system

Abstract

Provide a system. [Solution] A means for users to create an account and select a language model; A means for the device to establish communication with the server over a VoLTE or VoNR line and verify user authentication and encryption; means for receiving user text input and transmitting it to a server; means for the server to send the user's input to the language model and generate a response; means for the server to send the generated response to the terminal and for the terminal to display the received response to the user; A system including:

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] Conventional AI conversation services often suffer from problems with communication quality and response accuracy, which can impair the user experience. Furthermore, users cannot select the language model that is best suited to different applications, resulting in limited levels of response. Furthermore, systems have not been developed to reliably process user input and provide responses in real time. There is a need to resolve these issues and provide advanced AI conversation services using flexibly selectable language models in a high-quality communication environment. [Means for solving the problem]

[0005] The present invention provides a means for a user to create an account and select from multiple language models. The terminal has means for establishing communication with a server using a VoLTE or VoNR line and verifying user authentication and encryption. It includes means for receiving the user's text input and sending it to the server. The server has means for sending the user's input to the selected language model and generating a response. The server provides means for sending the generated response to the terminal, and the terminal displays the received response to the user. The present invention enables users to have a satisfying conversation experience using flexibly selectable language models in a high-quality communication environment.

[0006] A "user" is an individual who uses the system, creates an account, selects a language model, and engages in conversation with the AI.

[0007] An "account" is a data set containing individual identifying information that a user creates to use the system.

[0008] A "language model" is a set of algorithms and datasets that perform natural language processing and generate appropriate responses to input.

[0009] "Terminal" refers to electronic devices used by users, such as smartphones, tablets, and personal computers.

[0010] "VoLTE" stands for Voice over Long-Term Evolution and is a communications technology that enables high-quality voice calls.

[0011] "VoNR" stands for Voice over New Radio and is a communications technology that enables high-quality voice calls over 5G, the next-generation communications standard.

[0012] A "server" is a central computing device that processes requests from users and generates responses using an appropriate language model.

[0013] "Communication" refers to the process of sending and receiving data between a terminal and a server.

[0014] "Authentication" is the process of verifying a user's identity and granting access to a system.

[0015] "Encryption" is a technology that converts data into a format that cannot be understood by third parties in order to protect the data.

[0016] "Text input" refers to the act and data of a user entering questions or requests into a system as text.

[0017] A "response" is a reply to a user's input that is generated by the server using a language model.

[0018] "Display" refers to the act of visually presenting the response received by the terminal from the server on the user's screen.

[0019] A "conversation session" refers to a series of processes in which a user and a system continuously exchange data and engage in dialogue. [Brief explanation of the drawings]

[0020] [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

[0021] 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.

[0022] First, the terms used in the following description will be explained.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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."

[0028] [First embodiment]

[0029] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0030] 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.

[0031] 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).

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0037] 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.

[0038] 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.

[0039] 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.

[0040] 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."

[0041] ---

[0042] The following describes in detail the embodiments of the present invention. The present invention is a system that allows a user to select a language model and engage in high-quality conversation with an AI based on that selection. The user accesses the system using a terminal and establishes communication with a server via a VoLTE or VoNR line. The program processing of the present invention is performed as follows.

[0043] First, the user installs the application and creates an account. When creating the account, the user selects the language model they want to use from multiple language models (e.g., GPT-4 (registered trademark), BLOOM, LLaMA, etc.). The device sends this information to the server, which then records the selected language model in a database.

[0044] Next, the device establishes communication with the server using a VoLTE or VoNR line. The server receives this connection, verifies that the communication is encrypted, and authenticates the user, ensuring that data transmitted between the device and the server is secure.

[0045] Next, the user enters a query within the application. For example, the user might enter, "What's the weather like today?" This input data is sent in real time by the device to the server. The server analyzes this input and sends a request to the language model. The server then invokes the processing of the language model previously selected by the user (in this case, GPT-4) to generate the appropriate response.

[0046] The generated response is then sent back to the device via the server. For example, if a response such as "The current weather in Tokyo is sunny," is generated, the device receives it and displays it to the user. In this way, the user can receive high-quality responses in real time.

[0047] If the user subsequently asks an additional question, for example, "What's the weather going to be like tomorrow?", the same process is repeated. The user's input is sent to the server via the device, and the server generates a response based on the language model and sends it back to the device. The device then displays the response to the user. Through this process, the user can enjoy a continuous conversation with the AI.

[0048] As described above, the present invention is a system that provides a high-quality communication environment and flexible language model selection, thereby offering users a satisfying conversation experience. Users can select the optimal language model based on their own goals and preferences, and receive real-time responses based on that selection. In this way, the present invention solves the problems of the past and provides an improved AI conversation service.

[0049] ---

[0050] The processing flow will be explained below.

[0051] Step 1:

[0052] The user installs the application. The user launches the application and is taken to an account creation screen. The user enters the required information (name, email address, password, etc.) and creates an account. After creating the account, the user selects the language model they want to use (e.g., GPT-4, BLOOM, LLaMA, etc.). The device sends information about the selected language model to the server. The server records this information in a database.

[0053] Step 2:

[0054] The device establishes communication with the server using a VoLTE or VoNR line. The server is notified that communication has begun. The server authenticates the connected device. The server verifies that the communication is encrypted. If authentication and encryption are successful, data transmission between the device and server begins.

[0055] Step 3:

[0056] The user enters a question or request into a text box within the application (e.g., "What's the current weather?"). The device sends the entered text data to the server in real time. The server receives the user's input.

[0057] Step 4:

[0058] The server analyzes the received user input. The analysis result is sent as a request to the selected language model (e.g., GPT-4). The language model generates an appropriate response to the user input. The response generated by the language model is sent back to the server.

[0059] Step 5:

[0060] The server receives the generated response and sends it to the device. The server confirms that the response has been sent. The device analyzes the response received from the server and displays it on the user's screen (e.g., "The current weather in Tokyo is sunny.").

[0061] Step 6:

[0062] If the user wants to enter an additional question or request, the process returns to step 3. For example, the user enters "What's the weather going to be like tomorrow?" The device again sends the input data to the server, and the process repeats.

[0063] Step 7:

[0064] When a user wants to end a conversation session, they press the end button in the app. The device sends a request to end the session to the server. The server ends the session and disconnects the connection. The device then performs the termination process and notifies the user that the session has ended.

[0065] This allows users to enjoy real-time conversations with AI using the language model of their choice in a high-quality communication environment.

[0066] Example 1

[0067] 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."

[0068] Conventional AI conversation systems have issues such as limited language models that users can select and lack of guaranteed communication security. They also face challenges in generating responses to user input in real time and displaying accurate responses. There is a need to solve these issues and enable high-quality real-time conversations based on the language model selected by the user.

[0069] 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.

[0070] In this invention, the server includes means for analyzing user input data in real time and converting it into an appropriate format, means for sending the analyzed data as a request to a language model, and means for sending a generated response to the terminal, thereby enabling responses to be generated in real time based on a language model selected by the user, and providing safe and high-quality AI conversations.

[0071] "User" refers to a person who uses a terminal to access the system, create an account, select a language model, and enter text.

[0072] "Terminal" refers to a device operated by a user, such as a smartphone, PC, or tablet, that communicates with a server and has the ability to display user input and received responses.

[0073] "Communication line" refers to a network infrastructure such as the Internet or a mobile network used to transmit and receive data between a terminal and a central processing unit (server).

[0074] "Central Processing Unit" refers to a server that analyzes a user's input, generates a response based on an appropriate language model, and transmits the generated response to the terminal.

[0075] "User authentication" refers to the process of verifying a user's identity and ensuring legitimate access to a system.

[0076] "Encryption" refers to the technology of encrypting data to protect it from unauthorized access in order to transmit it safely.

[0077] "Text input" refers to strings of data that a user sends to a system using an interface on a terminal.

[0078] "Language model" refers to AI technologies used to generate appropriate responses to user text input, including GPT-4, BLOOM, and LLaMA.

[0079] "Real-time analytics" refers to the process of instantly analyzing user input data, converting it into an appropriate format, and generating a response.

[0080] A "request" refers to a data transmission that requests a language model to generate a response based on the results of analyzing the user's input data.

[0081] "Generated response" refers to text data generated by a language model based on a user's request.

[0082] The following describes in detail the mode for carrying out the present invention. This invention is a system that allows a user to select from multiple language models and engage in high-quality conversations with an AI based on that selection. The configuration and operation of the system are described below.

[0083] System configuration:

[0084] The system consists of the following elements:

[0085] 1. User device: A smartphone, PC, tablet, etc. operated by a user. An application is installed on this device.

[0086] 2. Server: A high-performance cloud server (e.g., AWS (registered trademark), Google (registered trademark) Cloud) that analyzes user input and generates responses using a language model.

[0087] 3. Communication line: Data communication between the terminal and the server is carried out using network infrastructure such as VoLTE or VoNR.

[0088] Software used:

[0089] User application: iOS or ANDROID (registered trademark) app, or web app

[0090] Language models: AI models such as GPT-4, BLOOM, and LLaMA

[0091] Communication protocol: VoLTE, VoNR

[0092] How it works:

[0093] 1. Create an account and select a model:

[0094] The user installs and launches the application. They enter the required information (email address, password, etc.) on the account creation screen. The device sends this information to the server, which records it in a database.

[0095] After creating an account, users select a language model to use from multiple models (GPT-4, BLOOM, LLaMA, etc.). The device sends the selection information to the server, which records it in a database.

[0096] 2. Communication establishment and authentication:

[0097] The device establishes communication with the server using a VoLTE or VoNR line. The server accepts the connection, verifies that the communication is encrypted, and then authenticates the user. If authentication is successful, communication continues.

[0098] 3. User input and response generation:

[0099] The user enters a query into a chat window within the application, for example, "What's the weather like now?"

[0100] The device sends this input data in real time to the server, which analyzes the input data and sends requests to the language model.

[0101] The language model (e.g., GPT-4) generates an appropriate response and returns the generated response data to the server, which then sends the response data to the device, which then displays the response to the user.

[0102] Specific working example:

[0103] For example, consider the case where a user asks a question about the weather. The user types, "What's the weather like in Tokyo tomorrow?" into the application's chat window. The device captures this input data and immediately sends it to the server. The server analyzes the input data and sends a request to a language model (such as GPT-4). The language model generates a response such as, "Tomorrow's weather in Tokyo will be sunny with occasional cloudy weather." The server then sends the generated response data to the device, which then displays the response to the user. Through this process, the user can enjoy a flexible AI conversation in real time.

[0104] In this way, the present invention is a system that enables high-quality conversation in real time based on a language model selected by the user, and solves the problems of the prior art.

[0105] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0106] Step 1: Create a user account

[0107] The user installs and launches the application. They enter the required information (email address, password, etc.) on the account creation screen. This is the input. The device sends this account information to the server. The server records the received account information in a database and sends a confirmation email to the user. This is the output.

[0108] Step 2: Language model selection

[0109] After creating an account, the user selects the language model (GPT-4, BLOOM, LLaMA, etc.) to use on the app's settings screen. This is the input. The device sends information about the selected language model to the server. The server associates the selection information with the user's account information and records it in a database. This is the output.

[0110] Step 3: Establishing communication and authenticating

[0111] The device establishes communication with the server using a VoLTE or VoNR line. This is the input. The server receives the connection and verifies that the communication is encrypted. It then authenticates the user. If authentication is successful, communication is established. This is the output.

[0112] Step 4: User input

[0113] The user types a query into a chat window within the application (e.g., "What's the weather like now?"). This is the input. The device captures the user's input data in real time. The captured data becomes the input for the next step.

[0114] Step 5: Submitting input data

[0115] The device sends the captured input data to the server. This is the input. The server prepares the received data for analysis. The input data is converted into an analyzable format. This is the output.

[0116] Step 6: Analyzing the inquiry

[0117] The server parses the input data it receives and converts it into an appropriate format. This is the input. Natural Language Processing (NLP) techniques are used in this parsing step. The parsed data becomes the input for the next step. This is the output.

[0118] Step 7: Requesting a Language Model

[0119] The server sends the parsed data as a request to the user's chosen language model, which is the input. The language model generates an appropriate response based on the input data, which is the output.

[0120] Step 8: Response Generation

[0121] The language model generates a response and returns that data to the server, which is the input. The server then prepares the received response data to send to the user's device, which is the output.

[0122] Step 9: Send and view the response

[0123] The server sends the response data to the terminal. This is the input. The terminal displays the received response data to the user. This is the output. The user can check the displayed response and enter the next query (e.g., "What's the weather like tomorrow?"), which will cause the same process to start again from step 4.

[0124] (Application example 1)

[0125] 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."

[0126] In today's electronic payment services, users lack the means to quickly and accurately obtain information about new payment methods and questions. This often leaves users feeling anxious and frustrated when making payments, resulting in a poor customer experience. The present invention aims to solve these user issues and provide a smoother and more satisfying electronic payment experience.

[0127] 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.

[0128] In this invention, the server includes means for a user to create an account and select a language model, means for a terminal to establish communication using a VoLTE or VoNR line and confirm user authentication and encryption, means for transmitting the user's text input to the server in real time, means for generating appropriate responses based on the language model to the user's questions regarding payment methods and electronic payments, and means for transmitting these responses from the server to the terminal and displaying the responses received by the terminal to the user, thereby enabling the user to obtain high-quality information regarding electronic payments in real time.

[0129] "Means for creating an account" refers to the function that allows a user to register with the system and set individual user information.

[0130] "Means for selecting a language model" is a function that allows users to select the model to use from multiple AI language models.

[0131] "Means for the terminal to establish communication with the server using a VoLTE or VoNR line" refers to the ability of the user's device to establish a connection with the server using communications technology that enables high-quality voice and data communications.

[0132] "User authentication and encryption verification measures" are procedures used to verify the legitimacy of users and ensure data privacy and security.

[0133] "Means for receiving text input from the user and sending it to the server" is a function for obtaining text data input by the user into the terminal and sending it to the server.

[0134] The "means for the server to send the user's input to the language model and generate a response" is the function by which the server sends the text received from the user to the selected language model and generates an appropriate answer.

[0135] The "means for the server to send the generated response to the terminal and for the terminal to display the received response to the user" is a function for sending a text response generated by the server to the user's terminal and displaying the response on the terminal.

[0136] "Means for generating appropriate responses in real time to user questions about payment methods and electronic payments" is a function for providing immediate and appropriate answers to user questions about electronic payments.

[0137] The present invention is a system that allows users to obtain information in real time about questions about electronic payments and new payment methods. A specific embodiment of this system will be described below.

[0138] The system allows users to create an account using a device such as a smartphone or smart glasses, and then select from multiple language models. The selected language model is then used to generate high-quality responses for electronic payments in real time.

[0139] Hardware and software used

[0140] Devices: Smartphones (iOS, Android), smart glasses (e.g., Google Glass (registered trademark))

[0141] Communication infrastructure: VoLTE, VoNR lines

[0142] Server: Cloud server (e.g. AWS, Google Cloud)

[0143] Language Model API: Generative AI models (e.g., OpenAI's GPT-4, BLOOM, Meta's LLaMA)

[0144] Specific details of data processing and data calculation

[0145] 1. Create an account and select a model:

[0146] A user installs the application and creates an account. At that time, they select the language model they want to use from multiple models (GPT-4, BLOOM, LLaMA, etc.). This information is sent from the device to the server and recorded in a database on the server.

[0147] 2. Communication establishment and encryption:

[0148] The device establishes communication with the server using a VoLTE or VoNR line. The server then verifies that the communication is encrypted and authenticates the user. This communication method maintains high security and privacy.

[0149] 3. User input and response generation:

[0150] A user types a question about electronic payments within an application, such as "What points can I use to pay at this store?" This text data is sent in real time by the device to a server. The server analyzes the input and sends a request to a language model previously selected by the user. The language model generates an appropriate response and returns it to the server.

[0151] 4. View the response:

[0152] The server generates a response, which is sent to the terminal and displayed to the user, for example, "This store accepts points for payment." If the user then asks a follow-up question, for example, "What are some other payment methods?", the process is repeated.

[0153] Examples of prompt statements

[0154] Here are some example prompts that can be used when a user enters a question about an electronic payment:

[0155] User input: "What is your most recent payment method?"

[0156] GPT-4 prompt: “User wants the latest information on electronic payments. In particular, please provide details about new payment methods and their availability in stores.”

[0157] In this way, the system allows users to get real-time answers to questions and information about electronic payments instantly, providing a smoother and more satisfying experience.

[0158] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0159] Step 1:

[0160] A user creates an account and selects a language model. Input includes a username, password, and the selected language model (e.g., GPT-4, BLOOM, LLaMA). The device collects this information and sends it to the server, which records it in a database.

[0161] Step 2:

[0162] The device establishes communication with the server using a VoLTE or VoNR line. Input includes the user's device information and authentication information. The server confirms that communication has been established and that the transmitted data is encrypted. The user's authentication information is verified to ensure the security of the communication.

[0163] Step 3:

[0164] The user inputs a question about electronic payment within the application. For example, a question such as "How many points can I use to pay at this store?" This text data is collected by the terminal and sent to the server in real time. The input includes the user's text data.

[0165] Step 4:

[0166] The server analyzes the received text data, generates prompts against a pre-selected language model, and sends the request. The input includes the user's question and the selected language model. The language model (e.g., GPT-4) generates an appropriate response based on the request. The output is the appropriate response.

[0167] Step 5:

[0168] The server receives the response generated from the language model and sends it to the device. The input includes the generated response. The output includes the data to be sent to the device, allowing the user to receive the response on the device.

[0169] Step 6:

[0170] The terminal displays the response received from the server to the user. The response data sent from the server is included as input. Specifically, a response such as "Points can be used for payment at this store" is displayed. The user can check this information on the screen.

[0171] Step 7:

[0172] If the user subsequently asks a question (e.g., "What are the alternative payment methods?"), the process repeats, this time with the user's new question. The device again collects text data and sends it in real time to the server, where it is parsed, a language model generates a response, and the response is sent to the device for display.

[0173] The above is the specific processing flow of the program. This system allows users to receive high-quality responses in real time to questions about electronic payments.

[0174] 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.

[0175] ---

[0176] The present invention provides an AI conversation system incorporating an emotion engine that recognizes user emotions. The system includes means for a user to create an account and select a language model, means for establishing communication with a server using a VoLTE or VoNR line and verifying user authentication and encryption, means for receiving the user's text input and sending it to the server, means for the server to send the user's input to the language model and generate a response, means for the server to send the generated response to a terminal and means for the terminal to display the received response to the user, and means for recognizing the user's emotions using the emotion engine and generating or modifying an appropriate response based on the user's emotions.

[0177] First, the user installs a dedicated application and creates an account. After installation, the user is taken to an account creation screen and enters the necessary personal information to create the account. Next, the user selects the language model (e.g., GPT-4, BLOOM, LLaMA, etc.) they wish to use when creating the account or later. This information is sent from the device to the server, and the server records the selected language model in a database.

[0178] The device establishes communication with the server using a VoLTE or VoNR connection. Once communication is established, the server authenticates and authorizes the connected device. The server then verifies that the communication is encrypted. Once this verification is complete, secure data transmission between the device and the server begins.

[0179] Users input questions or requests into a text box within the app. For example, if a user types "How are you feeling today?", the device sends this data to the server in real time. At the same time, the emotion engine analyzes the user's input and recognizes the user's emotional state (e.g., joy, sadness, anger, etc.). The emotional information recognized by the emotion engine is also sent to the server.

[0180] The server analyzes the received user input and emotional information and sends a request to the selected language model. The language model generates a more appropriate response based on the user input and emotional information. For example, the response generated might be, "You look great today, have a great day!" This response is then sent to the device via the server.

[0181] The device analyzes the response received from the server and displays it on the user's screen, allowing the user to receive a response that reflects their emotional state. If the user wants to ask a follow-up question, such as "What are your plans for tomorrow?", the same process is repeated.

[0182] When a user wants to end a conversation session, they press the end button in the app. The device sends a request to end the session to the server, and the server ends the session and disconnects the communication. The device then performs the termination process and notifies the user that the session has ended.

[0183] In this way, by incorporating an emotion engine, the present invention is a system that can provide more appropriate and high-quality responses according to the user's emotional state, thereby resolving conventional problems and providing users with a satisfying conversation experience.

[0184] ---

[0185] The processing flow will be explained below.

[0186] Step 1:

[0187] The user installs the dedicated application. The user launches the application and moves to the account creation screen. The user enters the required information such as name, email address, and password to create an account.

[0188] Step 2:

[0189] After creating an account, the user selects the language model they want to use (e.g., GPT-4, BLOOM, LLaMA, etc.). The device sends information about the selected language model to the server, which then records this information in a database.

[0190] Step 3:

[0191] The device establishes communication with the server using a VoLTE or VoNR line and notifies the server that communication has begun.

[0192] Step 4:

[0193] The server authenticates the connected device. The server verifies that the communication is encrypted. Once authentication and encryption are successful, data transmission between the device and the server begins.

[0194] Step 5:

[0195] The user types a question or request into a text box within the application (e.g., "How are you feeling today?"). The device sends the text data in real time to the server. In parallel, the device analyzes the input and runs an emotion engine to recognize the user's emotions.

[0196] Step 6:

[0197] The device sends the emotional information analyzed by the emotion engine to the server. The emotion engine identifies the user's emotion (e.g., joy, sadness, anger, etc.). This emotional information is also sent to the server.

[0198] Step 7:

[0199] The server receives and analyzes the user's input and emotion information. The server sends a request to the selected language model. The language model generates a response based on the user's input and emotion information.

[0200] Step 8:

[0201] The server receives the response generated from the language model and sends it to the terminal. The server then confirms that the response has been sent.

[0202] Step 9:

[0203] The device parses the response received from the server and displays it on the user's screen. For example, if the user asks, "How are you feeling today?", the language model generates a response such as, "You look great today, have a great day!", which the device displays.

[0204] Step 10:

[0205] If the user wants to enter an additional question or request, the process returns to step 5. For example, the user enters, "What are your plans for tomorrow?" The device again sends the input data and emotion information to the server, and the same process is repeated.

[0206] Step 11:

[0207] When a user wants to end a conversation session, they press the end button in the app. The device sends a request to end the session to the server. The server ends the session and disconnects the connection. The device then performs the termination process and notifies the user that the session has ended.

[0208] In this way, incorporating an emotion engine allows the system to provide more appropriate and higher quality responses based on the user's emotional state, resulting in a more satisfying conversational experience for the user.

[0209] Example 2

[0210] 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."

[0211] Conventional AI conversation systems generate uniform responses without considering the user's emotions, making it difficult to improve the individual user experience. For this reason, there is a need for a system that can provide more natural and satisfying conversations that respond to the user's emotions.

[0212] 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.

[0213] In this invention, the server includes means for a user to create an account and select a language model, means for a terminal to establish communication with the server using a mobile communication environment and confirm user authentication and encryption, means for receiving a text input from the user and sending it to the server, means for the server to send the user's input to the language model and generate a response, means for the server to send the generated response to the terminal and display the response received by the terminal to the user, means for recognizing the user's emotional state using an emotion engine and sending that information to the server, and means for the server to modify and generate a response based on the emotion information, thereby making it possible to provide a more appropriate and satisfying response that corresponds to the user's emotions.

[0214] "Account" means the personal registration information required for a User to access the System.

[0215] A "language model" is an artificial intelligence-based algorithm or program for generating responses based on a user's text input.

[0216] A "mobile communication environment" is a network that uses wireless communication technologies such as VoLTE and VoNR.

[0217] "User authentication" is the process by which a system verifies a user's identity.

[0218] "Encryption" is the technique of converting plaintext into ciphertext in order to protect data.

[0219] "Text input" refers to the act of a user inputting text into a system.

[0220] An "emotion engine" is a system that analyzes and recognizes the emotional state from the user's input.

[0221] A "response" is a reply message generated in response to a user's text input.

[0222] A "server" is a computer system or network that centrally manages data processing and storage.

[0223] "Terminal" refers to the device used by the user (smartphone, PC, etc.).

[0224] The present invention provides an AI conversation system incorporating an emotion engine that recognizes user emotions. The system includes means for a user to create an account and select a language model, means for establishing communication with a server using a mobile communication environment and confirming user authentication and encryption, means for receiving the user's text input and sending it to the server, means for the server to send the user's input to the language model and generate a response, means for the server to send the generated response to a terminal and means for the terminal to display the received response to the user, and means for recognizing the user's emotions using the emotion engine and generating and modifying an appropriate response based on the user's emotions.

[0225] Hardware and Software Configuration

[0226] An implementation of this system includes the following hardware and software:

[0227] Device: Smartphone, PC, or other device

[0228] Server: A computer system that centrally manages data processing and storage.

[0229] Mobile communication environment: Wireless communication technologies such as VoLTE and VoNR

[0230] Dedicated applications: User-installed apps

[0231] Language models: Artificial intelligence-based algorithms such as GPT-4, BLOOM, and LLaMA

[0232] Emotion Engine: A system that analyzes the emotional state from user input

[0233] System Operation

[0234] First, the user installs a dedicated application and creates an account. After installation, the user is taken to an account creation screen and enters the necessary personal information (name, email address, password, etc.) to create an account. Next, the user selects the language model they want to use (e.g., GPT-4, BLOOM, LLaMA, etc.). This information is sent from the device to the server, and the server records the selected language model in a database.

[0235] The device establishes communication with the server using a VoLTE or VoNR line. Once communication is established, the server authenticates the connected device and verifies encrypted communication. After this verification is complete, secure data transmission between the device and the server begins.

[0236] Users input questions or requests into a text box within the app. For example, if they type "How are you feeling today?", the device sends this data to the server in real time. At the same time, the emotion engine analyzes the user's input and recognizes the user's emotional state (e.g., joy, sadness, anger, etc.). The analyzed emotion information is also sent from the device to the server.

[0237] The server analyzes the received user input and emotional information and sends a request to the selected language model. The language model generates a more appropriate response based on the user input and emotional information. For example, the response generated might be, "You look great today, have a great day!" This response is then sent to the device via the server.

[0238] The device analyzes the response received from the server and displays it on the user's screen, allowing the user to receive a response that reflects their emotional state. If the user has additional questions, for example, "What are your plans for tomorrow?", the same process is repeated.

[0239] When a user wants to end a conversation session, they press the end button in the app, and the device sends a session termination request to the server. The server ends the session and disconnects the connection. The device then performs the termination process and notifies the user of the end of the session.

[0240] In this way, the present invention provides a system that can provide more appropriate and high-quality responses according to the user's emotional state, thereby resolving the problems of the past and providing the user with a satisfying conversation experience.

[0241] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0242] Step 1:

[0243] Installing the application and creating an account

[0244] 1. User installs the application:

[0245] Input: The user downloads a dedicated application onto their smartphone or PC.

[0246] How it works: The application is downloaded over the internet and installed on your device.

[0247] Output: The application is installed on the device and its icon is displayed.

[0248] 2. User creates an account:

[0249] Input: A user enters personal information into an application, such as their name, email address, or password.

[0250] How it works: The information you enter is encrypted on the device.

[0251] Output: Encrypted personal information is generated.

[0252] 3. The device sends the information to the server:

[0253] Input: Encrypted personal information.

[0254] Operation: The terminal uses a communication line to send data to the server.

[0255] Output: Personal information received on the server side.

[0256] 4. The server creates the account:

[0257] Input: Encrypted personal information received by the server.

[0258] How it works: The server records the information in a database and creates an account for the user.

[0259] Output: The account is registered in the database.

[0260] Step 2:

[0261] Language model selection

[0262] 1. The user selects a language model:

[0263] Input: The user selects the language model they want to use (GPT-4, BLOOM, LLaMA, etc.).

[0264] What it does: The application retrieves the ID of the selected model.

[0265] Output: The selected language model ID is generated.

[0266] 2. The device sends the model selection information to the server:

[0267] Input: The selected language model ID.

[0268] Operation: The terminal sends model selection information to the server.

[0269] Output: Model selection information received on the server.

[0270] 3. The server records the selection:

[0271] Input: The language model ID received by the server.

[0272] Operation: The server records the model selection information in a database.

[0273] Output: Model selection information is persisted in a database.

[0274] Step 3:

[0275] Establishing communication

[0276] 1. The device establishes communication with the server:

[0277] Input: The terminal sends a request to start communication.

[0278] Operation: The device starts communication using a VoLTE or VoNR line.

[0279] Output: The request arrives at the server.

[0280] 2. The server authenticates the device and verifies the encrypted communication:

[0281] Input: A communication connection request from the device.

[0282] How it works: The server verifies the device's identity and checks the status of the encrypted communication.

[0283] Output: Communication is authenticated and cryptographic verification is complete.

[0284] 3. Initiating secure data transmission:

[0285] Input: An authenticated and encrypted communications connection.

[0286] How it works: Secure data transmission begins between the device and the server.

[0287] Output: Ready for data transmission.

[0288] Step 4:

[0289] User Input and Emotion Recognition

[0290] 1. User enters text:

[0291] Input: The user types a question or request into a text box.

[0292] What it does: Enter a prompt sentence into the text box, such as "How are you feeling today?"

[0293] Output: The input text data is generated.

[0294] 2. The device sends the text to the server:

[0295] Input: The text data entered.

[0296] How it works: The device sends text data to the server in real time.

[0297] Output: Text data arrives at the server.

[0298] 3. The emotion engine analyzes the text and recognizes emotions:

[0299] Input: User's text data.

[0300] How it works: The emotion engine analyzes the text and recognizes the user's emotional state (happy, sad, angry, etc.).

[0301] Output: Emotional information is generated.

[0302] 4. The device sends the emotion information to the server:

[0303] Input: Emotion information.

[0304] Operation: The device sends emotion information to the server.

[0305] Output: Emotion information arrives at the server.

[0306] Step 5:

[0307] Response Generation

[0308] 1. The server analyzes the user's input and emotional information:

[0309] Input: User's text data and emotion information.

[0310] How it works: The server parses both pieces of data.

[0311] Output: The analysis results are generated.

[0312] 2. The server sends a request to the language model:

[0313] Input: Analysis results.

[0314] How it works: The server sends the appropriate request to the language model.

[0315] Output: The request arrives at the language model.

[0316] 3. The language model generates a response:

[0317] Input: The request sent to the language model.

[0318] How it works: The language model generates a response based on the analysis.

[0319] Output: A response such as "You look great today, have a great day!" is generated.

[0320] 4. The server generates a response and sends it to the device:

[0321] Input: The generated response.

[0322] Action: The server sends a response to the device.

[0323] Output: The response arrives on the terminal.

[0324] Step 6:

[0325] Viewing the response

[0326] 1. Parse the response received by the device:

[0327] Input: The response sent by the server.

[0328] Action: The device analyzes the response data.

[0329] Output: The parsed response data is generated.

[0330] 2. The terminal displays the response to the user:

[0331] Input: Parsed response data.

[0332] Behavior: Displays a response message on the screen.

[0333] Output: The user will be able to see the response on the screen.

[0334] Step 7:

[0335] Session End

[0336] 1. User ends conversation session:

[0337] Input: The user presses the exit button.

[0338] Behavior: A click event on the Exit button occurs.

[0339] Output: A session termination request is generated.

[0340] 2. The device sends a session termination request to the server:

[0341] Input: Session termination request.

[0342] Operation: The device sends a request to the server.

[0343] Output: The request arrives at the server.

[0344] 3. The server ends the session and disconnects:

[0345] Input: Session termination request.

[0346] Action: The server ends the session and disconnects.

[0347] Output: The connection is dropped and the session is terminated.

[0348] 4. The terminal shuts down and notifies the user:

[0349] Input: Communication is lost.

[0350] Behavior: The application performs termination processing and notifies the user of the end of the session.

[0351] Output: The user can confirm the session has ended.

[0352] (Application example 2)

[0353] 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."

[0354] Conventional food delivery systems have difficulty providing customized services based on a user's emotional state. Furthermore, when a user is feeling stressed or tired, they are unable to appropriately suggest meals or special offers that correspond to that state. As a result, users' satisfaction declines and they tend to use the service less frequently. Therefore, the present invention aims to build a system that incorporates user emotion recognition to provide a customized food delivery service that corresponds to the user's emotional state, thereby improving user satisfaction.

[0355] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0356] means for recognizing a user's emotion and generating and modifying an appropriate response based on the recognized emotion;

[0357] A means for suggesting additional services or products customized to the user based on the perceived emotions;

[0358] A means for users to review the proposed services and products and confirm their orders;

[0359] This makes it possible to propose appropriate services and products according to the user's emotional state.

[0360] Definitions of important words

[0361] An "account" is personal identification information created by a user to use the system.

[0362] A "language model" is an algorithm or program used in natural language processing to generate appropriate responses based on user input.

[0363] "Wireless communication line" refers to a data transmission means using wireless communication technology such as VoLTE or VoNR.

[0364] A "server" is a computer system responsible for receiving and analyzing user data, and generating and sending an appropriate response.

[0365] An "emotion engine" is an algorithm or software that analyzes and recognizes emotions from a user's text or voice input.

[0366] A "response" is a response or reaction generated in response to user input.

[0367] "Customized additional services and products" are services and products that are specifically suggested taking into account the user's emotional state.

[0368] "Confirming an order" means that the user has made clear their intention to purchase the proposed service or product, and the transaction is now complete.

[0369] "User authentication" is a procedure for verifying the legitimacy of a user accessing a system.

[0370] "Encryption" is a data conversion technology for securely transmitting communication data.

[0371] "Text input" refers to a string of characters that a user enters to communicate or give instructions to a system.

[0372] Description: Detailed Description of the Invention

[0373] This invention is a system for recognizing user emotions in a food delivery service and providing customized services to users based on that data. Specific implementation methods for realizing this system will be described.

[0374] Hardware and software used

[0375] Hardware: Smartphones, servers

[0376] Software: EmotionRecognitionAPI, FoodDeliveryAPI

[0377] Overall system overview

[0378] First, the user installs a dedicated application on their smartphone and creates an account. After launching the application, the user enters the necessary personal information and creates an account. When creating the account or later, the user selects the language model (for example, a general artificial intelligence model) they wish to use. This information is sent from the smartphone to the server, and the selected language model is recorded in the server's database.

[0379] Establishment and authentication of communication over VoLTE / VoNR lines

[0380] The smartphone establishes communication with the server using a VoLTE or VoNR connection. After communication is established, the server authenticates and authorizes the smartphone. It also ensures that the communication is encrypted. Once this is complete, secure data transmission begins.

[0381] User Input and Emotion Recognition

[0382] Users enter questions or requests into a text box within the app. For example, if a user types "I'm very tired today," the smartphone sends this data to the server in real time. At this time, the EmotionRecognition API analyzes the entered text and recognizes the user's emotional state (e.g., fatigue). The emotional information recognized by the emotion engine is also sent to the server.

[0383] Server response generation

[0384] The server analyzes the received user input and emotional information and sends a request to the selected language model. The language model generates an appropriate response based on the user input and emotional information. For example, the response generated might be, "Would you like some relaxing herbal tea and a light snack?" This response is then sent via the server to the smartphone.

[0385] Response display and order confirmation

[0386] The smartphone analyzes the response received from the server and displays it on the user's screen. The user checks the proposed services and products and decides whether to place an order. If the order is confirmed, the smartphone sends the order data to the server, which processes the order.

[0387] Specific prompt examples

[0388] "I'm very tired today"

[0389] "Is there a relaxation menu you would recommend?"

[0390] This will realize a system that provides food delivery services according to the user's emotional state. Users can receive services that match their emotional state, which is expected to improve their satisfaction.

[0391] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0392] Program processing steps

[0393] Step 1

[0394] User account creation

[0395] Input: User's personal information (name, contact details, etc.)

[0396] How it works: The device captures the personal information entered by the user to create an account.

[0397] Output: An account creation request is generated and sent to the server.

[0398] Data processing: Formatting user information and converting it into a data structure to send to the server.

[0399] Step 2

[0400] Language model selection

[0401] Input: Selection information for the language model you want to use (e.g., a general-purpose artificial intelligence model)

[0402] Operation: The device obtains information about the language model selected by the user.

[0403] Output: A language model selection request is sent to the server and the selection information is recorded in a database.

[0404] Data processing: The selected language model information is formatted and converted into a data structure for transmission to the server.

[0405] Step 3

[0406] Establishing communication

[0407] Input: Device connection information

[0408] Operation: The terminal starts communication with the server using a VoLTE or VoNR line.

[0409] Output: The server receives the connection request and establishes communication.

[0410] Data Computing: Encrypts and authenticates connections to ensure communications are secure.

[0411] Step 4

[0412] Accepting text input

[0413] Input: The text of the user's question or request (e.g., "I'm very tired today")

[0414] Action: The terminal captures the text entered by the user.

[0415] Output: The text input data is sent to the server.

[0416] Data processing: Converting user input into a data structure to be sent to the server in real time.

[0417] Step 5

[0418] Emotion Recognition and Analysis

[0419] Input: User text input data

[0420] How it works: The server uses the EmotionRecognition API to analyze the text and recognize the user's emotions.

[0421] Output: The recognized emotion information is generated and included in the request to the language model.

[0422] Data Computation: Perform text analysis and emotion recognition to extract emotional information.

[0423] Step 6

[0424] Generating a response

[0425] Input: User text input and emotional information

[0426] Operation: The server sends a request to the selected language model and generates an appropriate response.

[0427] Output: The generated response is sent to the terminal.

[0428] Data Computation: Uses language models to generate appropriate responses based on input data.

[0429] Step 7

[0430] Viewing the response

[0431] Input: Response data received from the server

[0432] Operation: The device analyzes the received response data and displays it on the user's screen.

[0433] Output: The user is shown an appropriate response.

[0434] Data processing: Converting response data into a format that can be displayed.

[0435] Step 8

[0436] Proposals for services and products

[0437] Input: Recognized emotion information and generated response

[0438] How it works: The server suggests customized services and products based on the user's emotional state.

[0439] Output: The proposed data is sent to the device.

[0440] Data calculation: Generates suggestion data based on emotional information and makes optimal suggestions to users.

[0441] Step 9

[0442] Confirming the order

[0443] Input: User confirmation and order intention

[0444] How it works: The terminal allows the user to review the proposed services and products and place the order.

[0445] Output: The order confirmation data is sent to the server and the transaction is completed.

[0446] Data processing: Order data is formatted and sent to the server for order processing.

[0447] Examples of prompt statements

[0448] "I'm very tired today"

[0449] "Is there a relaxation menu you would recommend?"

[0450] This enables system-wide processing, allowing users to enjoy a food delivery service that is customized according to their emotions.

[0451] 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.

[0452] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (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.

[0453] 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.

[0454] [Second embodiment]

[0455] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0456] 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.

[0457] 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).

[0458] 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.

[0459] 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.

[0460] 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).

[0461] 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. 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.

[0462] 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.

[0463] 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.

[0464] 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.

[0465] 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.

[0466] 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."

[0467] ---

[0468] The following describes in detail the embodiments of the present invention. The present invention is a system that allows a user to select a language model and engage in high-quality conversation with an AI based on that selection. The user accesses the system using a terminal and establishes communication with a server via a VoLTE or VoNR line. The program processing of the present invention is performed as follows.

[0469] First, the user installs the application and creates an account. When creating an account, the user selects the language model they want to use from multiple language models (e.g., GPT-4, BLOOM, LLaMA, etc.). The device sends this information to the server, which then records the selected language model in a database.

[0470] Next, the device establishes communication with the server using a VoLTE or VoNR line. The server receives this connection, verifies that the communication is encrypted, and authenticates the user, ensuring that data transmitted between the device and the server is secure.

[0471] Next, the user enters a query within the application. For example, the user might enter, "What's the weather like today?" This input data is sent in real time by the device to the server. The server analyzes this input and sends a request to the language model. The server then invokes the processing of the language model previously selected by the user (in this case, GPT-4) to generate the appropriate response.

[0472] The generated response is then sent back to the device via the server. For example, if a response such as "The current weather in Tokyo is sunny," is generated, the device receives it and displays it to the user. In this way, the user can receive high-quality responses in real time.

[0473] If the user subsequently asks an additional question, for example, "What's the weather going to be like tomorrow?", the same process is repeated. The user's input is sent to the server via the device, and the server generates a response based on the language model and sends it back to the device. The device then displays the response to the user. Through this process, the user can enjoy a continuous conversation with the AI.

[0474] As described above, the present invention is a system that provides a high-quality communication environment and flexible language model selection, thereby offering users a satisfying conversation experience. Users can select the optimal language model based on their own goals and preferences, and receive real-time responses based on that selection. In this way, the present invention solves the problems of the past and provides an improved AI conversation service.

[0475] ---

[0476] The processing flow will be explained below.

[0477] Step 1:

[0478] The user installs the application. The user launches the application and is taken to an account creation screen. The user enters the required information (name, email address, password, etc.) and creates an account. After creating the account, the user selects the language model they want to use (e.g., GPT-4, BLOOM, LLaMA, etc.). The device sends information about the selected language model to the server. The server records this information in a database.

[0479] Step 2:

[0480] The device establishes communication with the server using a VoLTE or VoNR line. The server is notified that communication has begun. The server authenticates the connected device. The server verifies that the communication is encrypted. If authentication and encryption are successful, data transmission between the device and server begins.

[0481] Step 3:

[0482] The user enters a question or request into a text box within the application (e.g., "What's the current weather?"). The device sends the entered text data to the server in real time. The server receives the user's input.

[0483] Step 4:

[0484] The server analyzes the received user input. The analysis result is sent as a request to the selected language model (e.g., GPT-4). The language model generates an appropriate response to the user input. The response generated by the language model is sent back to the server.

[0485] Step 5:

[0486] The server receives the generated response and sends it to the device. The server confirms that the response has been sent. The device analyzes the response received from the server and displays it on the user's screen (e.g., "The current weather in Tokyo is sunny.").

[0487] Step 6:

[0488] If the user wants to enter an additional question or request, the process returns to step 3. For example, the user enters "What's the weather going to be like tomorrow?" The device again sends the input data to the server, and the process repeats.

[0489] Step 7:

[0490] When a user wants to end a conversation session, they press the end button in the app. The device sends a request to end the session to the server. The server ends the session and disconnects the connection. The device then performs the termination process and notifies the user that the session has ended.

[0491] This allows users to enjoy real-time conversations with AI using the language model of their choice in a high-quality communication environment.

[0492] Example 1

[0493] 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."

[0494] Conventional AI conversation systems have issues such as limited language models that users can select and lack of guaranteed communication security. They also face challenges in generating responses to user input in real time and displaying accurate responses. There is a need to solve these issues and enable high-quality real-time conversations based on the language model selected by the user.

[0495] 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.

[0496] In this invention, the server includes means for analyzing user input data in real time and converting it into an appropriate format, means for sending the analyzed data as a request to a language model, and means for sending a generated response to the terminal, thereby enabling responses to be generated in real time based on a language model selected by the user, and providing safe and high-quality AI conversations.

[0497] "User" refers to a person who uses a terminal to access the system, create an account, select a language model, and enter text.

[0498] "Terminal" refers to a device operated by a user, such as a smartphone, PC, or tablet, that communicates with a server and has the ability to display user input and received responses.

[0499] "Communication line" refers to a network infrastructure such as the Internet or a mobile network used to transmit and receive data between a terminal and a central processing unit (server).

[0500] "Central Processing Unit" refers to a server that analyzes a user's input, generates a response based on an appropriate language model, and transmits the generated response to the terminal.

[0501] "User authentication" refers to the process of verifying a user's identity and ensuring legitimate access to a system.

[0502] "Encryption" refers to the technology of encrypting data to protect it from unauthorized access in order to transmit it safely.

[0503] "Text input" refers to strings of data that a user sends to a system using an interface on a terminal.

[0504] "Language model" refers to AI technologies used to generate appropriate responses to user text input, including GPT-4, BLOOM, and LLaMA.

[0505] "Real-time analytics" refers to the process of instantly analyzing user input data, converting it into an appropriate format, and generating a response.

[0506] A "request" refers to a data transmission that requests a language model to generate a response based on the results of analyzing the user's input data.

[0507] "Generated response" refers to text data generated by a language model based on a user's request.

[0508] The following describes in detail the mode for carrying out the present invention. This invention is a system that allows a user to select from multiple language models and engage in high-quality conversations with an AI based on that selection. The configuration and operation of the system are described below.

[0509] System configuration:

[0510] The system consists of the following elements:

[0511] 1. User device: A smartphone, PC, tablet, etc. operated by a user. An application is installed on this device.

[0512] 2. Server: A high-performance cloud server (e.g., AWS, Google Cloud) that analyzes user input and generates responses using a language model.

[0513] 3. Communication line: Data communication between the terminal and the server is carried out using network infrastructure such as VoLTE or VoNR.

[0514] Software used:

[0515] User application: iOS or Android app, or web app

[0516] Language models: AI models such as GPT-4, BLOOM, and LLaMA

[0517] Communication protocol: VoLTE, VoNR

[0518] How it works:

[0519] 1. Create an account and select a model:

[0520] The user installs and launches the application. They enter the required information (email address, password, etc.) on the account creation screen. The device sends this information to the server, which records it in a database.

[0521] After creating an account, users select a language model to use from multiple models (GPT-4, BLOOM, LLaMA, etc.). The device sends the selection information to the server, which records it in a database.

[0522] 2. Communication establishment and authentication:

[0523] The device establishes communication with the server using a VoLTE or VoNR line. The server accepts the connection, verifies that the communication is encrypted, and then authenticates the user. If authentication is successful, communication continues.

[0524] 3. User input and response generation:

[0525] The user enters a query into a chat window within the application, for example, "What's the weather like now?"

[0526] The device sends this input data in real time to the server, which analyzes the input data and sends requests to the language model.

[0527] The language model (e.g., GPT-4) generates an appropriate response and returns the generated response data to the server, which then sends the response data to the device, which then displays the response to the user.

[0528] Specific working example:

[0529] For example, consider the case where a user asks a question about the weather. The user types, "What's the weather like in Tokyo tomorrow?" into the application's chat window. The device captures this input data and immediately sends it to the server. The server analyzes the input data and sends a request to a language model (such as GPT-4). The language model generates a response such as, "Tomorrow's weather in Tokyo will be sunny with occasional cloudy weather." The server then sends the generated response data to the device, which then displays the response to the user. Through this process, the user can enjoy a flexible AI conversation in real time.

[0530] In this way, the present invention is a system that enables high-quality conversation in real time based on a language model selected by the user, and solves the problems of the prior art.

[0531] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0532] Step 1: Create a user account

[0533] The user installs and launches the application. They enter the required information (email address, password, etc.) on the account creation screen. This is the input. The device sends this account information to the server. The server records the received account information in a database and sends a confirmation email to the user. This is the output.

[0534] Step 2: Language model selection

[0535] After creating an account, the user selects the language model (GPT-4, BLOOM, LLaMA, etc.) to use on the app's settings screen. This is the input. The device sends information about the selected language model to the server. The server associates the selection information with the user's account information and records it in a database. This is the output.

[0536] Step 3: Establishing communication and authenticating

[0537] The device establishes communication with the server using a VoLTE or VoNR line. This is the input. The server receives the connection and verifies that the communication is encrypted. It then authenticates the user. If authentication is successful, communication is established. This is the output.

[0538] Step 4: User input

[0539] The user types a query into a chat window within the application (e.g., "What's the weather like now?"). This is the input. The device captures the user's input data in real time. The captured data becomes the input for the next step.

[0540] Step 5: Submitting input data

[0541] The device sends the captured input data to the server. This is the input. The server prepares the received data for analysis. The input data is converted into an analyzable format. This is the output.

[0542] Step 6: Analyzing the inquiry

[0543] The server parses the input data it receives and converts it into an appropriate format. This is the input. Natural Language Processing (NLP) techniques are used in this parsing step. The parsed data becomes the input for the next step. This is the output.

[0544] Step 7: Requesting a Language Model

[0545] The server sends the parsed data as a request to the user's chosen language model, which is the input. The language model generates an appropriate response based on the input data, which is the output.

[0546] Step 8: Response Generation

[0547] The language model generates a response and returns that data to the server, which is the input. The server then prepares the received response data to send to the user's device, which is the output.

[0548] Step 9: Send and view the response

[0549] The server sends the response data to the terminal. This is the input. The terminal displays the received response data to the user. This is the output. The user can check the displayed response and enter the next query (e.g., "What's the weather like tomorrow?"), which will cause the same process to start again from step 4.

[0550] (Application example 1)

[0551] 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."

[0552] In today's electronic payment services, users lack the means to quickly and accurately obtain information about new payment methods and questions. This often leaves users feeling anxious and frustrated when making payments, resulting in a poor customer experience. The present invention aims to solve these user issues and provide a smoother and more satisfying electronic payment experience.

[0553] 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.

[0554] In this invention, the server includes means for a user to create an account and select a language model, means for a terminal to establish communication using a VoLTE or VoNR line and confirm user authentication and encryption, means for transmitting the user's text input to the server in real time, means for generating appropriate responses based on the language model to the user's questions regarding payment methods and electronic payments, and means for transmitting these responses from the server to the terminal and displaying the responses received by the terminal to the user, thereby enabling the user to obtain high-quality information regarding electronic payments in real time.

[0555] "Means for creating an account" refers to the function that allows a user to register with the system and set individual user information.

[0556] "Means for selecting a language model" is a function that allows users to select the model to use from multiple AI language models.

[0557] "Means for the terminal to establish communication with the server using a VoLTE or VoNR line" refers to the ability of the user's device to establish a connection with the server using communications technology that enables high-quality voice and data communications.

[0558] "User authentication and encryption verification measures" are procedures used to verify the legitimacy of users and ensure data privacy and security.

[0559] "Means for receiving text input from the user and sending it to the server" is a function for obtaining text data input by the user into the terminal and sending it to the server.

[0560] The "means for the server to send the user's input to the language model and generate a response" is the function by which the server sends the text received from the user to the selected language model and generates an appropriate answer.

[0561] The "means for the server to send the generated response to the terminal and for the terminal to display the received response to the user" is a function for sending a text response generated by the server to the user's terminal and displaying the response on the terminal.

[0562] "Means for generating appropriate responses in real time to user questions about payment methods and electronic payments" is a function for providing immediate and appropriate answers to user questions about electronic payments.

[0563] The present invention is a system that allows users to obtain information in real time about questions about electronic payments and new payment methods. A specific embodiment of this system will be described below.

[0564] The system allows users to create an account using a device such as a smartphone or smart glasses, and then select from multiple language models. The selected language model is then used to generate high-quality responses for electronic payments in real time.

[0565] Hardware and software used

[0566] Devices: Smartphones (iOS, Android), smart glasses (e.g., Google Glass)

[0567] Communication infrastructure: VoLTE, VoNR lines

[0568] Server: Cloud server (e.g. AWS, Google Cloud)

[0569] Language Model API: Generative AI models (e.g., OpenAI's GPT-4, BLOOM, Meta's LLaMA)

[0570] Specific details of data processing and data calculation

[0571] 1. Create an account and select a model:

[0572] A user installs the application and creates an account. At that time, they select the language model they want to use from multiple models (GPT-4, BLOOM, LLaMA, etc.). This information is sent from the device to the server and recorded in a database on the server.

[0573] 2. Communication establishment and encryption:

[0574] The device establishes communication with the server using a VoLTE or VoNR line. The server then verifies that the communication is encrypted and authenticates the user. This communication method maintains high security and privacy.

[0575] 3. User input and response generation:

[0576] A user types a question about electronic payments within an application, such as "What points can I use to pay at this store?" This text data is sent in real time by the device to a server. The server analyzes the input and sends a request to a language model previously selected by the user. The language model generates an appropriate response and returns it to the server.

[0577] 4. View the response:

[0578] The server generates a response, which is sent to the terminal and displayed to the user, for example, "This store accepts points for payment." If the user then asks a follow-up question, for example, "What are some other payment methods?", the process is repeated.

[0579] Examples of prompt statements

[0580] Here are some example prompts that can be used when a user enters a question about an electronic payment:

[0581] User input: "What is your most recent payment method?"

[0582] GPT-4 prompt: “User wants the latest information on electronic payments. In particular, please provide details about new payment methods and their availability in stores.”

[0583] In this way, the system allows users to get real-time answers to questions and information about electronic payments instantly, providing a smoother and more satisfying experience.

[0584] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0585] Step 1:

[0586] A user creates an account and selects a language model. Input includes a username, password, and the selected language model (e.g., GPT-4, BLOOM, LLaMA). The device collects this information and sends it to the server, which records it in a database.

[0587] Step 2:

[0588] The device establishes communication with the server using a VoLTE or VoNR line. Input includes the user's device information and authentication information. The server confirms that communication has been established and that the transmitted data is encrypted. The user's authentication information is verified to ensure the security of the communication.

[0589] Step 3:

[0590] The user inputs a question about electronic payment within the application. For example, a question such as "How many points can I use to pay at this store?" This text data is collected by the terminal and sent to the server in real time. The input includes the user's text data.

[0591] Step 4:

[0592] The server analyzes the received text data, generates prompts against a pre-selected language model, and sends the request. The input includes the user's question and the selected language model. The language model (e.g., GPT-4) generates an appropriate response based on the request. The output is the appropriate response.

[0593] Step 5:

[0594] The server receives the response generated from the language model and sends it to the device. The input includes the generated response. The output includes the data to be sent to the device, allowing the user to receive the response on the device.

[0595] Step 6:

[0596] The terminal displays the response received from the server to the user. The response data sent from the server is included as input. Specifically, a response such as "Points can be used for payment at this store" is displayed. The user can check this information on the screen.

[0597] Step 7:

[0598] If the user subsequently asks a question (e.g., "What are the alternative payment methods?"), the process repeats, this time with the user's new question. The device again collects text data and sends it in real time to the server, where it is parsed, a language model generates a response, and the response is sent to the device for display.

[0599] The above is the specific processing flow of the program. This system allows users to receive high-quality responses in real time to questions about electronic payments.

[0600] 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.

[0601] ---

[0602] The present invention provides an AI conversation system incorporating an emotion engine that recognizes user emotions. The system includes means for a user to create an account and select a language model, means for establishing communication with a server using a VoLTE or VoNR line and verifying user authentication and encryption, means for receiving the user's text input and sending it to the server, means for the server to send the user's input to the language model and generate a response, means for the server to send the generated response to a terminal and means for the terminal to display the received response to the user, and means for recognizing the user's emotions using the emotion engine and generating or modifying an appropriate response based on the user's emotions.

[0603] First, the user installs a dedicated application and creates an account. After installation, the user is taken to an account creation screen and enters the necessary personal information to create the account. Next, the user selects the language model (e.g., GPT-4, BLOOM, LLaMA, etc.) they wish to use when creating the account or later. This information is sent from the device to the server, and the server records the selected language model in a database.

[0604] The device establishes communication with the server using a VoLTE or VoNR connection. Once communication is established, the server authenticates and authorizes the connected device. The server then verifies that the communication is encrypted. Once this verification is complete, secure data transmission between the device and the server begins.

[0605] Users input questions or requests into a text box within the app. For example, if a user types "How are you feeling today?", the device sends this data to the server in real time. At the same time, the emotion engine analyzes the user's input and recognizes the user's emotional state (e.g., joy, sadness, anger, etc.). The emotional information recognized by the emotion engine is also sent to the server.

[0606] The server analyzes the received user input and emotional information and sends a request to the selected language model. The language model generates a more appropriate response based on the user input and emotional information. For example, the response generated might be, "You look great today, have a great day!" This response is then sent to the device via the server.

[0607] The device analyzes the response received from the server and displays it on the user's screen, allowing the user to receive a response that reflects their emotional state. If the user wants to ask a follow-up question, such as "What are your plans for tomorrow?", the same process is repeated.

[0608] When a user wants to end a conversation session, they press the end button in the app. The device sends a request to end the session to the server, and the server ends the session and disconnects the communication. The device then performs the termination process and notifies the user that the session has ended.

[0609] In this way, by incorporating an emotion engine, the present invention is a system that can provide more appropriate and high-quality responses according to the user's emotional state, thereby resolving conventional problems and providing users with a satisfying conversation experience.

[0610] ---

[0611] The processing flow will be explained below.

[0612] Step 1:

[0613] The user installs the dedicated application. The user launches the application and moves to the account creation screen. The user enters the required information such as name, email address, and password to create an account.

[0614] Step 2:

[0615] After creating an account, the user selects the language model they want to use (e.g., GPT-4, BLOOM, LLaMA, etc.). The device sends information about the selected language model to the server, which then records this information in a database.

[0616] Step 3:

[0617] The device establishes communication with the server using a VoLTE or VoNR line and notifies the server that communication has begun.

[0618] Step 4:

[0619] The server authenticates the connected device. The server verifies that the communication is encrypted. Once authentication and encryption are successful, data transmission between the device and the server begins.

[0620] Step 5:

[0621] The user types a question or request into a text box within the application (e.g., "How are you feeling today?"). The device sends the text data in real time to the server. In parallel, the device analyzes the input and runs an emotion engine to recognize the user's emotions.

[0622] Step 6:

[0623] The device sends the emotional information analyzed by the emotion engine to the server. The emotion engine identifies the user's emotion (e.g., joy, sadness, anger, etc.). This emotional information is also sent to the server.

[0624] Step 7:

[0625] The server receives and analyzes the user's input and emotion information. The server sends a request to the selected language model. The language model generates a response based on the user's input and emotion information.

[0626] Step 8:

[0627] The server receives the response generated from the language model and sends it to the terminal. The server then confirms that the response has been sent.

[0628] Step 9:

[0629] The device parses the response received from the server and displays it on the user's screen. For example, if the user asks, "How are you feeling today?", the language model generates a response such as, "You look great today, have a great day!", which the device displays.

[0630] Step 10:

[0631] If the user wants to enter an additional question or request, the process returns to step 5. For example, the user enters, "What are your plans for tomorrow?" The device again sends the input data and emotion information to the server, and the same process is repeated.

[0632] Step 11:

[0633] When a user wants to end a conversation session, they press the end button in the app. The device sends a request to end the session to the server. The server ends the session and disconnects the connection. The device then performs the termination process and notifies the user that the session has ended.

[0634] In this way, incorporating an emotion engine allows the system to provide more appropriate and higher quality responses based on the user's emotional state, resulting in a more satisfying conversational experience for the user.

[0635] Example 2

[0636] 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."

[0637] Conventional AI conversation systems generate uniform responses without considering the user's emotions, making it difficult to improve the individual user experience. For this reason, there is a need for a system that can provide more natural and satisfying conversations that respond to the user's emotions.

[0638] 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.

[0639] In this invention, the server includes means for a user to create an account and select a language model, means for a terminal to establish communication with the server using a mobile communication environment and confirm user authentication and encryption, means for receiving a text input from the user and sending it to the server, means for the server to send the user's input to the language model and generate a response, means for the server to send the generated response to the terminal and display the response received by the terminal to the user, means for recognizing the user's emotional state using an emotion engine and sending that information to the server, and means for the server to modify and generate a response based on the emotion information, thereby making it possible to provide a more appropriate and satisfying response that corresponds to the user's emotions.

[0640] "Account" means the personal registration information required for a User to access the System.

[0641] A "language model" is an artificial intelligence-based algorithm or program for generating responses based on a user's text input.

[0642] A "mobile communication environment" is a network that uses wireless communication technologies such as VoLTE and VoNR.

[0643] "User authentication" is the process by which a system verifies a user's identity.

[0644] "Encryption" is the technique of converting plaintext into ciphertext in order to protect data.

[0645] "Text input" refers to the act of a user inputting text into a system.

[0646] An "emotion engine" is a system that analyzes and recognizes the emotional state from the user's input.

[0647] A "response" is a reply message generated in response to a user's text input.

[0648] A "server" is a computer system or network that centrally manages data processing and storage.

[0649] "Terminal" refers to the device used by the user (smartphone, PC, etc.).

[0650] The present invention provides an AI conversation system incorporating an emotion engine that recognizes user emotions. The system includes means for a user to create an account and select a language model, means for establishing communication with a server using a mobile communication environment and confirming user authentication and encryption, means for receiving the user's text input and sending it to the server, means for the server to send the user's input to the language model and generate a response, means for the server to send the generated response to a terminal and means for the terminal to display the received response to the user, and means for recognizing the user's emotions using the emotion engine and generating and modifying an appropriate response based on the user's emotions.

[0651] Hardware and Software Configuration

[0652] An implementation of this system includes the following hardware and software:

[0653] Device: Smartphone, PC, or other device

[0654] Server: A computer system that centrally manages data processing and storage.

[0655] Mobile communication environment: Wireless communication technologies such as VoLTE and VoNR

[0656] Dedicated applications: User-installed apps

[0657] Language models: Artificial intelligence-based algorithms such as GPT-4, BLOOM, and LLaMA

[0658] Emotion Engine: A system that analyzes the emotional state from user input

[0659] System Operation

[0660] First, the user installs a dedicated application and creates an account. After installation, the user is taken to an account creation screen and enters the necessary personal information (name, email address, password, etc.) to create an account. Next, the user selects the language model they want to use (e.g., GPT-4, BLOOM, LLaMA, etc.). This information is sent from the device to the server, and the server records the selected language model in a database.

[0661] The device establishes communication with the server using a VoLTE or VoNR line. Once communication is established, the server authenticates the connected device and verifies encrypted communication. After this verification is complete, secure data transmission between the device and the server begins.

[0662] Users input questions or requests into a text box within the app. For example, if they type "How are you feeling today?", the device sends this data to the server in real time. At the same time, the emotion engine analyzes the user's input and recognizes the user's emotional state (e.g., joy, sadness, anger, etc.). The analyzed emotion information is also sent from the device to the server.

[0663] The server analyzes the received user input and emotional information and sends a request to the selected language model. The language model generates a more appropriate response based on the user input and emotional information. For example, the response generated might be, "You look great today, have a great day!" This response is then sent to the device via the server.

[0664] The device analyzes the response received from the server and displays it on the user's screen, allowing the user to receive a response that reflects their emotional state. If the user has additional questions, for example, "What are your plans for tomorrow?", the same process is repeated.

[0665] When a user wants to end a conversation session, they press the end button in the app, and the device sends a session termination request to the server. The server ends the session and disconnects the connection. The device then performs the termination process and notifies the user of the end of the session.

[0666] In this way, the present invention provides a system that can provide more appropriate and high-quality responses according to the user's emotional state, thereby resolving the problems of the past and providing the user with a satisfying conversation experience.

[0667] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0668] Step 1:

[0669] Installing the application and creating an account

[0670] 1. User installs the application:

[0671] Input: The user downloads a dedicated application onto their smartphone or PC.

[0672] How it works: The application is downloaded over the internet and installed on your device.

[0673] Output: The application is installed on the device and its icon is displayed.

[0674] 2. User creates an account:

[0675] Input: A user enters personal information into an application, such as their name, email address, or password.

[0676] How it works: The information you enter is encrypted on the device.

[0677] Output: Encrypted personal information is generated.

[0678] 3. The device sends the information to the server:

[0679] Input: Encrypted personal information.

[0680] Operation: The terminal uses a communication line to send data to the server.

[0681] Output: Personal information received on the server side.

[0682] 4. The server creates the account:

[0683] Input: Encrypted personal information received by the server.

[0684] How it works: The server records the information in a database and creates an account for the user.

[0685] Output: The account is registered in the database.

[0686] Step 2:

[0687] Language model selection

[0688] 1. The user selects a language model:

[0689] Input: The user selects the language model they want to use (GPT-4, BLOOM, LLaMA, etc.).

[0690] What it does: The application retrieves the ID of the selected model.

[0691] Output: The selected language model ID is generated.

[0692] 2. The device sends the model selection information to the server:

[0693] Input: The selected language model ID.

[0694] Operation: The terminal sends model selection information to the server.

[0695] Output: Model selection information received on the server.

[0696] 3. The server records the selection:

[0697] Input: The language model ID received by the server.

[0698] Operation: The server records the model selection information in a database.

[0699] Output: Model selection information is persisted in a database.

[0700] Step 3:

[0701] Establishing communication

[0702] 1. The device establishes communication with the server:

[0703] Input: The terminal sends a request to start communication.

[0704] Operation: The device starts communication using a VoLTE or VoNR line.

[0705] Output: The request arrives at the server.

[0706] 2. The server authenticates the device and verifies the encrypted communication:

[0707] Input: A communication connection request from the device.

[0708] How it works: The server verifies the device's identity and checks the status of the encrypted communication.

[0709] Output: Communication is authenticated and cryptographic verification is complete.

[0710] 3. Initiating secure data transmission:

[0711] Input: An authenticated and encrypted communications connection.

[0712] How it works: Secure data transmission begins between the device and the server.

[0713] Output: Ready for data transmission.

[0714] Step 4:

[0715] User Input and Emotion Recognition

[0716] 1. User enters text:

[0717] Input: The user types a question or request into a text box.

[0718] What it does: Enter a prompt sentence into the text box, such as "How are you feeling today?"

[0719] Output: The input text data is generated.

[0720] 2. The device sends the text to the server:

[0721] Input: The text data entered.

[0722] How it works: The device sends text data to the server in real time.

[0723] Output: Text data arrives at the server.

[0724] 3. The emotion engine analyzes the text and recognizes emotions:

[0725] Input: User's text data.

[0726] How it works: The emotion engine analyzes the text and recognizes the user's emotional state (happy, sad, angry, etc.).

[0727] Output: Emotional information is generated.

[0728] 4. The device sends the emotion information to the server:

[0729] Input: Emotion information.

[0730] Operation: The device sends emotion information to the server.

[0731] Output: Emotion information arrives at the server.

[0732] Step 5:

[0733] Response Generation

[0734] 1. The server analyzes the user's input and emotional information:

[0735] Input: User's text data and emotion information.

[0736] How it works: The server parses both pieces of data.

[0737] Output: The analysis results are generated.

[0738] 2. The server sends a request to the language model:

[0739] Input: Analysis results.

[0740] How it works: The server sends the appropriate request to the language model.

[0741] Output: The request arrives at the language model.

[0742] 3. The language model generates a response:

[0743] Input: The request sent to the language model.

[0744] How it works: The language model generates a response based on the analysis.

[0745] Output: A response such as "You look great today, have a great day!" is generated.

[0746] 4. The server generates a response and sends it to the device:

[0747] Input: The generated response.

[0748] Action: The server sends a response to the device.

[0749] Output: The response arrives on the terminal.

[0750] Step 6:

[0751] Viewing the response

[0752] 1. Parse the response received by the device:

[0753] Input: The response sent by the server.

[0754] Action: The device analyzes the response data.

[0755] Output: The parsed response data is generated.

[0756] 2. The terminal displays the response to the user:

[0757] Input: Parsed response data.

[0758] Behavior: Displays a response message on the screen.

[0759] Output: The user will be able to see the response on the screen.

[0760] Step 7:

[0761] Session End

[0762] 1. User ends conversation session:

[0763] Input: The user presses the exit button.

[0764] Behavior: A click event on the Exit button occurs.

[0765] Output: A session termination request is generated.

[0766] 2. The device sends a session termination request to the server:

[0767] Input: Session termination request.

[0768] Operation: The device sends a request to the server.

[0769] Output: The request arrives at the server.

[0770] 3. The server ends the session and disconnects:

[0771] Input: Session termination request.

[0772] Action: The server ends the session and disconnects.

[0773] Output: The connection is dropped and the session is terminated.

[0774] 4. The terminal shuts down and notifies the user:

[0775] Input: Communication is lost.

[0776] Behavior: The application performs termination processing and notifies the user of the end of the session.

[0777] Output: The user can confirm the session has ended.

[0778] (Application example 2)

[0779] 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."

[0780] Conventional food delivery systems have difficulty providing customized services based on a user's emotional state. Furthermore, when a user is feeling stressed or tired, they are unable to appropriately suggest meals or special offers that correspond to that state. As a result, users' satisfaction declines and they tend to use the service less frequently. Therefore, the present invention aims to build a system that incorporates user emotion recognition to provide a customized food delivery service that corresponds to the user's emotional state, thereby improving user satisfaction.

[0781] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0782] means for recognizing a user's emotion and generating and modifying an appropriate response based on the recognized emotion;

[0783] A means for suggesting additional services or products customized to the user based on the perceived emotions;

[0784] A means for users to review the proposed services and products and confirm their orders;

[0785] This makes it possible to propose appropriate services and products according to the user's emotional state.

[0786] Definitions of important words

[0787] An "account" is personal identification information created by a user to use the system.

[0788] A "language model" is an algorithm or program used in natural language processing to generate appropriate responses based on user input.

[0789] "Wireless communication line" refers to a data transmission means using wireless communication technology such as VoLTE or VoNR.

[0790] A "server" is a computer system responsible for receiving and analyzing user data, and generating and sending an appropriate response.

[0791] An "emotion engine" is an algorithm or software that analyzes and recognizes emotions from a user's text or voice input.

[0792] A "response" is a response or reaction generated in response to user input.

[0793] "Customized additional services and products" are services and products that are specifically suggested taking into account the user's emotional state.

[0794] "Confirming an order" means that the user has made clear their intention to purchase the proposed service or product, and the transaction is now complete.

[0795] "User authentication" is a procedure for verifying the legitimacy of a user accessing a system.

[0796] "Encryption" is a data conversion technology for securely transmitting communication data.

[0797] "Text input" refers to a string of characters that a user enters to communicate or give instructions to a system.

[0798] Description: Detailed Description of the Invention

[0799] This invention is a system for recognizing user emotions in a food delivery service and providing customized services to users based on that data. Specific implementation methods for realizing this system will be described.

[0800] Hardware and software used

[0801] Hardware: Smartphones, servers

[0802] Software: EmotionRecognitionAPI, FoodDeliveryAPI

[0803] Overall system overview

[0804] First, the user installs a dedicated application on their smartphone and creates an account. After launching the application, the user enters the necessary personal information and creates an account. When creating the account or later, the user selects the language model (for example, a general artificial intelligence model) they wish to use. This information is sent from the smartphone to the server, and the selected language model is recorded in the server's database.

[0805] Establishment and authentication of communication over VoLTE / VoNR lines

[0806] The smartphone establishes communication with the server using a VoLTE or VoNR connection. After communication is established, the server authenticates and authorizes the smartphone. It also ensures that the communication is encrypted. Once this is complete, secure data transmission begins.

[0807] User Input and Emotion Recognition

[0808] Users enter questions or requests into a text box within the app. For example, if a user types "I'm very tired today," the smartphone sends this data to the server in real time. At this time, the EmotionRecognition API analyzes the entered text and recognizes the user's emotional state (e.g., fatigue). The emotional information recognized by the emotion engine is also sent to the server.

[0809] Server response generation

[0810] The server analyzes the received user input and emotional information and sends a request to the selected language model. The language model generates an appropriate response based on the user input and emotional information. For example, the response generated might be, "Would you like some relaxing herbal tea and a light snack?" This response is then sent via the server to the smartphone.

[0811] Response display and order confirmation

[0812] The smartphone analyzes the response received from the server and displays it on the user's screen. The user checks the proposed services and products and decides whether to place an order. If the order is confirmed, the smartphone sends the order data to the server, which processes the order.

[0813] Specific prompt examples

[0814] "I'm very tired today"

[0815] "Is there a relaxation menu you would recommend?"

[0816] This will realize a system that provides food delivery services according to the user's emotional state. Users can receive services that match their emotional state, which is expected to improve their satisfaction.

[0817] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0818] Program processing steps

[0819] Step 1

[0820] User account creation

[0821] Input: User's personal information (name, contact details, etc.)

[0822] How it works: The device captures the personal information entered by the user to create an account.

[0823] Output: An account creation request is generated and sent to the server.

[0824] Data processing: Formatting user information and converting it into a data structure to send to the server.

[0825] Step 2

[0826] Language model selection

[0827] Input: Selection information for the language model you want to use (e.g., a general-purpose artificial intelligence model)

[0828] Operation: The device obtains information about the language model selected by the user.

[0829] Output: A language model selection request is sent to the server and the selection information is recorded in a database.

[0830] Data processing: The selected language model information is formatted and converted into a data structure for transmission to the server.

[0831] Step 3

[0832] Establishing communication

[0833] Input: Device connection information

[0834] Operation: The terminal starts communication with the server using a VoLTE or VoNR line.

[0835] Output: The server receives the connection request and establishes communication.

[0836] Data Computing: Encrypts and authenticates connections to ensure communications are secure.

[0837] Step 4

[0838] Accepting text input

[0839] Input: The text of the user's question or request (e.g., "I'm very tired today")

[0840] Action: The terminal captures the text entered by the user.

[0841] Output: The text input data is sent to the server.

[0842] Data processing: Converting user input into a data structure to be sent to the server in real time.

[0843] Step 5

[0844] Emotion Recognition and Analysis

[0845] Input: User text input data

[0846] How it works: The server uses the EmotionRecognition API to analyze the text and recognize the user's emotions.

[0847] Output: The recognized emotion information is generated and included in the request to the language model.

[0848] Data Computation: Perform text analysis and emotion recognition to extract emotional information.

[0849] Step 6

[0850] Generating a response

[0851] Input: User text input and emotional information

[0852] Operation: The server sends a request to the selected language model and generates an appropriate response.

[0853] Output: The generated response is sent to the terminal.

[0854] Data Computation: Uses language models to generate appropriate responses based on input data.

[0855] Step 7

[0856] Viewing the response

[0857] Input: Response data received from the server

[0858] Operation: The device analyzes the received response data and displays it on the user's screen.

[0859] Output: The user is shown an appropriate response.

[0860] Data processing: Converting response data into a format that can be displayed.

[0861] Step 8

[0862] Proposals for services and products

[0863] Input: Recognized emotion information and generated response

[0864] How it works: The server suggests customized services and products based on the user's emotional state.

[0865] Output: The proposed data is sent to the device.

[0866] Data calculation: Generates suggestion data based on emotional information and makes optimal suggestions to users.

[0867] Step 9

[0868] Confirming the order

[0869] Input: User confirmation and order intention

[0870] How it works: The terminal allows the user to review the proposed services and products and place the order.

[0871] Output: The order confirmation data is sent to the server and the transaction is completed.

[0872] Data processing: Order data is formatted and sent to the server for order processing.

[0873] Examples of prompt statements

[0874] "I'm very tired today"

[0875] "Is there a relaxation menu you would recommend?"

[0876] This enables system-wide processing, allowing users to enjoy a food delivery service that is customized according to their emotions.

[0877] 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.

[0878] 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.

[0879] 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.

[0880] [Third embodiment]

[0881] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0882] 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.

[0883] 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).

[0884] 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.

[0885] 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.

[0886] 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).

[0887] 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. 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.

[0888] 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.

[0889] 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.

[0890] 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.

[0891] 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.

[0892] 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."

[0893] ---

[0894] The following describes in detail the embodiments of the present invention. The present invention is a system that allows a user to select a language model and engage in high-quality conversation with an AI based on that selection. The user accesses the system using a terminal and establishes communication with a server via a VoLTE or VoNR line. The program processing of the present invention is performed as follows.

[0895] First, the user installs the application and creates an account. When creating an account, the user selects the language model they want to use from multiple language models (e.g., GPT-4, BLOOM, LLaMA, etc.). The device sends this information to the server, which then records the selected language model in a database.

[0896] Next, the device establishes communication with the server using a VoLTE or VoNR line. The server receives this connection, verifies that the communication is encrypted, and authenticates the user, ensuring that data transmitted between the device and the server is secure.

[0897] Next, the user enters a query within the application. For example, the user might enter, "What's the weather like today?" This input data is sent in real time by the device to the server. The server analyzes this input and sends a request to the language model. The server then invokes the processing of the language model previously selected by the user (in this case, GPT-4) to generate the appropriate response.

[0898] The generated response is then sent back to the device via the server. For example, if a response such as "The current weather in Tokyo is sunny," is generated, the device receives it and displays it to the user. In this way, the user can receive high-quality responses in real time.

[0899] If the user subsequently asks an additional question, for example, "What's the weather going to be like tomorrow?", the same process is repeated. The user's input is sent to the server via the device, and the server generates a response based on the language model and sends it back to the device. The device then displays the response to the user. Through this process, the user can enjoy a continuous conversation with the AI.

[0900] As described above, the present invention is a system that provides a high-quality communication environment and flexible language model selection, thereby offering users a satisfying conversation experience. Users can select the optimal language model based on their own goals and preferences, and receive real-time responses based on that selection. In this way, the present invention solves the problems of the past and provides an improved AI conversation service.

[0901] ---

[0902] The processing flow will be explained below.

[0903] Step 1:

[0904] The user installs the application. The user launches the application and is taken to an account creation screen. The user enters the required information (name, email address, password, etc.) and creates an account. After creating the account, the user selects the language model they want to use (e.g., GPT-4, BLOOM, LLaMA, etc.). The device sends information about the selected language model to the server. The server records this information in a database.

[0905] Step 2:

[0906] The device establishes communication with the server using a VoLTE or VoNR line. The server is notified that communication has begun. The server authenticates the connected device. The server verifies that the communication is encrypted. If authentication and encryption are successful, data transmission between the device and server begins.

[0907] Step 3:

[0908] The user enters a question or request into a text box within the application (e.g., "What's the current weather?"). The device sends the entered text data to the server in real time. The server receives the user's input.

[0909] Step 4:

[0910] The server analyzes the received user input. The analysis result is sent as a request to the selected language model (e.g., GPT-4). The language model generates an appropriate response to the user input. The response generated by the language model is sent back to the server.

[0911] Step 5:

[0912] The server receives the generated response and sends it to the device. The server confirms that the response has been sent. The device analyzes the response received from the server and displays it on the user's screen (e.g., "The current weather in Tokyo is sunny.").

[0913] Step 6:

[0914] If the user wants to enter an additional question or request, the process returns to step 3. For example, the user enters "What's the weather going to be like tomorrow?" The device again sends the input data to the server, and the process repeats.

[0915] Step 7:

[0916] When a user wants to end a conversation session, they press the end button in the app. The device sends a request to end the session to the server. The server ends the session and disconnects the connection. The device then performs the termination process and notifies the user that the session has ended.

[0917] This allows users to enjoy real-time conversations with AI using the language model of their choice in a high-quality communication environment.

[0918] Example 1

[0919] 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."

[0920] Conventional AI conversation systems have issues such as limited language models that users can select and lack of guaranteed communication security. They also face challenges in generating responses to user input in real time and displaying accurate responses. There is a need to solve these issues and enable high-quality real-time conversations based on the language model selected by the user.

[0921] 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.

[0922] In this invention, the server includes means for analyzing user input data in real time and converting it into an appropriate format, means for sending the analyzed data as a request to a language model, and means for sending a generated response to the terminal, thereby enabling responses to be generated in real time based on a language model selected by the user, and providing safe and high-quality AI conversations.

[0923] "User" refers to a person who uses a terminal to access the system, create an account, select a language model, and enter text.

[0924] "Terminal" refers to a device operated by a user, such as a smartphone, PC, or tablet, that communicates with a server and has the ability to display user input and received responses.

[0925] "Communication line" refers to a network infrastructure such as the Internet or a mobile network used to transmit and receive data between a terminal and a central processing unit (server).

[0926] "Central Processing Unit" refers to a server that analyzes a user's input, generates a response based on an appropriate language model, and transmits the generated response to the terminal.

[0927] "User authentication" refers to the process of verifying a user's identity and ensuring legitimate access to a system.

[0928] "Encryption" refers to the technology of encrypting data to protect it from unauthorized access in order to transmit it safely.

[0929] "Text input" refers to strings of data that a user sends to a system using an interface on a terminal.

[0930] "Language model" refers to AI technologies used to generate appropriate responses to user text input, including GPT-4, BLOOM, and LLaMA.

[0931] "Real-time analytics" refers to the process of instantly analyzing user input data, converting it into an appropriate format, and generating a response.

[0932] A "request" refers to a data transmission that requests a language model to generate a response based on the results of analyzing the user's input data.

[0933] "Generated response" refers to text data generated by a language model based on a user's request.

[0934] The following describes in detail the mode for carrying out the present invention. This invention is a system that allows a user to select from multiple language models and engage in high-quality conversations with an AI based on that selection. The configuration and operation of the system are described below.

[0935] System configuration:

[0936] The system consists of the following elements:

[0937] 1. User device: A smartphone, PC, tablet, etc. operated by a user. An application is installed on this device.

[0938] 2. Server: A high-performance cloud server (e.g., AWS, Google Cloud) that analyzes user input and generates responses using a language model.

[0939] 3. Communication line: Data communication between the terminal and the server is carried out using network infrastructure such as VoLTE or VoNR.

[0940] Software used:

[0941] User application: iOS or Android app, or web app

[0942] Language models: AI models such as GPT-4, BLOOM, and LLaMA

[0943] Communication protocol: VoLTE, VoNR

[0944] How it works:

[0945] 1. Create an account and select a model:

[0946] The user installs and launches the application. They enter the required information (email address, password, etc.) on the account creation screen. The device sends this information to the server, which records it in a database.

[0947] After creating an account, users select a language model to use from multiple models (GPT-4, BLOOM, LLaMA, etc.). The device sends the selection information to the server, which records it in a database.

[0948] 2. Communication establishment and authentication:

[0949] The device establishes communication with the server using a VoLTE or VoNR line. The server accepts the connection, verifies that the communication is encrypted, and then authenticates the user. If authentication is successful, communication continues.

[0950] 3. User input and response generation:

[0951] The user enters a query into a chat window within the application, for example, "What's the weather like now?"

[0952] The device sends this input data in real time to the server, which analyzes the input data and sends requests to the language model.

[0953] The language model (e.g., GPT-4) generates an appropriate response and returns the generated response data to the server, which then sends the response data to the device, which then displays the response to the user.

[0954] Specific working example:

[0955] For example, consider the case where a user asks a question about the weather. The user types, "What's the weather like in Tokyo tomorrow?" into the application's chat window. The device captures this input data and immediately sends it to the server. The server analyzes the input data and sends a request to a language model (such as GPT-4). The language model generates a response such as, "Tomorrow's weather in Tokyo will be sunny with occasional cloudy weather." The server then sends the generated response data to the device, which then displays the response to the user. Through this process, the user can enjoy a flexible AI conversation in real time.

[0956] In this way, the present invention is a system that enables high-quality conversation in real time based on a language model selected by the user, and solves the problems of the prior art.

[0957] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0958] Step 1: Create a user account

[0959] The user installs and launches the application. They enter the required information (email address, password, etc.) on the account creation screen. This is the input. The device sends this account information to the server. The server records the received account information in a database and sends a confirmation email to the user. This is the output.

[0960] Step 2: Language model selection

[0961] After creating an account, the user selects the language model (GPT-4, BLOOM, LLaMA, etc.) to use on the app's settings screen. This is the input. The device sends information about the selected language model to the server. The server associates the selection information with the user's account information and records it in a database. This is the output.

[0962] Step 3: Establishing communication and authenticating

[0963] The device establishes communication with the server using a VoLTE or VoNR line. This is the input. The server receives the connection and verifies that the communication is encrypted. It then authenticates the user. If authentication is successful, communication is established. This is the output.

[0964] Step 4: User input

[0965] The user types a query into a chat window within the application (e.g., "What's the weather like now?"). This is the input. The device captures the user's input data in real time. The captured data becomes the input for the next step.

[0966] Step 5: Submitting input data

[0967] The device sends the captured input data to the server. This is the input. The server prepares the received data for analysis. The input data is converted into an analyzable format. This is the output.

[0968] Step 6: Analyzing the inquiry

[0969] The server parses the input data it receives and converts it into an appropriate format. This is the input. Natural Language Processing (NLP) techniques are used in this parsing step. The parsed data becomes the input for the next step. This is the output.

[0970] Step 7: Requesting a Language Model

[0971] The server sends the parsed data as a request to the user's chosen language model, which is the input. The language model generates an appropriate response based on the input data, which is the output.

[0972] Step 8: Response Generation

[0973] The language model generates a response and returns that data to the server, which is the input. The server then prepares the received response data to send to the user's device, which is the output.

[0974] Step 9: Send and view the response

[0975] The server sends the response data to the terminal. This is the input. The terminal displays the received response data to the user. This is the output. The user can check the displayed response and enter the next query (e.g., "What's the weather like tomorrow?"), which will cause the same process to start again from step 4.

[0976] (Application example 1)

[0977] 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."

[0978] In today's electronic payment services, users lack the means to quickly and accurately obtain information about new payment methods and questions. This often leaves users feeling anxious and frustrated when making payments, resulting in a poor customer experience. The present invention aims to solve these user issues and provide a smoother and more satisfying electronic payment experience.

[0979] 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.

[0980] In this invention, the server includes means for a user to create an account and select a language model, means for a terminal to establish communication using a VoLTE or VoNR line and confirm user authentication and encryption, means for transmitting the user's text input to the server in real time, means for generating appropriate responses based on the language model to the user's questions regarding payment methods and electronic payments, and means for transmitting these responses from the server to the terminal and displaying the responses received by the terminal to the user, thereby enabling the user to obtain high-quality information regarding electronic payments in real time.

[0981] "Means for creating an account" refers to the function that allows a user to register with the system and set individual user information.

[0982] "Means for selecting a language model" is a function that allows users to select the model to use from multiple AI language models.

[0983] "Means for the terminal to establish communication with the server using a VoLTE or VoNR line" refers to the ability of the user's device to establish a connection with the server using communications technology that enables high-quality voice and data communications.

[0984] "User authentication and encryption verification measures" are procedures used to verify the legitimacy of users and ensure data privacy and security.

[0985] "Means for receiving text input from the user and sending it to the server" is a function for obtaining text data input by the user into the terminal and sending it to the server.

[0986] The "means for the server to send the user's input to the language model and generate a response" is the function by which the server sends the text received from the user to the selected language model and generates an appropriate answer.

[0987] The "means for the server to send the generated response to the terminal and for the terminal to display the received response to the user" is a function for sending a text response generated by the server to the user's terminal and displaying the response on the terminal.

[0988] "Means for generating appropriate responses in real time to user questions about payment methods and electronic payments" is a function for providing immediate and appropriate answers to user questions about electronic payments.

[0989] The present invention is a system that allows users to obtain information in real time about questions about electronic payments and new payment methods. A specific embodiment of this system will be described below.

[0990] The system allows users to create an account using a device such as a smartphone or smart glasses, and then select from multiple language models. The selected language model is then used to generate high-quality responses for electronic payments in real time.

[0991] Hardware and software used

[0992] Devices: Smartphones (iOS, Android), smart glasses (e.g., Google Glass)

[0993] Communication infrastructure: VoLTE, VoNR lines

[0994] Server: Cloud server (e.g. AWS, Google Cloud)

[0995] Language Model API: Generative AI models (e.g., OpenAI's GPT-4, BLOOM, Meta's LLaMA)

[0996] Specific details of data processing and data calculation

[0997] 1. Create an account and select a model:

[0998] A user installs the application and creates an account. At that time, they select the language model they want to use from multiple models (GPT-4, BLOOM, LLaMA, etc.). This information is sent from the device to the server and recorded in a database on the server.

[0999] 2. Communication establishment and encryption:

[1000] The device establishes communication with the server using a VoLTE or VoNR line. The server then verifies that the communication is encrypted and authenticates the user. This communication method maintains high security and privacy.

[1001] 3. User input and response generation:

[1002] A user types a question about electronic payments within an application, such as "What points can I use to pay at this store?" This text data is sent in real time by the device to a server. The server analyzes the input and sends a request to a language model previously selected by the user. The language model generates an appropriate response and returns it to the server.

[1003] 4. View the response:

[1004] The server generates a response, which is sent to the terminal and displayed to the user, for example, "This store accepts points for payment." If the user then asks a follow-up question, for example, "What are some other payment methods?", the process is repeated.

[1005] Examples of prompt statements

[1006] Here are some example prompts that can be used when a user enters a question about an electronic payment:

[1007] User input: "What is your most recent payment method?"

[1008] GPT-4 prompt: “User wants the latest information on electronic payments. In particular, please provide details about new payment methods and their availability in stores.”

[1009] In this way, the system allows users to get real-time answers to questions and information about electronic payments instantly, providing a smoother and more satisfying experience.

[1010] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1011] Step 1:

[1012] A user creates an account and selects a language model. Input includes a username, password, and the selected language model (e.g., GPT-4, BLOOM, LLaMA). The device collects this information and sends it to the server, which records it in a database.

[1013] Step 2:

[1014] The device establishes communication with the server using a VoLTE or VoNR line. Input includes the user's device information and authentication information. The server confirms that communication has been established and that the transmitted data is encrypted. The user's authentication information is verified to ensure the security of the communication.

[1015] Step 3:

[1016] The user inputs a question about electronic payment within the application. For example, a question such as "How many points can I use to pay at this store?" This text data is collected by the terminal and sent to the server in real time. The input includes the user's text data.

[1017] Step 4:

[1018] The server analyzes the received text data, generates prompts against a pre-selected language model, and sends the request. The input includes the user's question and the selected language model. The language model (e.g., GPT-4) generates an appropriate response based on the request. The output is the appropriate response.

[1019] Step 5:

[1020] The server receives the response generated from the language model and sends it to the device. The input includes the generated response. The output includes the data to be sent to the device, allowing the user to receive the response on the device.

[1021] Step 6:

[1022] The terminal displays the response received from the server to the user. The response data sent from the server is included as input. Specifically, a response such as "Points can be used for payment at this store" is displayed. The user can check this information on the screen.

[1023] Step 7:

[1024] If the user subsequently asks a question (e.g., "What are the alternative payment methods?"), the process repeats, this time with the user's new question. The device again collects text data and sends it in real time to the server, where it is parsed, a language model generates a response, and the response is sent to the device for display.

[1025] The above is the specific processing flow of the program. This system allows users to receive high-quality responses in real time to questions about electronic payments.

[1026] 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.

[1027] ---

[1028] The present invention provides an AI conversation system incorporating an emotion engine that recognizes user emotions. The system includes means for a user to create an account and select a language model, means for establishing communication with a server using a VoLTE or VoNR line and verifying user authentication and encryption, means for receiving the user's text input and sending it to the server, means for the server to send the user's input to the language model and generate a response, means for the server to send the generated response to a terminal and means for the terminal to display the received response to the user, and means for recognizing the user's emotions using the emotion engine and generating or modifying an appropriate response based on the user's emotions.

[1029] First, the user installs a dedicated application and creates an account. After installation, the user is taken to an account creation screen and enters the necessary personal information to create the account. Next, the user selects the language model (e.g., GPT-4, BLOOM, LLaMA, etc.) they wish to use when creating the account or later. This information is sent from the device to the server, and the server records the selected language model in a database.

[1030] The device establishes communication with the server using a VoLTE or VoNR connection. Once communication is established, the server authenticates and authorizes the connected device. The server then verifies that the communication is encrypted. Once this verification is complete, secure data transmission between the device and the server begins.

[1031] Users input questions or requests into a text box within the app. For example, if a user types "How are you feeling today?", the device sends this data to the server in real time. At the same time, the emotion engine analyzes the user's input and recognizes the user's emotional state (e.g., joy, sadness, anger, etc.). The emotional information recognized by the emotion engine is also sent to the server.

[1032] The server analyzes the received user input and emotional information and sends a request to the selected language model. The language model generates a more appropriate response based on the user input and emotional information. For example, the response generated might be, "You look great today, have a great day!" This response is then sent to the device via the server.

[1033] The device analyzes the response received from the server and displays it on the user's screen, allowing the user to receive a response that reflects their emotional state. If the user wants to ask a follow-up question, such as "What are your plans for tomorrow?", the same process is repeated.

[1034] When a user wants to end a conversation session, they press the end button in the app. The device sends a request to end the session to the server, and the server ends the session and disconnects the communication. The device then performs the termination process and notifies the user that the session has ended.

[1035] In this way, by incorporating an emotion engine, the present invention is a system that can provide more appropriate and high-quality responses according to the user's emotional state, thereby resolving conventional problems and providing users with a satisfying conversation experience.

[1036] ---

[1037] The processing flow will be explained below.

[1038] Step 1:

[1039] The user installs the dedicated application. The user launches the application and moves to the account creation screen. The user enters the required information such as name, email address, and password to create an account.

[1040] Step 2:

[1041] After creating an account, the user selects the language model they want to use (e.g., GPT-4, BLOOM, LLaMA, etc.). The device sends information about the selected language model to the server, which then records this information in a database.

[1042] Step 3:

[1043] The device establishes communication with the server using a VoLTE or VoNR line and notifies the server that communication has begun.

[1044] Step 4:

[1045] The server authenticates the connected device. The server verifies that the communication is encrypted. Once authentication and encryption are successful, data transmission between the device and the server begins.

[1046] Step 5:

[1047] The user types a question or request into a text box within the application (e.g., "How are you feeling today?"). The device sends the text data in real time to the server. In parallel, the device analyzes the input and runs an emotion engine to recognize the user's emotions.

[1048] Step 6:

[1049] The device sends the emotional information analyzed by the emotion engine to the server. The emotion engine identifies the user's emotion (e.g., joy, sadness, anger, etc.). This emotional information is also sent to the server.

[1050] Step 7:

[1051] The server receives and analyzes the user's input and emotion information. The server sends a request to the selected language model. The language model generates a response based on the user's input and emotion information.

[1052] Step 8:

[1053] The server receives the response generated from the language model and sends it to the terminal. The server then confirms that the response has been sent.

[1054] Step 9:

[1055] The device parses the response received from the server and displays it on the user's screen. For example, if the user asks, "How are you feeling today?", the language model generates a response such as, "You look great today, have a great day!", which the device displays.

[1056] Step 10:

[1057] If the user wants to enter an additional question or request, the process returns to step 5. For example, the user enters, "What are your plans for tomorrow?" The device again sends the input data and emotion information to the server, and the same process is repeated.

[1058] Step 11:

[1059] When a user wants to end a conversation session, they press the end button in the app. The device sends a request to end the session to the server. The server ends the session and disconnects the connection. The device then performs the termination process and notifies the user that the session has ended.

[1060] In this way, incorporating an emotion engine allows the system to provide more appropriate and higher quality responses based on the user's emotional state, resulting in a more satisfying conversational experience for the user.

[1061] Example 2

[1062] 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."

[1063] Conventional AI conversation systems generate uniform responses without considering the user's emotions, making it difficult to improve the individual user experience. For this reason, there is a need for a system that can provide more natural and satisfying conversations that respond to the user's emotions.

[1064] 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.

[1065] In this invention, the server includes means for a user to create an account and select a language model, means for a terminal to establish communication with the server using a mobile communication environment and confirm user authentication and encryption, means for receiving a text input from the user and sending it to the server, means for the server to send the user's input to the language model and generate a response, means for the server to send the generated response to the terminal and display the response received by the terminal to the user, means for recognizing the user's emotional state using an emotion engine and sending that information to the server, and means for the server to modify and generate a response based on the emotion information, thereby making it possible to provide a more appropriate and satisfying response that corresponds to the user's emotions.

[1066] "Account" means the personal registration information required for a User to access the System.

[1067] A "language model" is an artificial intelligence-based algorithm or program for generating responses based on a user's text input.

[1068] A "mobile communication environment" is a network that uses wireless communication technologies such as VoLTE and VoNR.

[1069] "User authentication" is the process by which a system verifies a user's identity.

[1070] "Encryption" is the technique of converting plaintext into ciphertext in order to protect data.

[1071] "Text input" refers to the act of a user inputting text into a system.

[1072] An "emotion engine" is a system that analyzes and recognizes the emotional state from the user's input.

[1073] A "response" is a reply message generated in response to a user's text input.

[1074] A "server" is a computer system or network that centrally manages data processing and storage.

[1075] "Terminal" refers to the device used by the user (smartphone, PC, etc.).

[1076] The present invention provides an AI conversation system incorporating an emotion engine that recognizes user emotions. The system includes means for a user to create an account and select a language model, means for establishing communication with a server using a mobile communication environment and confirming user authentication and encryption, means for receiving the user's text input and sending it to the server, means for the server to send the user's input to the language model and generate a response, means for the server to send the generated response to a terminal and means for the terminal to display the received response to the user, and means for recognizing the user's emotions using the emotion engine and generating and modifying an appropriate response based on the user's emotions.

[1077] Hardware and Software Configuration

[1078] An implementation of this system includes the following hardware and software:

[1079] Device: Smartphone, PC, or other device

[1080] Server: A computer system that centrally manages data processing and storage.

[1081] Mobile communication environment: Wireless communication technologies such as VoLTE and VoNR

[1082] Dedicated applications: User-installed apps

[1083] Language models: Artificial intelligence-based algorithms such as GPT-4, BLOOM, and LLaMA

[1084] Emotion Engine: A system that analyzes the emotional state from user input

[1085] System Operation

[1086] First, the user installs a dedicated application and creates an account. After installation, the user is taken to an account creation screen and enters the necessary personal information (name, email address, password, etc.) to create an account. Next, the user selects the language model they want to use (e.g., GPT-4, BLOOM, LLaMA, etc.). This information is sent from the device to the server, and the server records the selected language model in a database.

[1087] The device establishes communication with the server using a VoLTE or VoNR line. Once communication is established, the server authenticates the connected device and verifies encrypted communication. After this verification is complete, secure data transmission between the device and the server begins.

[1088] Users input questions or requests into a text box within the app. For example, if they type "How are you feeling today?", the device sends this data to the server in real time. At the same time, the emotion engine analyzes the user's input and recognizes the user's emotional state (e.g., joy, sadness, anger, etc.). The analyzed emotion information is also sent from the device to the server.

[1089] The server analyzes the received user input and emotional information and sends a request to the selected language model. The language model generates a more appropriate response based on the user input and emotional information. For example, the response generated might be, "You look great today, have a great day!" This response is then sent to the device via the server.

[1090] The device analyzes the response received from the server and displays it on the user's screen, allowing the user to receive a response that reflects their emotional state. If the user has additional questions, for example, "What are your plans for tomorrow?", the same process is repeated.

[1091] When a user wants to end a conversation session, they press the end button in the app, and the device sends a session termination request to the server. The server ends the session and disconnects the connection. The device then performs the termination process and notifies the user of the end of the session.

[1092] In this way, the present invention provides a system that can provide more appropriate and high-quality responses according to the user's emotional state, thereby resolving the problems of the past and providing the user with a satisfying conversation experience.

[1093] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1094] Step 1:

[1095] Installing the application and creating an account

[1096] 1. User installs the application:

[1097] Input: The user downloads a dedicated application onto their smartphone or PC.

[1098] How it works: The application is downloaded over the internet and installed on your device.

[1099] Output: The application is installed on the device and its icon is displayed.

[1100] 2. User creates an account:

[1101] Input: A user enters personal information into an application, such as their name, email address, or password.

[1102] How it works: The information you enter is encrypted on the device.

[1103] Output: Encrypted personal information is generated.

[1104] 3. The device sends the information to the server:

[1105] Input: Encrypted personal information.

[1106] Operation: The terminal uses a communication line to send data to the server.

[1107] Output: Personal information received on the server side.

[1108] 4. The server creates the account:

[1109] Input: Encrypted personal information received by the server.

[1110] How it works: The server records the information in a database and creates an account for the user.

[1111] Output: The account is registered in the database.

[1112] Step 2:

[1113] Language model selection

[1114] 1. The user selects a language model:

[1115] Input: The user selects the language model they want to use (GPT-4, BLOOM, LLaMA, etc.).

[1116] What it does: The application retrieves the ID of the selected model.

[1117] Output: The selected language model ID is generated.

[1118] 2. The device sends the model selection information to the server:

[1119] Input: The selected language model ID.

[1120] Operation: The terminal sends model selection information to the server.

[1121] Output: Model selection information received on the server.

[1122] 3. The server records the selection:

[1123] Input: The language model ID received by the server.

[1124] Operation: The server records the model selection information in a database.

[1125] Output: Model selection information is persisted in a database.

[1126] Step 3:

[1127] Establishing communication

[1128] 1. The device establishes communication with the server:

[1129] Input: The terminal sends a request to start communication.

[1130] Operation: The device starts communication using a VoLTE or VoNR line.

[1131] Output: The request arrives at the server.

[1132] 2. The server authenticates the device and verifies the encrypted communication:

[1133] Input: A communication connection request from the device.

[1134] How it works: The server verifies the device's identity and checks the status of the encrypted communication.

[1135] Output: Communication is authenticated and cryptographic verification is complete.

[1136] 3. Initiating secure data transmission:

[1137] Input: An authenticated and encrypted communications connection.

[1138] How it works: Secure data transmission begins between the device and the server.

[1139] Output: Ready for data transmission.

[1140] Step 4:

[1141] User Input and Emotion Recognition

[1142] 1. User enters text:

[1143] Input: The user types a question or request into a text box.

[1144] What it does: Enter a prompt sentence into the text box, such as "How are you feeling today?"

[1145] Output: The input text data is generated.

[1146] 2. The device sends the text to the server:

[1147] Input: The text data entered.

[1148] How it works: The device sends text data to the server in real time.

[1149] Output: Text data arrives at the server.

[1150] 3. The emotion engine analyzes the text and recognizes emotions:

[1151] Input: User's text data.

[1152] How it works: The emotion engine analyzes the text and recognizes the user's emotional state (happy, sad, angry, etc.).

[1153] Output: Emotional information is generated.

[1154] 4. The device sends the emotion information to the server:

[1155] Input: Emotion information.

[1156] Operation: The device sends emotion information to the server.

[1157] Output: Emotion information arrives at the server.

[1158] Step 5:

[1159] Response Generation

[1160] 1. The server analyzes the user's input and emotional information:

[1161] Input: User's text data and emotion information.

[1162] How it works: The server parses both pieces of data.

[1163] Output: The analysis results are generated.

[1164] 2. The server sends a request to the language model:

[1165] Input: Analysis results.

[1166] How it works: The server sends the appropriate request to the language model.

[1167] Output: The request arrives at the language model.

[1168] 3. The language model generates a response:

[1169] Input: The request sent to the language model.

[1170] How it works: The language model generates a response based on the analysis.

[1171] Output: A response such as "You look great today, have a great day!" is generated.

[1172] 4. The server generates a response and sends it to the device:

[1173] Input: The generated response.

[1174] Action: The server sends a response to the device.

[1175] Output: The response arrives on the terminal.

[1176] Step 6:

[1177] Viewing the response

[1178] 1. Parse the response received by the device:

[1179] Input: The response sent by the server.

[1180] Action: The device analyzes the response data.

[1181] Output: The parsed response data is generated.

[1182] 2. The terminal displays the response to the user:

[1183] Input: Parsed response data.

[1184] Behavior: Displays a response message on the screen.

[1185] Output: The user will be able to see the response on the screen.

[1186] Step 7:

[1187] Session End

[1188] 1. User ends conversation session:

[1189] Input: The user presses the exit button.

[1190] Behavior: A click event on the Exit button occurs.

[1191] Output: A session termination request is generated.

[1192] 2. The device sends a session termination request to the server:

[1193] Input: Session termination request.

[1194] Operation: The device sends a request to the server.

[1195] Output: The request arrives at the server.

[1196] 3. The server ends the session and disconnects:

[1197] Input: Session termination request.

[1198] Action: The server ends the session and disconnects.

[1199] Output: The connection is dropped and the session is terminated.

[1200] 4. The terminal shuts down and notifies the user:

[1201] Input: Communication is lost.

[1202] Behavior: The application performs termination processing and notifies the user of the end of the session.

[1203] Output: The user can confirm the session has ended.

[1204] (Application example 2)

[1205] 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."

[1206] Conventional food delivery systems have difficulty providing customized services based on a user's emotional state. Furthermore, when a user is feeling stressed or tired, they are unable to appropriately suggest meals or special offers that correspond to that state. As a result, users' satisfaction declines and they tend to use the service less frequently. Therefore, the present invention aims to build a system that incorporates user emotion recognition to provide a customized food delivery service that corresponds to the user's emotional state, thereby improving user satisfaction.

[1207] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1208] means for recognizing a user's emotion and generating and modifying an appropriate response based on the recognized emotion;

[1209] A means for suggesting additional services or products customized to the user based on the perceived emotions;

[1210] A means for users to review the proposed services and products and confirm their orders;

[1211] This makes it possible to propose appropriate services and products according to the user's emotional state.

[1212] Definitions of important words

[1213] An "account" is personal identification information created by a user to use the system.

[1214] A "language model" is an algorithm or program used in natural language processing to generate appropriate responses based on user input.

[1215] "Wireless communication line" refers to a data transmission means using wireless communication technology such as VoLTE or VoNR.

[1216] A "server" is a computer system responsible for receiving and analyzing user data, and generating and sending an appropriate response.

[1217] An "emotion engine" is an algorithm or software that analyzes and recognizes emotions from a user's text or voice input.

[1218] A "response" is a response or reaction generated in response to user input.

[1219] "Customized additional services and products" are services and products that are specifically suggested taking into account the user's emotional state.

[1220] "Confirming an order" means that the user has made clear their intention to purchase the proposed service or product, and the transaction is now complete.

[1221] "User authentication" is a procedure for verifying the legitimacy of a user accessing a system.

[1222] "Encryption" is a data conversion technology for securely transmitting communication data.

[1223] "Text input" refers to a string of characters that a user enters to communicate or give instructions to a system.

[1224] Description: Detailed Description of the Invention

[1225] This invention is a system for recognizing user emotions in a food delivery service and providing customized services to users based on that data. Specific implementation methods for realizing this system will be described.

[1226] Hardware and software used

[1227] Hardware: Smartphones, servers

[1228] Software: EmotionRecognitionAPI, FoodDeliveryAPI

[1229] Overall system overview

[1230] First, the user installs a dedicated application on their smartphone and creates an account. After launching the application, the user enters the necessary personal information and creates an account. When creating the account or later, the user selects the language model (for example, a general artificial intelligence model) they wish to use. This information is sent from the smartphone to the server, and the selected language model is recorded in the server's database.

[1231] Establishment and authentication of communication over VoLTE / VoNR lines

[1232] The smartphone establishes communication with the server using a VoLTE or VoNR connection. After communication is established, the server authenticates and authorizes the smartphone. It also ensures that the communication is encrypted. Once this is complete, secure data transmission begins.

[1233] User Input and Emotion Recognition

[1234] Users enter questions or requests into a text box within the app. For example, if a user types "I'm very tired today," the smartphone sends this data to the server in real time. At this time, the EmotionRecognition API analyzes the entered text and recognizes the user's emotional state (e.g., fatigue). The emotional information recognized by the emotion engine is also sent to the server.

[1235] Server response generation

[1236] The server analyzes the received user input and emotional information and sends a request to the selected language model. The language model generates an appropriate response based on the user input and emotional information. For example, the response generated might be, "Would you like some relaxing herbal tea and a light snack?" This response is then sent via the server to the smartphone.

[1237] Response display and order confirmation

[1238] The smartphone analyzes the response received from the server and displays it on the user's screen. The user checks the proposed services and products and decides whether to place an order. If the order is confirmed, the smartphone sends the order data to the server, which processes the order.

[1239] Specific prompt examples

[1240] "I'm very tired today"

[1241] "Is there a relaxation menu you would recommend?"

[1242] This will realize a system that provides food delivery services according to the user's emotional state. Users can receive services that match their emotional state, which is expected to improve their satisfaction.

[1243] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1244] Program processing steps

[1245] Step 1

[1246] User account creation

[1247] Input: User's personal information (name, contact details, etc.)

[1248] How it works: The device captures the personal information entered by the user to create an account.

[1249] Output: An account creation request is generated and sent to the server.

[1250] Data processing: Formatting user information and converting it into a data structure to send to the server.

[1251] Step 2

[1252] Language model selection

[1253] Input: Selection information for the language model you want to use (e.g., a general-purpose artificial intelligence model)

[1254] Operation: The device obtains information about the language model selected by the user.

[1255] Output: A language model selection request is sent to the server and the selection information is recorded in a database.

[1256] Data processing: The selected language model information is formatted and converted into a data structure for transmission to the server.

[1257] Step 3

[1258] Establishing communication

[1259] Input: Device connection information

[1260] Operation: The terminal starts communication with the server using a VoLTE or VoNR line.

[1261] Output: The server receives the connection request and establishes communication.

[1262] Data Computing: Encrypts and authenticates connections to ensure communications are secure.

[1263] Step 4

[1264] Accepting text input

[1265] Input: The text of the user's question or request (e.g., "I'm very tired today")

[1266] Action: The terminal captures the text entered by the user.

[1267] Output: The text input data is sent to the server.

[1268] Data processing: Converting user input into a data structure to be sent to the server in real time.

[1269] Step 5

[1270] Emotion Recognition and Analysis

[1271] Input: User text input data

[1272] How it works: The server uses the EmotionRecognition API to analyze the text and recognize the user's emotions.

[1273] Output: The recognized emotion information is generated and included in the request to the language model.

[1274] Data Computation: Perform text analysis and emotion recognition to extract emotional information.

[1275] Step 6

[1276] Generating a response

[1277] Input: User text input and emotional information

[1278] Operation: The server sends a request to the selected language model and generates an appropriate response.

[1279] Output: The generated response is sent to the terminal.

[1280] Data Computation: Uses language models to generate appropriate responses based on input data.

[1281] Step 7

[1282] Viewing the response

[1283] Input: Response data received from the server

[1284] Operation: The device analyzes the received response data and displays it on the user's screen.

[1285] Output: The user is shown an appropriate response.

[1286] Data processing: Converting response data into a format that can be displayed.

[1287] Step 8

[1288] Proposals for services and products

[1289] Input: Recognized emotion information and generated response

[1290] How it works: The server suggests customized services and products based on the user's emotional state.

[1291] Output: The proposed data is sent to the device.

[1292] Data calculation: Generates suggestion data based on emotional information and makes optimal suggestions to users.

[1293] Step 9

[1294] Confirming the order

[1295] Input: User confirmation and order intention

[1296] How it works: The terminal allows the user to review the proposed services and products and place the order.

[1297] Output: The order confirmation data is sent to the server and the transaction is completed.

[1298] Data processing: Order data is formatted and sent to the server for order processing.

[1299] Examples of prompt statements

[1300] "I'm very tired today"

[1301] "Is there a relaxation menu you would recommend?"

[1302] This enables system-wide processing, allowing users to enjoy a food delivery service that is customized according to their emotions.

[1303] 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.

[1304] 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.

[1305] 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.

[1306] [Fourth embodiment]

[1307] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1308] 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.

[1309] 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).

[1310] 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.

[1311] 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.

[1312] 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).

[1313] 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. 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.

[1314] 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.

[1315] 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.

[1316] 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.

[1317] 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.

[1318] 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.

[1319] 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."

[1320] ---

[1321] The following describes in detail the embodiments of the present invention. The present invention is a system that allows a user to select a language model and engage in high-quality conversation with an AI based on that selection. The user accesses the system using a terminal and establishes communication with a server via a VoLTE or VoNR line. The program processing of the present invention is performed as follows.

[1322] First, the user installs the application and creates an account. When creating an account, the user selects the language model they want to use from multiple language models (e.g., GPT-4, BLOOM, LLaMA, etc.). The device sends this information to the server, which then records the selected language model in a database.

[1323] Next, the device establishes communication with the server using a VoLTE or VoNR line. The server receives this connection, verifies that the communication is encrypted, and authenticates the user, ensuring that data transmitted between the device and the server is secure.

[1324] Next, the user enters a query within the application. For example, the user might enter, "What's the weather like today?" This input data is sent in real time by the device to the server. The server analyzes this input and sends a request to the language model. The server then invokes the processing of the language model previously selected by the user (in this case, GPT-4) to generate the appropriate response.

[1325] The generated response is then sent back to the device via the server. For example, if a response such as "The current weather in Tokyo is sunny," is generated, the device receives it and displays it to the user. In this way, the user can receive high-quality responses in real time.

[1326] If the user subsequently asks an additional question, for example, "What's the weather going to be like tomorrow?", the same process is repeated. The user's input is sent to the server via the device, and the server generates a response based on the language model and sends it back to the device. The device then displays the response to the user. Through this process, the user can enjoy a continuous conversation with the AI.

[1327] As described above, the present invention is a system that provides a high-quality communication environment and flexible language model selection, thereby offering users a satisfying conversation experience. Users can select the optimal language model based on their own goals and preferences, and receive real-time responses based on that selection. In this way, the present invention solves the problems of the past and provides an improved AI conversation service.

[1328] ---

[1329] The processing flow will be explained below.

[1330] Step 1:

[1331] The user installs the application. The user launches the application and is taken to an account creation screen. The user enters the required information (name, email address, password, etc.) and creates an account. After creating the account, the user selects the language model they want to use (e.g., GPT-4, BLOOM, LLaMA, etc.). The device sends information about the selected language model to the server. The server records this information in a database.

[1332] Step 2:

[1333] The device establishes communication with the server using a VoLTE or VoNR line. The server is notified that communication has begun. The server authenticates the connected device. The server verifies that the communication is encrypted. If authentication and encryption are successful, data transmission between the device and server begins.

[1334] Step 3:

[1335] The user enters a question or request into a text box within the application (e.g., "What's the current weather?"). The device sends the entered text data to the server in real time. The server receives the user's input.

[1336] Step 4:

[1337] The server analyzes the received user input. The analysis result is sent as a request to the selected language model (e.g., GPT-4). The language model generates an appropriate response to the user input. The response generated by the language model is sent back to the server.

[1338] Step 5:

[1339] The server receives the generated response and sends it to the device. The server confirms that the response has been sent. The device analyzes the response received from the server and displays it on the user's screen (e.g., "The current weather in Tokyo is sunny.").

[1340] Step 6:

[1341] If the user wants to enter an additional question or request, the process returns to step 3. For example, the user enters "What's the weather going to be like tomorrow?" The device again sends the input data to the server, and the process repeats.

[1342] Step 7:

[1343] When a user wants to end a conversation session, they press the end button in the app. The device sends a request to end the session to the server. The server ends the session and disconnects the connection. The device then performs the termination process and notifies the user that the session has ended.

[1344] This allows users to enjoy real-time conversations with AI using the language model of their choice in a high-quality communication environment.

[1345] Example 1

[1346] 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."

[1347] Conventional AI conversation systems have issues such as limited language models that users can select and lack of guaranteed communication security. They also face challenges in generating responses to user input in real time and displaying accurate responses. There is a need to solve these issues and enable high-quality real-time conversations based on the language model selected by the user.

[1348] 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.

[1349] In this invention, the server includes means for analyzing user input data in real time and converting it into an appropriate format, means for sending the analyzed data as a request to a language model, and means for sending a generated response to the terminal, thereby enabling responses to be generated in real time based on a language model selected by the user, and providing safe and high-quality AI conversations.

[1350] "User" refers to a person who uses a terminal to access the system, create an account, select a language model, and enter text.

[1351] "Terminal" refers to a device operated by a user, such as a smartphone, PC, or tablet, that communicates with a server and has the ability to display user input and received responses.

[1352] "Communication line" refers to a network infrastructure such as the Internet or a mobile network used to transmit and receive data between a terminal and a central processing unit (server).

[1353] "Central Processing Unit" refers to a server that analyzes a user's input, generates a response based on an appropriate language model, and transmits the generated response to the terminal.

[1354] "User authentication" refers to the process of verifying a user's identity and ensuring legitimate access to a system.

[1355] "Encryption" refers to the technology of encrypting data to protect it from unauthorized access in order to transmit it safely.

[1356] "Text input" refers to strings of data that a user sends to a system using an interface on a terminal.

[1357] "Language model" refers to AI technologies used to generate appropriate responses to user text input, including GPT-4, BLOOM, and LLaMA.

[1358] "Real-time analytics" refers to the process of instantly analyzing user input data, converting it into an appropriate format, and generating a response.

[1359] A "request" refers to a data transmission that requests a language model to generate a response based on the results of analyzing the user's input data.

[1360] "Generated response" refers to text data generated by a language model based on a user's request.

[1361] The following describes in detail the mode for carrying out the present invention. This invention is a system that allows a user to select from multiple language models and engage in high-quality conversations with an AI based on that selection. The configuration and operation of the system are described below.

[1362] System configuration:

[1363] The system consists of the following elements:

[1364] 1. User device: A smartphone, PC, tablet, etc. operated by a user. An application is installed on this device.

[1365] 2. Server: A high-performance cloud server (e.g., AWS, Google Cloud) that analyzes user input and generates responses using a language model.

[1366] 3. Communication line: Data communication between the terminal and the server is carried out using network infrastructure such as VoLTE or VoNR.

[1367] Software used:

[1368] User application: iOS or Android app, or web app

[1369] Language models: AI models such as GPT-4, BLOOM, and LLaMA

[1370] Communication protocol: VoLTE, VoNR

[1371] How it works:

[1372] 1. Create an account and select a model:

[1373] The user installs and launches the application. They enter the required information (email address, password, etc.) on the account creation screen. The device sends this information to the server, which records it in a database.

[1374] After creating an account, users select a language model to use from multiple models (GPT-4, BLOOM, LLaMA, etc.). The device sends the selection information to the server, which records it in a database.

[1375] 2. Communication establishment and authentication:

[1376] The device establishes communication with the server using a VoLTE or VoNR line. The server accepts the connection, verifies that the communication is encrypted, and then authenticates the user. If authentication is successful, communication continues.

[1377] 3. User input and response generation:

[1378] The user enters a query into a chat window within the application, for example, "What's the weather like now?"

[1379] The device sends this input data in real time to the server, which analyzes the input data and sends requests to the language model.

[1380] The language model (e.g., GPT-4) generates an appropriate response and returns the generated response data to the server, which then sends the response data to the device, which then displays the response to the user.

[1381] Specific working example:

[1382] For example, consider the case where a user asks a question about the weather. The user types, "What's the weather like in Tokyo tomorrow?" into the application's chat window. The device captures this input data and immediately sends it to the server. The server analyzes the input data and sends a request to a language model (such as GPT-4). The language model generates a response such as, "Tomorrow's weather in Tokyo will be sunny with occasional cloudy weather." The server then sends the generated response data to the device, which then displays the response to the user. Through this process, the user can enjoy a flexible AI conversation in real time.

[1383] In this way, the present invention is a system that enables high-quality conversation in real time based on a language model selected by the user, and solves the problems of the prior art.

[1384] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1385] Step 1: Create a user account

[1386] The user installs and launches the application. They enter the required information (email address, password, etc.) on the account creation screen. This is the input. The device sends this account information to the server. The server records the received account information in a database and sends a confirmation email to the user. This is the output.

[1387] Step 2: Language model selection

[1388] After creating an account, the user selects the language model (GPT-4, BLOOM, LLaMA, etc.) to use on the app's settings screen. This is the input. The device sends information about the selected language model to the server. The server associates the selection information with the user's account information and records it in a database. This is the output.

[1389] Step 3: Establishing communication and authenticating

[1390] The device establishes communication with the server using a VoLTE or VoNR line. This is the input. The server receives the connection and verifies that the communication is encrypted. It then authenticates the user. If authentication is successful, communication is established. This is the output.

[1391] Step 4: User input

[1392] The user types a query into a chat window within the application (e.g., "What's the weather like now?"). This is the input. The device captures the user's input data in real time. The captured data becomes the input for the next step.

[1393] Step 5: Submitting input data

[1394] The device sends the captured input data to the server. This is the input. The server prepares the received data for analysis. The input data is converted into an analyzable format. This is the output.

[1395] Step 6: Analyzing the inquiry

[1396] The server parses the input data it receives and converts it into an appropriate format. This is the input. Natural Language Processing (NLP) techniques are used in this parsing step. The parsed data becomes the input for the next step. This is the output.

[1397] Step 7: Requesting a Language Model

[1398] The server sends the parsed data as a request to the user's chosen language model, which is the input. The language model generates an appropriate response based on the input data, which is the output.

[1399] Step 8: Response Generation

[1400] The language model generates a response and returns that data to the server, which is the input. The server then prepares the received response data to send to the user's device, which is the output.

[1401] Step 9: Send and view the response

[1402] The server sends the response data to the terminal. This is the input. The terminal displays the received response data to the user. This is the output. The user can check the displayed response and enter the next query (e.g., "What's the weather like tomorrow?"), which will cause the same process to start again from step 4.

[1403] (Application example 1)

[1404] 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."

[1405] In today's electronic payment services, users lack the means to quickly and accurately obtain information about new payment methods and questions. This often leaves users feeling anxious and frustrated when making payments, resulting in a poor customer experience. The present invention aims to solve these user issues and provide a smoother and more satisfying electronic payment experience.

[1406] 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.

[1407] In this invention, the server includes means for a user to create an account and select a language model, means for a terminal to establish communication using a VoLTE or VoNR line and confirm user authentication and encryption, means for transmitting the user's text input to the server in real time, means for generating appropriate responses based on the language model to the user's questions regarding payment methods and electronic payments, and means for transmitting these responses from the server to the terminal and displaying the responses received by the terminal to the user, thereby enabling the user to obtain high-quality information regarding electronic payments in real time.

[1408] "Means for creating an account" refers to the function that allows a user to register with the system and set individual user information.

[1409] "Means for selecting a language model" is a function that allows users to select the model to use from multiple AI language models.

[1410] "Means for the terminal to establish communication with the server using a VoLTE or VoNR line" refers to the ability of the user's device to establish a connection with the server using communications technology that enables high-quality voice and data communications.

[1411] "User authentication and encryption verification measures" are procedures used to verify the legitimacy of users and ensure data privacy and security.

[1412] "Means for receiving text input from the user and sending it to the server" is a function for obtaining text data input by the user into the terminal and sending it to the server.

[1413] The "means for the server to send the user's input to the language model and generate a response" is the function by which the server sends the text received from the user to the selected language model and generates an appropriate answer.

[1414] The "means for the server to send the generated response to the terminal and for the terminal to display the received response to the user" is a function for sending a text response generated by the server to the user's terminal and displaying the response on the terminal.

[1415] "Means for generating appropriate responses in real time to user questions about payment methods and electronic payments" is a function for providing immediate and appropriate answers to user questions about electronic payments.

[1416] The present invention is a system that allows users to obtain information in real time about questions about electronic payments and new payment methods. A specific embodiment of this system will be described below.

[1417] The system allows users to create an account using a device such as a smartphone or smart glasses, and then select from multiple language models. The selected language model is then used to generate high-quality responses for electronic payments in real time.

[1418] Hardware and software used

[1419] Devices: Smartphones (iOS, Android), smart glasses (e.g., Google Glass)

[1420] Communication infrastructure: VoLTE, VoNR lines

[1421] Server: Cloud server (e.g. AWS, Google Cloud)

[1422] Language Model API: Generative AI models (e.g., OpenAI's GPT-4, BLOOM, Meta's LLaMA)

[1423] Specific details of data processing and data calculation

[1424] 1. Create an account and select a model:

[1425] A user installs the application and creates an account. At that time, they select the language model they want to use from multiple models (GPT-4, BLOOM, LLaMA, etc.). This information is sent from the device to the server and recorded in a database on the server.

[1426] 2. Communication establishment and encryption:

[1427] The device establishes communication with the server using a VoLTE or VoNR line. The server then verifies that the communication is encrypted and authenticates the user. This communication method maintains high security and privacy.

[1428] 3. User input and response generation:

[1429] A user types a question about electronic payments within an application, such as "What points can I use to pay at this store?" This text data is sent in real time by the device to a server. The server analyzes the input and sends a request to a language model previously selected by the user. The language model generates an appropriate response and returns it to the server.

[1430] 4. View the response:

[1431] The server generates a response, which is sent to the terminal and displayed to the user, for example, "This store accepts points for payment." If the user then asks a follow-up question, for example, "What are some other payment methods?", the process is repeated.

[1432] Examples of prompt statements

[1433] Here are some example prompts that can be used when a user enters a question about an electronic payment:

[1434] User input: "What is your most recent payment method?"

[1435] GPT-4 prompt: “User wants the latest information on electronic payments. In particular, please provide details about new payment methods and their availability in stores.”

[1436] In this way, the system allows users to get real-time answers to questions and information about electronic payments instantly, providing a smoother and more satisfying experience.

[1437] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1438] Step 1:

[1439] A user creates an account and selects a language model. Input includes a username, password, and the selected language model (e.g., GPT-4, BLOOM, LLaMA). The device collects this information and sends it to the server, which records it in a database.

[1440] Step 2:

[1441] The device establishes communication with the server using a VoLTE or VoNR line. Input includes the user's device information and authentication information. The server confirms that communication has been established and that the transmitted data is encrypted. The user's authentication information is verified to ensure the security of the communication.

[1442] Step 3:

[1443] The user inputs a question about electronic payment within the application. For example, a question such as "How many points can I use to pay at this store?" This text data is collected by the terminal and sent to the server in real time. The input includes the user's text data.

[1444] Step 4:

[1445] The server analyzes the received text data, generates prompts against a pre-selected language model, and sends the request. The input includes the user's question and the selected language model. The language model (e.g., GPT-4) generates an appropriate response based on the request. The output is the appropriate response.

[1446] Step 5:

[1447] The server receives the response generated from the language model and sends it to the device. The input includes the generated response. The output includes the data to be sent to the device, allowing the user to receive the response on the device.

[1448] Step 6:

[1449] The terminal displays the response received from the server to the user. The response data sent from the server is included as input. Specifically, a response such as "Points can be used for payment at this store" is displayed. The user can check this information on the screen.

[1450] Step 7:

[1451] If the user subsequently asks a question (e.g., "What are the alternative payment methods?"), the process repeats, this time with the user's new question. The device again collects text data and sends it in real time to the server, where it is parsed, a language model generates a response, and the response is sent to the device for display.

[1452] The above is the specific processing flow of the program. This system allows users to receive high-quality responses in real time to questions about electronic payments.

[1453] 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.

[1454] ---

[1455] The present invention provides an AI conversation system incorporating an emotion engine that recognizes user emotions. The system includes means for a user to create an account and select a language model, means for establishing communication with a server using a VoLTE or VoNR line and verifying user authentication and encryption, means for receiving the user's text input and sending it to the server, means for the server to send the user's input to the language model and generate a response, means for the server to send the generated response to a terminal and means for the terminal to display the received response to the user, and means for recognizing the user's emotions using the emotion engine and generating or modifying an appropriate response based on the user's emotions.

[1456] First, the user installs a dedicated application and creates an account. After installation, the user is taken to an account creation screen and enters the necessary personal information to create the account. Next, the user selects the language model (e.g., GPT-4, BLOOM, LLaMA, etc.) they wish to use when creating the account or later. This information is sent from the device to the server, and the server records the selected language model in a database.

[1457] The device establishes communication with the server using a VoLTE or VoNR connection. Once communication is established, the server authenticates and authorizes the connected device. The server then verifies that the communication is encrypted. Once this verification is complete, secure data transmission between the device and the server begins.

[1458] Users input questions or requests into a text box within the app. For example, if a user types "How are you feeling today?", the device sends this data to the server in real time. At the same time, the emotion engine analyzes the user's input and recognizes the user's emotional state (e.g., joy, sadness, anger, etc.). The emotional information recognized by the emotion engine is also sent to the server.

[1459] The server analyzes the received user input and emotional information and sends a request to the selected language model. The language model generates a more appropriate response based on the user input and emotional information. For example, the response generated might be, "You look great today, have a great day!" This response is then sent to the device via the server.

[1460] The device analyzes the response received from the server and displays it on the user's screen, allowing the user to receive a response that reflects their emotional state. If the user wants to ask a follow-up question, such as "What are your plans for tomorrow?", the same process is repeated.

[1461] When a user wants to end a conversation session, they press the end button in the app. The device sends a request to end the session to the server, and the server ends the session and disconnects the communication. The device then performs the termination process and notifies the user that the session has ended.

[1462] In this way, by incorporating an emotion engine, the present invention is a system that can provide more appropriate and high-quality responses according to the user's emotional state, thereby resolving conventional problems and providing users with a satisfying conversation experience.

[1463] ---

[1464] The processing flow will be explained below.

[1465] Step 1:

[1466] The user installs the dedicated application. The user launches the application and moves to the account creation screen. The user enters the required information such as name, email address, and password to create an account.

[1467] Step 2:

[1468] After creating an account, the user selects the language model they want to use (e.g., GPT-4, BLOOM, LLaMA, etc.). The device sends information about the selected language model to the server, which then records this information in a database.

[1469] Step 3:

[1470] The device establishes communication with the server using a VoLTE or VoNR line and notifies the server that communication has begun.

[1471] Step 4:

[1472] The server authenticates the connected device. The server verifies that the communication is encrypted. Once authentication and encryption are successful, data transmission between the device and the server begins.

[1473] Step 5:

[1474] The user types a question or request into a text box within the application (e.g., "How are you feeling today?"). The device sends the text data in real time to the server. In parallel, the device analyzes the input and runs an emotion engine to recognize the user's emotions.

[1475] Step 6:

[1476] The device sends the emotional information analyzed by the emotion engine to the server. The emotion engine identifies the user's emotion (e.g., joy, sadness, anger, etc.). This emotional information is also sent to the server.

[1477] Step 7:

[1478] The server receives and analyzes the user's input and emotion information. The server sends a request to the selected language model. The language model generates a response based on the user's input and emotion information.

[1479] Step 8:

[1480] The server receives the response generated from the language model and sends it to the terminal. The server then confirms that the response has been sent.

[1481] Step 9:

[1482] The device parses the response received from the server and displays it on the user's screen. For example, if the user asks, "How are you feeling today?", the language model generates a response such as, "You look great today, have a great day!", which the device displays.

[1483] Step 10:

[1484] If the user wants to enter an additional question or request, the process returns to step 5. For example, the user enters, "What are your plans for tomorrow?" The device again sends the input data and emotion information to the server, and the same process is repeated.

[1485] Step 11:

[1486] When a user wants to end a conversation session, they press the end button in the app. The device sends a request to end the session to the server. The server ends the session and disconnects the connection. The device then performs the termination process and notifies the user that the session has ended.

[1487] In this way, incorporating an emotion engine allows the system to provide more appropriate and higher quality responses based on the user's emotional state, resulting in a more satisfying conversational experience for the user.

[1488] Example 2

[1489] 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."

[1490] Conventional AI conversation systems generate uniform responses without considering the user's emotions, making it difficult to improve the individual user experience. For this reason, there is a need for a system that can provide more natural and satisfying conversations that respond to the user's emotions.

[1491] 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.

[1492] In this invention, the server includes means for a user to create an account and select a language model, means for a terminal to establish communication with the server using a mobile communication environment and confirm user authentication and encryption, means for receiving a text input from the user and sending it to the server, means for the server to send the user's input to the language model and generate a response, means for the server to send the generated response to the terminal and display the response received by the terminal to the user, means for recognizing the user's emotional state using an emotion engine and sending that information to the server, and means for the server to modify and generate a response based on the emotion information, thereby making it possible to provide a more appropriate and satisfying response that corresponds to the user's emotions.

[1493] "Account" means the personal registration information required for a User to access the System.

[1494] A "language model" is an artificial intelligence-based algorithm or program for generating responses based on a user's text input.

[1495] A "mobile communication environment" is a network that uses wireless communication technologies such as VoLTE and VoNR.

[1496] "User authentication" is the process by which a system verifies a user's identity.

[1497] "Encryption" is the technique of converting plaintext into ciphertext in order to protect data.

[1498] "Text input" refers to the act of a user inputting text into a system.

[1499] An "emotion engine" is a system that analyzes and recognizes the emotional state from the user's input.

[1500] A "response" is a reply message generated in response to a user's text input.

[1501] A "server" is a computer system or network that centrally manages data processing and storage.

[1502] "Terminal" refers to the device used by the user (smartphone, PC, etc.).

[1503] The present invention provides an AI conversation system incorporating an emotion engine that recognizes user emotions. The system includes means for a user to create an account and select a language model, means for establishing communication with a server using a mobile communication environment and confirming user authentication and encryption, means for receiving the user's text input and sending it to the server, means for the server to send the user's input to the language model and generate a response, means for the server to send the generated response to a terminal and means for the terminal to display the received response to the user, and means for recognizing the user's emotions using the emotion engine and generating and modifying an appropriate response based on the user's emotions.

[1504] Hardware and Software Configuration

[1505] An implementation of this system includes the following hardware and software:

[1506] Device: Smartphone, PC, or other device

[1507] Server: A computer system that centrally manages data processing and storage.

[1508] Mobile communication environment: Wireless communication technologies such as VoLTE and VoNR

[1509] Dedicated applications: User-installed apps

[1510] Language models: Artificial intelligence-based algorithms such as GPT-4, BLOOM, and LLaMA

[1511] Emotion Engine: A system that analyzes the emotional state from user input

[1512] System Operation

[1513] First, the user installs a dedicated application and creates an account. After installation, the user is taken to an account creation screen and enters the necessary personal information (name, email address, password, etc.) to create an account. Next, the user selects the language model they want to use (e.g., GPT-4, BLOOM, LLaMA, etc.). This information is sent from the device to the server, and the server records the selected language model in a database.

[1514] The device establishes communication with the server using a VoLTE or VoNR line. Once communication is established, the server authenticates the connected device and verifies encrypted communication. After this verification is complete, secure data transmission between the device and the server begins.

[1515] Users input questions or requests into a text box within the app. For example, if they type "How are you feeling today?", the device sends this data to the server in real time. At the same time, the emotion engine analyzes the user's input and recognizes the user's emotional state (e.g., joy, sadness, anger, etc.). The analyzed emotion information is also sent from the device to the server.

[1516] The server analyzes the received user input and emotional information and sends a request to the selected language model. The language model generates a more appropriate response based on the user input and emotional information. For example, the response generated might be, "You look great today, have a great day!" This response is then sent to the device via the server.

[1517] The device analyzes the response received from the server and displays it on the user's screen, allowing the user to receive a response that reflects their emotional state. If the user has additional questions, for example, "What are your plans for tomorrow?", the same process is repeated.

[1518] When a user wants to end a conversation session, they press the end button in the app, and the device sends a session termination request to the server. The server ends the session and disconnects the connection. The device then performs the termination process and notifies the user of the end of the session.

[1519] In this way, the present invention provides a system that can provide more appropriate and high-quality responses according to the user's emotional state, thereby resolving the problems of the past and providing the user with a satisfying conversation experience.

[1520] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1521] Step 1:

[1522] Installing the application and creating an account

[1523] 1. User installs the application:

[1524] Input: The user downloads a dedicated application onto their smartphone or PC.

[1525] How it works: The application is downloaded over the internet and installed on your device.

[1526] Output: The application is installed on the device and its icon is displayed.

[1527] 2. User creates an account:

[1528] Input: A user enters personal information into an application, such as their name, email address, or password.

[1529] How it works: The information you enter is encrypted on the device.

[1530] Output: Encrypted personal information is generated.

[1531] 3. The device sends the information to the server:

[1532] Input: Encrypted personal information.

[1533] Operation: The terminal uses a communication line to send data to the server.

[1534] Output: Personal information received on the server side.

[1535] 4. The server creates the account:

[1536] Input: Encrypted personal information received by the server.

[1537] How it works: The server records the information in a database and creates an account for the user.

[1538] Output: The account is registered in the database.

[1539] Step 2:

[1540] Language model selection

[1541] 1. The user selects a language model:

[1542] Input: The user selects the language model they want to use (GPT-4, BLOOM, LLaMA, etc.).

[1543] What it does: The application retrieves the ID of the selected model.

[1544] Output: The selected language model ID is generated.

[1545] 2. The device sends the model selection information to the server:

[1546] Input: The selected language model ID.

[1547] Operation: The terminal sends model selection information to the server.

[1548] Output: Model selection information received on the server.

[1549] 3. The server records the selection:

[1550] Input: The language model ID received by the server.

[1551] Operation: The server records the model selection information in a database.

[1552] Output: Model selection information is persisted in a database.

[1553] Step 3:

[1554] Establishing communication

[1555] 1. The device establishes communication with the server:

[1556] Input: The terminal sends a request to start communication.

[1557] Operation: The device starts communication using a VoLTE or VoNR line.

[1558] Output: The request arrives at the server.

[1559] 2. The server authenticates the device and verifies the encrypted communication:

[1560] Input: A communication connection request from the device.

[1561] How it works: The server verifies the device's identity and checks the status of the encrypted communication.

[1562] Output: Communication is authenticated and cryptographic verification is complete.

[1563] 3. Initiating secure data transmission:

[1564] Input: An authenticated and encrypted communications connection.

[1565] How it works: Secure data transmission begins between the device and the server.

[1566] Output: Ready for data transmission.

[1567] Step 4:

[1568] User Input and Emotion Recognition

[1569] 1. User enters text:

[1570] Input: The user types a question or request into a text box.

[1571] What it does: Enter a prompt sentence into the text box, such as "How are you feeling today?"

[1572] Output: The input text data is generated.

[1573] 2. The device sends the text to the server:

[1574] Input: The text data entered.

[1575] How it works: The device sends text data to the server in real time.

[1576] Output: Text data arrives at the server.

[1577] 3. The emotion engine analyzes the text and recognizes emotions:

[1578] Input: User's text data.

[1579] How it works: The emotion engine analyzes the text and recognizes the user's emotional state (happy, sad, angry, etc.).

[1580] Output: Emotional information is generated.

[1581] 4. The device sends the emotion information to the server:

[1582] Input: Emotion information.

[1583] Operation: The device sends emotion information to the server.

[1584] Output: Emotion information arrives at the server.

[1585] Step 5:

[1586] Response Generation

[1587] 1. The server analyzes the user's input and emotional information:

[1588] Input: User's text data and emotion information.

[1589] How it works: The server parses both pieces of data.

[1590] Output: The analysis results are generated.

[1591] 2. The server sends a request to the language model:

[1592] Input: Analysis results.

[1593] How it works: The server sends the appropriate request to the language model.

[1594] Output: The request arrives at the language model.

[1595] 3. The language model generates a response:

[1596] Input: The request sent to the language model.

[1597] How it works: The language model generates a response based on the analysis.

[1598] Output: A response such as "You look great today, have a great day!" is generated.

[1599] 4. The server generates a response and sends it to the device:

[1600] Input: The generated response.

[1601] Action: The server sends a response to the device.

[1602] Output: The response arrives on the terminal.

[1603] Step 6:

[1604] Viewing the response

[1605] 1. Parse the response received by the device:

[1606] Input: The response sent by the server.

[1607] Action: The device analyzes the response data.

[1608] Output: The parsed response data is generated.

[1609] 2. The terminal displays the response to the user:

[1610] Input: Parsed response data.

[1611] Behavior: Displays a response message on the screen.

[1612] Output: The user will be able to see the response on the screen.

[1613] Step 7:

[1614] Session End

[1615] 1. User ends conversation session:

[1616] Input: The user presses the exit button.

[1617] Behavior: A click event on the Exit button occurs.

[1618] Output: A session termination request is generated.

[1619] 2. The device sends a session termination request to the server:

[1620] Input: Session termination request.

[1621] Operation: The device sends a request to the server.

[1622] Output: The request arrives at the server.

[1623] 3. The server ends the session and disconnects:

[1624] Input: Session termination request.

[1625] Action: The server ends the session and disconnects.

[1626] Output: The connection is dropped and the session is terminated.

[1627] 4. The terminal shuts down and notifies the user:

[1628] Input: Communication is lost.

[1629] Behavior: The application performs termination processing and notifies the user of the end of the session.

[1630] Output: The user can confirm the session has ended.

[1631] (Application example 2)

[1632] 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."

[1633] Conventional food delivery systems have difficulty providing customized services based on a user's emotional state. Furthermore, when a user is feeling stressed or tired, they are unable to appropriately suggest meals or special offers that correspond to that state. As a result, users' satisfaction declines and they tend to use the service less frequently. Therefore, the present invention aims to build a system that incorporates user emotion recognition to provide a customized food delivery service that corresponds to the user's emotional state, thereby improving user satisfaction.

[1634] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1635] means for recognizing a user's emotion and generating and modifying an appropriate response based on the recognized emotion;

[1636] A means for suggesting additional services or products customized to the user based on the perceived emotions;

[1637] A means for users to review the proposed services and products and confirm their orders;

[1638] This makes it possible to propose appropriate services and products according to the user's emotional state.

[1639] Definitions of important words

[1640] An "account" is personal identification information created by a user to use the system.

[1641] A "language model" is an algorithm or program used in natural language processing to generate appropriate responses based on user input.

[1642] "Wireless communication line" refers to a data transmission means using wireless communication technology such as VoLTE or VoNR.

[1643] A "server" is a computer system responsible for receiving and analyzing user data, and generating and sending an appropriate response.

[1644] An "emotion engine" is an algorithm or software that analyzes and recognizes emotions from a user's text or voice input.

[1645] A "response" is a response or reaction generated in response to user input.

[1646] "Customized additional services and products" are services and products that are specifically suggested taking into account the user's emotional state.

[1647] "Confirming an order" means that the user has made clear their intention to purchase the proposed service or product, and the transaction is now complete.

[1648] "User authentication" is a procedure for verifying the legitimacy of a user accessing a system.

[1649] "Encryption" is a data conversion technology for securely transmitting communication data.

[1650] "Text input" refers to a string of characters that a user enters to communicate or give instructions to a system.

[1651] Description: Detailed Description of the Invention

[1652] This invention is a system for recognizing user emotions in a food delivery service and providing customized services to users based on that data. Specific implementation methods for realizing this system will be described.

[1653] Hardware and software used

[1654] Hardware: Smartphones, servers

[1655] Software: EmotionRecognitionAPI, FoodDeliveryAPI

[1656] Overall system overview

[1657] First, the user installs a dedicated application on their smartphone and creates an account. After launching the application, the user enters the necessary personal information and creates an account. When creating the account or later, the user selects the language model (for example, a general artificial intelligence model) they wish to use. This information is sent from the smartphone to the server, and the selected language model is recorded in the server's database.

[1658] Establishment and authentication of communication over VoLTE / VoNR lines

[1659] The smartphone establishes communication with the server using a VoLTE or VoNR connection. After communication is established, the server authenticates and authorizes the smartphone. It also ensures that the communication is encrypted. Once this is complete, secure data transmission begins.

[1660] User Input and Emotion Recognition

[1661] Users enter questions or requests into a text box within the app. For example, if a user types "I'm very tired today," the smartphone sends this data to the server in real time. At this time, the EmotionRecognition API analyzes the entered text and recognizes the user's emotional state (e.g., fatigue). The emotional information recognized by the emotion engine is also sent to the server.

[1662] Server response generation

[1663] The server analyzes the received user input and emotional information and sends a request to the selected language model. The language model generates an appropriate response based on the user input and emotional information. For example, the response generated might be, "Would you like some relaxing herbal tea and a light snack?" This response is then sent via the server to the smartphone.

[1664] Response display and order confirmation

[1665] The smartphone analyzes the response received from the server and displays it on the user's screen. The user checks the proposed services and products and decides whether to place an order. If the order is confirmed, the smartphone sends the order data to the server, which processes the order.

[1666] Specific prompt examples

[1667] "I'm very tired today"

[1668] "Is there a relaxation menu you would recommend?"

[1669] This will realize a system that provides food delivery services according to the user's emotional state. Users can receive services that match their emotional state, which is expected to improve their satisfaction.

[1670] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1671] Program processing steps

[1672] Step 1

[1673] User account creation

[1674] Input: User's personal information (name, contact details, etc.)

[1675] How it works: The device captures the personal information entered by the user to create an account.

[1676] Output: An account creation request is generated and sent to the server.

[1677] Data processing: Formatting user information and converting it into a data structure to send to the server.

[1678] Step 2

[1679] Language model selection

[1680] Input: Selection information for the language model you want to use (e.g., a general-purpose artificial intelligence model)

[1681] Operation: The device obtains information about the language model selected by the user.

[1682] Output: A language model selection request is sent to the server and the selection information is recorded in a database.

[1683] Data processing: The selected language model information is formatted and converted into a data structure for transmission to the server.

[1684] Step 3

[1685] Establishing communication

[1686] Input: Device connection information

[1687] Operation: The terminal starts communication with the server using a VoLTE or VoNR line.

[1688] Output: The server receives the connection request and establishes communication.

[1689] Data Computing: Encrypts and authenticates connections to ensure communications are secure.

[1690] Step 4

[1691] Accepting text input

[1692] Input: The text of the user's question or request (e.g., "I'm very tired today")

[1693] Action: The terminal captures the text entered by the user.

[1694] Output: The text input data is sent to the server.

[1695] Data processing: Converting user input into a data structure to be sent to the server in real time.

[1696] Step 5

[1697] Emotion Recognition and Analysis

[1698] Input: User text input data

[1699] How it works: The server uses the EmotionRecognition API to analyze the text and recognize the user's emotions.

[1700] Output: The recognized emotion information is generated and included in the request to the language model.

[1701] Data Computation: Perform text analysis and emotion recognition to extract emotional information.

[1702] Step 6

[1703] Generating a response

[1704] Input: User text input and emotional information

[1705] Operation: The server sends a request to the selected language model and generates an appropriate response.

[1706] Output: The generated response is sent to the terminal.

[1707] Data Computation: Uses language models to generate appropriate responses based on input data.

[1708] Step 7

[1709] Viewing the response

[1710] Input: Response data received from the server

[1711] Operation: The device analyzes the received response data and displays it on the user's screen.

[1712] Output: The user is shown an appropriate response.

[1713] Data processing: Converting response data into a format that can be displayed.

[1714] Step 8

[1715] Proposals for services and products

[1716] Input: Recognized emotion information and generated response

[1717] How it works: The server suggests customized services and products based on the user's emotional state.

[1718] Output: The proposed data is sent to the device.

[1719] Data calculation: Generates suggestion data based on emotional information and makes optimal suggestions to users.

[1720] Step 9

[1721] Confirming the order

[1722] Input: User confirmation and order intention

[1723] How it works: The terminal allows the user to review the proposed services and products and place the order.

[1724] Output: The order confirmation data is sent to the server and the transaction is completed.

[1725] Data processing: Order data is formatted and sent to the server for order processing.

[1726] Examples of prompt statements

[1727] "I'm very tired today"

[1728] "Is there a relaxation menu you would recommend?"

[1729] This enables system-wide processing, allowing users to enjoy a food delivery service that is customized according to their emotions.

[1730] 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.

[1731] 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.

[1732] 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.

[1733] 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.

[1734] FIG. 9 illustrates 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 behaviors 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.

[1735] 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.

[1736] 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).

[1737] 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.

[1738] 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."

[1739] 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.

[1740] 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).

[1741] 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.

[1742] 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.

[1743] 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.

[1744] 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.

[1745] 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.

[1746] 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.

[1747] 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.

[1748] 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.

[1749] 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.

[1750] 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.

[1751] The following is further disclosed regarding the above embodiment.

[1752] (Claim 1)

[1753] A means for users to create an account and select a language model;

[1754] A means for the device to establish communication with the server over a VoLTE or VoNR line and verify user authentication and encryption;

[1755] means for receiving user text input and transmitting it to a server;

[1756] means for the server to send the user's input to the language model and generate a response;

[1757] means for the server to send the generated response to the terminal and for the terminal to display the received response to the user;

[1758] A system including:

[1759] (Claim 2)

[1760] 2. The system according to claim 1, wherein a user can select a language model to use from a plurality of language models after creating an account.

[1761] (Claim 3)

[1762] 2. The system according to claim 1, further comprising means for allowing the user to input additional questions when the terminal displays the received responses and for transmitting the questions again to the server.

[1763] "Example 1"

[1764] (Claim 1)

[1765] A means for users to create an account and select a language model;

[1766] means for the terminal to establish communication with the central processor over a communication line and verify user authentication and encryption;

[1767] means for receiving and transmitting user text input to a central processing unit;

[1768] means for the central processing unit to send user input to the language model to generate a response;

[1769] means for the central processing unit to send the generated response to the terminal and for the terminal to display the received response to the user;

[1770] A means to analyze user input data in real time and convert it into an appropriate format;

[1771] means for transmitting the parsed data as a request to a language model;

[1772] A system including:

[1773] (Claim 2)

[1774] The system of claim 1, wherein a user can select a language model to use from multiple language models after creating an account, and generates responses based on data analyzed in real time.

[1775] (Claim 3)

[1776] 2. The system of claim 1, further comprising means for allowing the user to input additional questions when the terminal displays the received responses and for transmitting the questions again to the central processing unit.

[1777] "Application Example 1"

[1778] (Claim 1)

[1779] A means for users to create an account and select a language model;

[1780] A means for the device to establish communication with the server over a VoLTE or VoNR line and verify user authentication and encryption;

[1781] means for receiving user text input and transmitting it to a server;

[1782] means for the server to send the user's input to the language model and generate a response;

[1783] means for the server to send the generated response to the terminal and for the terminal to display the received response to the user;

[1784] A means of generating appropriate responses in real time to user questions regarding payment methods and electronic payments;

[1785] A system including:

[1786] (Claim 2)

[1787] 2. The system according to claim 1, wherein a user can select a language model to use from a plurality of language models after creating an account.

[1788] (Claim 3)

[1789] 2. The system according to claim 1, further comprising means for allowing the user to input additional questions when the terminal displays the received responses and for transmitting the questions again to the server.

[1790] "Example 2: Combining Emotion Engines"

[1791] (Claim 1)

[1792] A means for users to create an account and select a language model;

[1793] A means for the terminal to establish communication with the server using a mobile communication environment and verify user authentication and encryption;

[1794] means for receiving user text input and transmitting it to a server;

[1795] means for the server to send the user's input to the language model and generate a response;

[1796] means for the server to send the generated response to the terminal and for the terminal to display the received response to the user;

[1797] means for recognizing the user's emotional state using an emotion engine and transmitting that information to a server;

[1798] A means for the server to modify and generate a response based on the emotion information;

[1799] A system including:

[1800] (Claim 2)

[1801] 2. The system according to claim 1, wherein a user can select a language model to use from a plurality of language models after creating an account.

[1802] (Claim 3)

[1803] 2. The system according to claim 1, further comprising means for allowing the user to input additional questions when the terminal displays the received responses and for transmitting the questions again to the server.

[1804] "Application example 2 when combining emotion engines"

[1805] Claiming a new invention

[1806] (Claim 1)

[1807] A means for users to create an account and select a language model;

[1808] A means for the terminal to establish communication with a server using a wireless communication link and verify user authentication and encryption;

[1809] means for receiving user text input and transmitting it to a server;

[1810] means for the server to send the user's input to the language model and generate a response;

[1811] means for the server to send the generated response to the terminal and for the terminal to display the received response to the user;

[1812] a means for recognizing a user's emotion using an emotion engine and generating and modifying an appropriate response based on the recognized emotion;

[1813] A means for suggesting additional services or products customized to the user based on the perceived emotions;

[1814] A means for users to review the proposed services and products and confirm their orders;

[1815] A system including:

[1816] (Claim 2)

[1817] 2. The system according to claim 1, wherein a user can select a language model to use from a plurality of language models after creating an account.

[1818] (Claim 3)

[1819] 2. The system according to claim 1, further comprising means for allowing the user to input additional questions when the terminal displays the received responses and for transmitting the questions again to the server. [Explanation of symbols]

[1820] 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. A means for users to create an account and select a language model; A means for the device to establish communication with the server over a VoLTE or VoNR line and verify user authentication and encryption; means for receiving user text input and transmitting it to a server; means for the server to send the user's input to the language model and generate a response; means for the server to send the generated response to the terminal and for the terminal to display the received response to the user; A system including:

2. The system according to claim 1, wherein a user can select a language model to use from a plurality of language models after creating an account.

3. 2. The system of claim 1, further comprising means for allowing the user to input additional questions when the terminal displays the received responses and for transmitting the questions again to the server.

Images