emotion engine to generate natural, emotion-aware responses.

[1376] The overall system configuration is as follows: a device such as a smartphone or tablet on which the user inputs text, a program that formats the text into a specific prompt format, a server that receives the formatted prompt and generates an appropriate response using a generative AI model and an emotion engine, and a program that displays the generated response on the user's device.

[1377] First, the user inputs text via a smartphone or tablet. For example, the user inputs a message such as "I'm not feeling so good today." This text is then formatted by the device's program into a specified prompt format, such as "User: I'm not feeling so good today."

[1378] The formatted prompt is sent to the server as an HTTP request, along with the required API key. The server receives the HTTP request and first sends the input text to an emotion engine to analyze the user's emotions. The emotion engine recognizes emotions from the text and identifies the emotion "sad," for example. The analysis results are returned to the server, which then sends them along with the prompt to a generative AI model to generate an appropriate response that takes the emotions into account.

[1379] The generative AI model uses natural language processing (NLP) technology to generate a response based on the user input and the recognized emotion. For example, a response such as "That's unfortunate. What's wrong?" is generated. This response is received by the server and sent to the device as an HTTP response. The device receives this HTTP response and displays the response in the chat interface. The user can see the response: "AI: That's unfortunate. What's wrong?"

[1380] As a concrete example, consider a scenario in which a store clerk is using a smartphone to interact with a customer. If the customer types, "I'm not feeling very good today," the clerk's smartphone sends this input to the server, and the emotion engine recognizes it as "sad." The generative AI model then generates a response, "That's unfortunate. What's wrong?", which is displayed on the clerk's smartphone, enabling more sympathetic customer service.

[1381] The hardware and software used include smartphones, tablets, servers, generative AI models (e.g., OpenAI's GPT-3), emotion engines (e.g., Emotion API), and HTTP communication libraries (e.g., Python requests).

[1382] An example prompt is:

[1383] User: I'm not feeling too great today

[1384] AI: That's unfortunate. What happened?

[1385] In this way, interactions with customers in physical stores become more natural and emotionally sensitive, which is expected to improve customer satisfaction.

[1386] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1387] Step 1:

[1388] A user inputs text via a smartphone or tablet, for example, "I'm not feeling very good today." This input text is sent to the device.

[1389] Step 2:

[1390] The terminal formats the received text into the specified prompt format, e.g., "User: I'm not feeling too great today." This formatted prompt is temporarily stored in the terminal.

[1391] Step 3:

[1392] The formatted prompt is sent from the terminal to the server. Specifically, it is sent as an HTTP request, and the request includes the prompt text and the API key. This request reaches the server.

[1393] Step 4:

[1394] The server receives the HTTP request sent from the device, extracts the prompt and API key from the request, and sends the extracted text to the emotion engine for sentiment analysis.

[1395] Step 5:

[1396] The emotion engine analyzes the received input text and recognizes the user's emotion. For example, the emotion "sad" is recognized. The emotion analysis result is returned to the server from the emotion engine.

[1397] Step 6:

[1398] The server sends the emotion analysis results and prompts returned by the emotion engine to the generative AI model, which uses natural language processing techniques to generate an appropriate response that takes emotion into account. For example, the generated response might be, "That's unfortunate. What's wrong?"

[1399] Step 7:

[1400] The server receives the generated response and sends it to the terminal as an HTTP response, which includes the generated response text.

[1401] Step 8:

[1402] The device receives the HTTP response from the server and displays the response text in the chat interface. For example, the response "AI: That's unfortunate. What's wrong?" is displayed on the user's device.

[1403] The above is the specific processing flow of the system for realizing an emotion-aware interactive chat platform in a physical store.

[1404] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1405] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1406] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1407] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1408] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1409] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1410] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1411] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1412] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1413] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1414] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1415] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1416] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1417] 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.

[1418] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1419] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1420] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific processing may be a single processor.

[1421] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1422] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1423] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1424] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1425] The following is further disclosed regarding the above embodiment.

[1426] (Claim 1)

[1427] means for receiving text input by a user;

[1428] means for formatting the text into a specified prompt format;

[1429] a means for sending the formatted prompt to a server;

[1430] means for generating a response corresponding to the input text using a generative artificial intelligence model;

[1431] means for receiving the response from the server and displaying it to the user;

[1432] A system including:

[1433] (Claim 2)

[1434] 2. The system of claim 1, wherein the generative artificial intelligence model uses natural language processing techniques to generate appropriate responses to user input.

[1435] (Claim 3)

[1436] 10. The system of claim 1, further comprising means for terminating the dialogue when the user enters a specific termination command.

[1437] "Example 1"

[1438] (Claim 1)

[1439] means for receiving text input by a user;

[1440] means for formatting the text into a specified prompt format;

[1441] a means for sending the formatted prompt to a server;

[1442] means for generating a response corresponding to the input text using a generative artificial intelligence model;

[1443] means for receiving the response from the server and displaying it to the user;

[1444] means for terminating the dialogue when the user enters a specific termination command;

[1445] A system including:

[1446] (Claim 2)

[1447] 2. The system of claim 1, further comprising: configuring an HTTP request including the formatted prompt; and including an API key for authentication.

[1448] (Claim 3)

[1449] 2. The system of claim 1, wherein the generative artificial intelligence model uses natural language processing techniques to generate appropriate responses to user input.

[1450] "Application Example 1"

[1451] (Claim 1)

[1452] means for receiving text input by a user;

[1453] means for formatting the text into a specified prompt format;

[1454] a means for sending the formatted prompt to a server;

[1455] means for generating a response corresponding to the input text using a generative artificial intelligence model;

[1456] means for receiving the response from the server and displaying it to the user;

[1457] A means of responding to user questions, recommending products, and assisting with the purchase process;

[1458] A system including:

[1459] (Claim 2)

[1460] 2. The system of claim 1, wherein the generative artificial intelligence model uses natural language processing techniques to generate appropriate responses to user input.

[1461] (Claim 3)

[1462] 10. The system of claim 1, further comprising means for terminating the dialogue when the user enters a specific termination command.

[1463] "Example 2: Combining Emotion Engines"

[1464] (Claim 1)

[1465] means for receiving text input by a user;

[1466] means for formatting the text into a specified prompt format;

[1467] a means for sending the formatted prompt to a server;

[1468] means for generating a response corresponding to the input text using a generative artificial intelligence model;

[1469] A means for the server to perform emotion analysis;

[1470] means for taking into account the user's emotions in the generated response;

[1471] means for receiving the response from the server and displaying it to the user;

[1472] A system including:

[1473] (Claim 2)

[1474] 2. The system of claim 1, wherein the generative artificial intelligence model uses natural language processing techniques to generate appropriate responses to user input and further takes into account sentiment analysis results.

[1475] (Claim 3)

[1476] 10. The system of claim 1, further comprising means for terminating the dialogue when the user enters a specific termination command.

[1477] "Application example 2 when combining emotion engines"

[1478] (Claim 1)

[1479] means for receiving text input by a user;

[1480] means for formatting the text into a specified prompt format;

[1481] a means for sending the formatted prompt to a server;

[1482] means for generating a response corresponding to the input text using a generative artificial intelligence model;

[1483] means for receiving the response from the server and displaying it to the user;

[1484] A sentiment analysis means for recognizing a user's sentiment;

[1485] means for generating a response based on the recognized emotion;

[1486] A system including:

[1487] (Claim 2)

[1488] 2. The system of claim 1, wherein the generative artificial intelligence model uses natural language processing technology to generate appropriate responses to user input, and further generates responses based on emotion recognition.

[1489] (Claim 3)

[1490] 10. The system of claim 1, further comprising means for terminating the dialogue when the user enters a specific termination command. [Explanation of symbols]

[1491] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for receiving text input by a user; means for formatting the text into a specified prompt format; a means for sending the formatted prompt to a server; means for generating a response corresponding to the input text using a generative artificial intelligence model; means for receiving the response from the server and displaying it to the user; A system including:

2. 10. The system of claim 1, wherein the generative artificial intelligence model uses natural language processing techniques to generate appropriate responses to user input.

3. 2. The system of claim 1, further comprising means for terminating the dialogue when the user enters a specific exit command.

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