System

Abstract

To provide a system.SOLUTION: A system includes: means for receiving a question about operation of an information processing device from a user; means for generating an answer to the received question; means for determining whether the generated answer is YES or NO; means for updating an interactive generative AI based on a determination result; means for providing a web-based interface for the user to input the question in a text form; means for inputting to the interactive generative AI a prompt sentence for instructing generation of the answer to the received question; means for transmitting the generated answer to a terminal of the user; and means for displaying the generated answer on the user terminal.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] There is a lack of information on how to operate smartphones, which can make it difficult for users to understand how to operate them. Also, different versions of smartphones have different operation methods, making it difficult for users to grasp the latest operation methods. [Means for solving the problem]

[0005] It receives questions from users about how to use their smartphones, generates answers to those questions, determines whether the generated answers are YES or NO, and updates the chat generation AI based on the results of that determination. This allows users to obtain the latest information on how to use their smartphones. [Brief explanation of the drawings]

[0006] [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. 2 is a sequence diagram showing a flow of processing in the data processing system according to the first embodiment of the first form example. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Embodiment 1. [Figure 13] FIG. 10 is a sequence diagram showing a processing flow of a data processing system in a second embodiment of the second form example. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Embodiment Example 2. [Figure 15] FIG. 10 is a sequence diagram showing the flow of processing in a data processing system according to a third embodiment of the third embodiment. [Figure 16]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Embodiment 3. [Figure 17] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the first embodiment of the first form example when an emotion engine is combined. [Figure 18] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Form Example 1 when an emotion engine is combined. [Figure 19] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the second embodiment of the second form example when an emotion engine is combined. [Figure 20] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Form Example 2 when an emotion engine is combined. [Figure 21] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the third embodiment of the third form example when an emotion engine is combined. [Figure 22] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Form Example 3 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0007] 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.

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

[0009] 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, the 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), an APU (Accelerated Processing Unit), or a TPU (TENSOR PROCESSING UNIT (registered trademark)).

[0010] 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.

[0011] 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.

[0012] 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.

[0013] 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."

[0014] [First embodiment]

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

[0016] 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.

[0017] 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).

[0018] 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.

[0019] 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.

[0020] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0021] 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.

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

[0023] 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.

[0024] 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.

[0025] 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.

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

[0027] "Example 1"

[0028] In one embodiment of the present invention, a web-based interface is provided as a means for receiving questions from users regarding smartphone operation. The interface is designed to allow users to input questions in text format. For example, if a user inputs the question, "How do I adjust the brightness of my smartphone screen?", the question is sent to the system.

[0029] "Example 2"

[0030] Next, a chat generation AI (Generative Pre-training Transformer) is used to generate answers to the received questions. Chat generation AI has the ability to learn from large amounts of text data and generate answers to questions in natural language. For example, in response to the above question, the chat generation AI would generate the answer, "You can adjust the screen brightness from the settings menu."

[0031] "Example 3"

[0032] The technology of natural language processing (NLP) is used as a means to determine whether the generated answer is YES or NO. Specifically, the generated answer is analyzed and its content is determined to be affirmative (YES) or negative (NO). For example, if the generated answer is "Yes, that is possible," this determination means determines that the answer is YES.

[0033] "Example 4"

[0034] Finally, a machine learning algorithm is used to update the chat generation AI based on the judgment result. Specifically, the judgment result is added to the learning data of the chat generation AI as feedback, improving the performance of the chat generation AI. For example, if the judgment result is YES, the question and answer pair is added to the learning data. This allows the chat generation AI to generate a more appropriate answer if the same question is asked again.

[0035] The processing flow of each embodiment will be described below.

[0036] "Example 1"

[0037] Step 1: A user inputs a smartphone operation question through a web-based interface, for example, "How do I adjust the brightness of my smartphone screen?"

[0038] Step 2: The system receives this question and sends it to the chat generation AI.

[0039] "Example 2"

[0040] Step 1: The chat generation AI analyzes the question and generates an appropriate answer, such as "You can adjust the screen brightness from the settings menu."

[0041] Step 2: The generated answer is sent to the system.

[0042] "Example 3"

[0043] Step 1: The system analyzes the generated answer and determines whether it is affirmative (YES) or negative (NO). For example, if the generated answer is "Yes, it is possible," the system determines that the answer is YES.

[0044] Step 2: The decision result is saved in the system.

[0045] "Example 4"

[0046] Step 1: The system adds the judgment results as feedback to the learning data of the chat generation AI.

[0047] Step 2: The chat generation AI is retrained using new training data, so that if the same question is asked again, the chat generation AI can generate a more appropriate answer.

[0048] Example 1

[0049] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0050] In conventional question-answering systems for operating information processing devices, users may not receive the appropriate answer even when they input a question. Furthermore, there is a lack of means to determine whether the generated answer is appropriate and to update the system, which leads to a problem of low user satisfaction. Furthermore, the interface for users to input questions can be difficult to use, which is an issue that does not improve the user experience.

[0051] 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.

[0052] In this invention, the server includes means for receiving a question from a user regarding the operation of an information processing device, means for generating an answer to the received question, means for determining whether the generated answer is YES or NO, means for updating the dialogue generation AI based on the determination result, means for providing a web-based interface for a user to input a question in text format, means for inputting the received question as a prompt sentence to the generative AI model, means for transmitting the generated answer to the user's terminal, and means for displaying the answer on the user's terminal. This allows a user to easily input a question and obtain an appropriate answer. Furthermore, the appropriateness of the generated answer can be determined and the system updated to improve the user experience.

[0053] A "user" is an individual or group that operates an information processing device and inputs a question.

[0054] An "information processing device" is an electronic device operated by a user, such as a smartphone or a personal computer.

[0055] The "means for receiving a question" is a function or device for receiving a question from a user as input.

[0056] The "means for generating an answer" is a function or device for creating an appropriate answer to a received question.

[0057] The "means for determining whether the generated answer is YES or NO" is a function or device for determining whether the generated answer is appropriate.

[0058] "Dialogue generation artificial intelligence" is an artificial intelligence system for generating dialogue with users.

[0059] The "means for updating the dialogue generation artificial intelligence" is a function or device for updating the data or algorithm of the dialogue generation artificial intelligence based on the appropriateness of the generated answers.

[0060] A "web-based interface" is a web page or application that a user accesses and enters a query over the Internet.

[0061] A "prompt" is a form of question that is input to a generative AI model.

[0062] A "generative artificial intelligence model" is an artificial intelligence model that generates answers in natural language to questions from users.

[0063] A "user terminal" is an information processing device used by a user.

[0064] The "means for displaying an answer" is a function or device for displaying the generated answer on the user's terminal.

[0065] This invention is a system that receives questions from users about the operation of information processing devices, generates appropriate answers, and provides them to the users. This system operates in cooperation with a server, a terminal, and a user.

[0066] System configuration

[0067] Hardware and Software

[0068] Server: A server with high-performance computing power is required to receive questions, run generative AI models, generate answers, and send them.

[0069] Terminal: An information processing device used by a user, such as a smartphone or PC. The terminal communicates with the server via a web browser.

[0070] Generative AI models: For example, using advanced natural language processing models such as OpenAI's GPT-4.

[0071] Data processing and calculation

[0072] Receiving questions

[0073] The user accesses the specified URL using the device's web browser. A web-based interface appears, and the user enters a text question, for example, "How do I adjust the brightness of my smartphone screen?"

[0074] Submit a Question

[0075] When a user enters a question and clicks the submit button, the device sends the question to the server as an HTTP request.

[0076] Parsing questions and generating prompts

[0077] The server analyzes the received question and converts it into a prompt suitable for the generative AI model, for example, "User asks: 'How do I adjust the brightness of my smartphone screen?'"

[0078] Generate answers

[0079] The server inputs a prompt into the generative AI model and generates an appropriate answer. The generative AI model generates an answer to the question based on the input prompt. For example, it generates an answer like, "To adjust the brightness of your smartphone screen, open the Settings app and select the 'Display' or 'Screen' option. From there, adjust the 'Brightness' slider to the desired brightness."

[0080] Submitting and viewing answers

[0081] The server sends the generated answer to the user's device as an HTTP response. The device displays the received answer on a web-based interface. The user can check the displayed answer and understand how to operate the information processing device.

[0082] Specific examples

[0083] Consider a case where a user types the question "How do I adjust the brightness of my smartphone screen?" This question is input to a generative AI model as a prompt like this:

[0084] Example prompt sentence:

[0085] A user asks: "How do I adjust the brightness of my smartphone screen?"

[0086] The generative AI model analyzes this prompt and generates the following answer:

[0087] To adjust the brightness of your phone's screen, open the Settings app and select the "Display" or "Screen" option. From there, adjust the "Brightness" slider to your desired brightness.

[0088] This response is sent to the user's device via the server and displayed to the user, who can then adjust the brightness of their smartphone screen.

[0089] In this way, users can easily enter their questions and get relevant answers, and the appropriateness of the generated answers can be assessed and the system updated to improve the user experience.

[0090] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0091] Step 1:

[0092] A user accesses a web-based interface.

[0093] Specific operation: The user opens a web browser on their smartphone or computer and accesses the specified URL.

[0094] Input: Access request from the user's device.

[0095] Output: A web-based interface is displayed on the user's terminal.

[0096] Step 2:

[0097] The user enters a question in text format.

[0098] What it does: A user types a question into a text box displayed on a web-based interface, for example, "How do I adjust the brightness of my smartphone screen?"

[0099] Input: The question text entered by the user.

[0100] Output: The question text is displayed on the interface.

[0101] Step 3:

[0102] The terminal sends the entered question to the server.

[0103] Specific operation: When the user clicks the submit button, the terminal sends the entered question to the server as an HTTP request.

[0104] Input: The question text entered by the user.

[0105] Output: An HTTP request containing the question text is sent to the server.

[0106] Step 4:

[0107] The server receives the question and inputs it as a prompt to the generative AI model.

[0108] How it works: The server analyzes the received question and converts it into a prompt suitable for the generative AI model, for example, "User asks: 'How do I adjust the brightness of my smartphone screen?'"

[0109] Input: The question text received as an HTTP request.

[0110] Output: A prompt to be fed into the generative AI model.

[0111] Step 5:

[0112] A generative AI model generates answers to questions.

[0113] Specific operation: The server inputs a prompt sentence into a generative AI model (e.g., OpenAI's GPT-4) and generates an appropriate answer.

[0114] Input: The prompt statement.

[0115] Output: The answer text generated by the generative AI model.

[0116] Step 6:

[0117] The server receives the generated response and sends it to the user's terminal.

[0118] Specific operation: The server sends the generated answer to the user's terminal as an HTTP response.

[0119] Input: Answer text generated by the generative AI model.

[0120] Output: HTTP response containing the answer text.

[0121] Step 7:

[0122] The terminal displays the answer to the user.

[0123] Specific operation: The user's device displays the answer received from the server on a web-based interface. The user can check the displayed answer and understand how to operate the information processing device.

[0124] Input: The answer text received from the server.

[0125] Output: Answer text displayed on the web-based interface.

[0126] (Application example 1)

[0127] Next, a description will be given of Application Example 1 of Embodiment 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."

[0128] While existing smartphone question-answering systems can provide appropriate answers to questions entered by users, they are unable to adequately respond to questions about electronic payments. Furthermore, the dialogue generation AI is not automatically updated to improve the quality of the generated answers, which can lead to a decline in the accuracy of the system.

[0129] 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.

[0130] In this invention, the server includes means for receiving questions from users regarding smartphone operation, means for generating answers to the received questions, means for determining whether the generated answers are YES or NO, means for updating the dialogue generation AI based on the determination result, means for receiving questions from users regarding electronic payments, means for generating answers to the received questions regarding electronic payments using a generative AI model, and means for displaying the generated answers to the users. This enables users to obtain appropriate answers to questions regarding electronic payments, and further enables the accuracy of the system to be improved by automatically updating the dialogue generation AI.

[0131] "User" means an individual or entity that uses the System to enter questions and receive answers.

[0132] "Smartphone operation" refers to operations and procedures related to smartphone functions and settings.

[0133] "Means for receiving questions" refers to the interface or function that allows the system to receive questions entered by the user.

[0134] "Means for generating answers" refers to algorithms or programs for generating appropriate answers to received questions.

[0135] "Means for determining whether the generated answer is YES or NO" refers to a function for determining whether the generated answer is appropriate.

[0136] "Dialogue generation artificial intelligence" refers to an artificial intelligence system for generating dialogue with users.

[0137] "Means for updating the dialogue generation AI" refers to a function for updating the learning data of the dialogue generation AI in order to improve the quality of the generated answers.

[0138] "Electronic payment" refers to payment transactions conducted over the Internet.

[0139] A "generative AI model" refers to a model that uses artificial intelligence to generate answers to questions.

[0140] "Means for displaying answers to the user" refers to the interface or functionality for visually presenting the generated answers to the user.

[0141] The system for carrying out this invention receives questions from a user regarding smartphone operation and electronic payment, generates appropriate answers, and displays them to the user. A specific embodiment of this system will be described below.

[0142] System Configuration

[0143] The system consists of the following main components:

[0144] 1. User device: The device through which the user enters their question, such as a smartphone or computer.

[0145] 2. Server: A central processing unit that receives questions, generates answers using generative AI models, and displays them to the user.

[0146] 3. Generative AI model: An artificial intelligence model for generating answers to questions, such as OpenAI's GPT-3 (registered trademark).

[0147] Program processing

[0148] The server performs the process in the following steps.

[0149] 1. Receiving a question: The server receives a question sent from the user's terminal. The question is entered in text format.

[0150] 2. Answer generation: The received question is input into the generative AI model, which generates an appropriate answer. The generative AI model generates the best answer based on the content of the question.

[0151] 3. Displaying the answer: The generated answer is sent to the user's device and displayed to the user.

[0152] 4. Evaluate the answer: The server determines whether the generated answer is appropriate, for example, by rating it based on user feedback.

[0153] 5. AI Update: Based on the evaluation results, the learning data of the dialogue generation AI is updated, thereby improving the accuracy of the system.

[0154] Hardware and software used

[0155] Hardware: Cloud servers (e.g., Amazon Web Services or Google® Cloud Platform)

[0156] Software: Flask (Python web framework), OpenAI API

[0157] Specific examples

[0158] When a user enters the question "How do I register a credit card?", the server processes the following:

[0159] 1. Receiving a question: The user's device sends the question "How do I register a credit card?" to the server.

[0160] 2. Generate an answer: The server inputs this question into a generative AI model and generates an answer like this:

[0161] To register a credit card, select "Payment Methods" from the app's settings menu and click "Add Credit Card." Then enter your card information and press the confirmation button.

[0162] 3. Displaying the answer: The generated answer is sent to the user's device and displayed to the user.

[0163] Prompt Sentence Examples

[0164] Electronic Payment Questions: How do I register my credit card?

[0165] answer:

[0166] In this way, users can get appropriate answers to questions about electronic payments, and the accuracy of the system can be improved by automatically updating the dialogue generation artificial intelligence.

[0167] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0168] Step 1:

[0169] The user uses the terminal to input a question. The input question is in text format, such as "How do I register a credit card?" The input question is sent from the terminal to the server.

[0170] Step 2:

[0171] The server receives the question sent from the terminal. The received question is saved as text data on the server. The input here is the question text from the user, and the output is the question data saved on the server.

[0172] Step 3:

[0173] The server inputs the received question into a generative AI model. For example, OpenAI's GPT-3 is used as the generative AI model. Based on the input question, the generative AI model generates an appropriate answer. Here, the input is the question text, and the output is the generated answer text.

[0174] Step 4:

[0175] The server receives the answer obtained from the generative AI model and sends it to the user's device. The user's device displays the received answer. The input here is the generated answer text, and the output is the answer displayed on the user's device.

[0176] Step 5:

[0177] The user reviews the displayed answers and provides feedback, such as a "YES" or "NO" rating, based on whether the answer is correct. The feedback is sent from the device to the server.

[0178] Step 6:

[0179] The server receives feedback from users and updates the learning data of the dialogue generation AI. The input here is user feedback, and the output is updated learning data. This improves the accuracy of the system.

[0180] In this way, users can receive appropriate answers to questions about electronic payments, and the accuracy of the system can be improved by automatically updating the dialogue generation artificial intelligence.

[0181] Example 2

[0182] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0183] Conventional information processing devices have had difficulty quickly providing appropriate answers to user questions. They also lacked effective means for improving the quality of generated answers. Furthermore, they lacked a mechanism for determining whether a generated answer was appropriate and updating the generative AI model based on the results.

[0184] 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.

[0185] In this invention, the server includes means for receiving a question from a user regarding the operation of an information processing device, means for preprocessing the received question, means for inputting the preprocessed question into a generative AI model, means for the generative AI model to generate an answer to the question, and means for transmitting the generated answer to the user. This makes it possible to provide a prompt and appropriate answer to a question from a user. It also provides an effective means for improving the quality of the generated answer, and the accuracy of the answer can be continuously improved by updating the generative AI model.

[0186] A "user" is an individual or group that operates an information processing device and inputs a question.

[0187] An "information processing device" is a device operated by a user, which inputs questions and receives answers.

[0188] The "means for receiving a question" is a function for receiving a question input by a user.

[0189] "Preprocessing means" is a function that processes received questions, such as normalizing them and deleting unnecessary information.

[0190] A "generative AI model" is an artificial intelligence model that learns from large amounts of text data and generates answers to questions in natural language.

[0191] "Means for inputting questions into the generative AI model" refers to a function for passing preprocessed questions to the generative AI model.

[0192] The "means for generating an answer" is the function that enables the generative AI model to generate an appropriate answer based on the question.

[0193] The "means for transmitting an answer to a user" is a function for transmitting the generated answer to the user's information processing device.

[0194] MODE FOR CARRYING OUT THE INVENTION

[0195] This invention relates to a system that provides prompt and appropriate answers to questions from users about the operation of information processing devices. In this system, a user inputs a question through an information processing device, and an answer to the question is generated using a generative AI model and provided to the user.

[0196] Hardware and software used

[0197] Hardware:

[0198] Information processing device: A device operated by a user (e.g., smartphone, tablet, PC, etc.)

[0199] Server: A server that receives questions, preprocesses them, feeds them into the generative AI model, and generates and sends answers.

[0200] software:

[0201] Generative AI models: Large-scale text generation models such as Generative Pre-training Transformer (GPT)

[0202] Preprocessing software: Software for normalizing text and removing unnecessary information

[0203] Data processing and calculation

[0204] The server processes the query received from the user in the following steps:

[0205] 1. Receiving Questions:

[0206] The server receives a natural language question submitted by the user, which is sent to the server as text data.

[0207] 2. Question preprocessing:

[0208] The server preprocesses the received query, which includes normalizing the text (e.g., converting uppercase to lowercase, removing unnecessary whitespace, etc.).

[0209] 3. Input to the generative AI model:

[0210] The server inputs the preprocessed questions into a generative AI model that has been trained from large amounts of text data and is capable of generating answers to natural language questions.

[0211] 4. Generate answers:

[0212] The generative AI model generates appropriate answers based on the questions entered. For example, if a user asks, "How do I adjust the screen brightness?", the model generates the answer, "You can adjust the screen brightness from the settings menu."

[0213] 5. Submit your response:

[0214] The server transmits the generated answer to the user, who can receive the answer through an information processing device.

[0215] Specific examples

[0216] Example prompt sentence:

[0217] How do I adjust the screen brightness?

[0218] Process flow:

[0219] 1. User action: The user opens a chat app on their smartphone and types, "How do I adjust the screen brightness?"

[0220] 2. Device behavior: The smartphone sends this question to the server as an HTTP request.

[0221] 3. Server action: The server receives the question and performs text normalization, for example converting "How do I adjust the brightness of my screen?" to "How do I adjust the brightness of my screen?"

[0222] 4. Server operation: The server inputs the preprocessed questions into the generative AI model.

[0223] 5. How the generative AI model works: The model generates the answer, "You can adjust the screen brightness from the settings menu."

[0224] 6. Server Action: The server receives the generated answer.

[0225] 7. Server operation: The server sends the answer to the user's smartphone as an HTTP response.

[0226] 8. User Action: The user checks the answer displayed on the smartphone screen.

[0227] In this way, users can enter questions in natural language and get appropriate answers using generative AI models.

[0228] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0229] Step 1:

[0230] The user enters a question.

[0231] The user inputs a question into an input field of the information processing device. For example, the user inputs "Please tell me how to adjust the screen brightness." The input question is saved as text data in the terminal.

[0232] Step 2:

[0233] The terminal sends a question to the server.

[0234] The terminal sends the question entered by the user to the server as an HTTP request. A communication process is performed to send the input data (question text) to the server. The input is the user's question text, and the output is an HTTP request to the server.

[0235] Step 3:

[0236] The server receives and pre-processes the query.

[0237] The server receives questions sent from the terminal. The received questions are stored as text data on the server. The server then normalizes the text, for example, converting uppercase to lowercase and removing unnecessary spaces. The input is the received question text, and the output is the preprocessed question text.

[0238] Step 4:

[0239] The server inputs the preprocessed questions into the generative AI model.

[0240] The server inputs the preprocessed questions into a generative AI model. The generative AI model learns from a large amount of text data and has the ability to generate answers to natural language questions. The input is the preprocessed question text, and the output is the input data for the generative AI model.

[0241] Step 5:

[0242] A generative AI model generates the answer.

[0243] A generative AI model generates an appropriate answer based on the input question. For example, if the question is "How do I adjust the screen brightness?", the model generates the answer "You can adjust the screen brightness from the settings menu." The input is the input data to the generative AI model, and the output is the generated answer text.

[0244] Step 6:

[0245] The server receives the generated answer.

[0246] The server receives the answer generated by the generative AI model. The received answer is stored on the server as text data. The input is the generated answer text, and the output is the received data to the server.

[0247] Step 7:

[0248] The server sends the response to the user's terminal.

[0249] The server sends the generated answer to the user's terminal as an HTTP response. The input is the generated answer text, and the output is the HTTP response to the user's terminal.

[0250] Step 8:

[0251] The user receives the answer.

[0252] The user receives the answer sent from the server through the terminal. The answer is displayed on the terminal screen and the user can check it. The input is the HTTP response from the server, and the output is the answer text displayed on the terminal.

[0253] (Application example 2)

[0254] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0255] In brick-and-mortar stores, customers need to be able to quickly and accurately resolve their questions about products and services. However, traditional methods require store staff to handle the inquiries, which can take a long time and lead to a decline in customer satisfaction. This also increases the burden on staff, making efficient operations difficult.

[0256] 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.

[0257] In this invention, the server includes means for receiving questions from users regarding smartphone operation, means for generating answers to the received questions, means for determining whether the generated answers are YES or NO, means for updating the chat generation AI based on the determination result, means for receiving questions from customers in a physical store and generating answers in real time, and means for providing the generated answers to customers. This allows customers to receive answers to their questions quickly and accurately, reducing the burden on store staff and enabling efficient store operations.

[0258] "User" refers to any individual or entity that uses the System.

[0259] "Smartphone operation" refers to a series of operations and inputs performed using a smartphone.

[0260] "Means for receiving questions" refers to the interface or function that allows the system to receive questions from users.

[0261] "Means for generating answers" refers to algorithms or functions for generating appropriate answers to received questions.

[0262] "Means for determining whether it is YES or NO" refers to a function for determining whether the generated answer is positive or negative.

[0263] "Chat generation AI" refers to artificial intelligence that learns from large amounts of text data and generates answers to questions in natural language.

[0264] "Means for updating the chat generation AI" refers to the function for updating the learning data and algorithms of the chat generation AI based on the judgment results.

[0265] "Brick and mortar store" refers to a commercial establishment located in a physical location.

[0266] "Customers" refers to consumers and users who visit physical stores.

[0267] "Means for generating answers in real time" refers to a function that allows you to instantly generate an answer the moment a question is received.

[0268] "Means for providing answers to customers" refers to interfaces and functions for communicating generated answers to customers.

[0269] A system for implementing this invention includes means for receiving questions from users regarding smartphone operation, means for generating answers to the received questions, means for determining whether the generated answers are YES or NO, means for updating the chat generation AI based on the determination result, means for receiving questions from customers in a physical store and generating answers in real time, and means for providing the generated answers to customers.

[0270] Program processing explanation

[0271] The server provides an interface for receiving questions from users. This interface is implemented as a smartphone application. When a user inputs a question, the question is sent to the server.

[0272] The server sends the received question to a generative AI model, which generates an appropriate answer. This generative AI model is implemented using OpenAI's API. The generated answer is analyzed by the server and determined to be either YES or NO.

[0273] Based on the results of the judgment, the server updates the learning data of the chat generation AI. This update process is carried out to improve the accuracy of the AI ​​model.

[0274] In a physical store, when a customer uses a smartphone app to enter a question, the question is sent to the server in real time, and the server instantly generates an answer and provides it to the customer. This process allows customers to get answers to their questions quickly and accurately.

[0275] Specific examples

[0276] For example, a customer opens a smartphone app in a physical store and enters the following question:

[0277] "Do you have this item in stock?"

[0278] The application sends the following prompt to the generative AI model:

[0279] Customer Question: Is this item in stock?

[0280] answer:

[0281] The generative AI model returns the following answer:

[0282] Yes, this item is in stock and can be found in store in Aisle 3.

[0283] In this way, customers can get fast and accurate information.

[0284] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0285] Step 1:

[0286] The user opens the smartphone app and enters a question.

[0287] Input: A question typed by the user (e.g., "Do you have this item in stock?")

[0288] Output: Question text

[0289] What happens: The user types a question into the app's text input field and presses the submit button.

[0290] Step 2:

[0291] The terminal sends the question text to the server.

[0292] Input: Question text

[0293] Output: Question data sent to the server

[0294] Specific operation: The smartphone app sends the question text to the server as an HTTP request.

[0295] Step 3:

[0296] The server receives the question and sends a prompt to the generative AI model.

[0297] Input: Question data sent to the server

[0298] Output: The prompt sent to the generative AI model

[0299] Specific operation: The server analyzes the question data, generates a prompt (e.g., "Customer question: Is this item in stock?\nAnswer:"), and sends it to the generative AI model.

[0300] Step 4:

[0301] A generative AI model generates an answer based on the prompt.

[0302] Input: prompt statement

[0303] Output: The generated answer (e.g., "Yes, we have this item in stock. It's in Aisle 3 of the store.")

[0304] What it does: The generative AI model analyzes the prompt and generates an appropriate response.

[0305] Step 5:

[0306] The server receives the generated response and determines whether it is YES or NO.

[0307] Input: Generated answer

[0308] Output: YES or NO result

[0309] What happens: The server analyzes the generated answer and determines whether the answer is positive or negative.

[0310] Step 6:

[0311] The server updates the learning data of the chat generation AI based on the judgment results.

[0312] Input: YES or NO result

[0313] Output: Updated training data

[0314] Specific operation: Based on the judgment results, the server updates the learning data of the chat generation AI.

[0315] Step 7:

[0316] The server sends the generated response to the terminal.

[0317] Input: Generated answer

[0318] Output: Answer data sent to the device

[0319] Specific operation: The server sends the generated answer to the terminal as an HTTP response.

[0320] Step 8:

[0321] The terminal displays the answer to the user.

[0322] Input: Answer data sent to the terminal

[0323] Output: The answer displayed to the user

[0324] Specific operation: The smartphone app receives the response data and displays it on the user's screen.

[0325] Example 3

[0326] Next, a description will be given of a third embodiment of the third embodiment. 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."

[0327] In conventional systems, generating answers to user questions and determining whether the answers are appropriate is often done manually, which is problematic in efficiency. Furthermore, there is no feedback loop to improve the quality of the generated answers, making it difficult to improve the system's performance.

[0328] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0329] In this invention, the server includes means for receiving questions from users regarding their operations, means for inputting the received questions as prompt sentences to the generative AI model, means for the generative AI model to generate answers, means for determining whether the generated answers are YES or NO, and means for updating the chat generation AI based on the determination result. This makes it possible to automatically generate prompt and appropriate answers to questions from users and continuously improve the quality of those answers.

[0330] A "user" is an entity that operates the system and inputs questions.

[0331] An "operational question" is a query about a specific operation or piece of information that a user inputs into the system.

[0332] "Means for receiving questions" refers to the functions and methods by which the system receives questions from users.

[0333] A "generative AI model" is an artificial intelligence algorithm or system that generates answers based on input prompts.

[0334] A "prompt" is a question or instruction text that is input into a generative AI model.

[0335] "Means for generating an answer" refers to the functions and methods by which a generative AI model creates an answer based on a prompt.

[0336] "Means for determining whether the answer is YES or NO" refers to a function or method for analyzing and determining whether the generated answer is affirmative (YES) or negative (NO).

[0337] "Chat generation AI" is an artificial intelligence system that generates appropriate answers to users' questions.

[0338] "Means for updating the chat generation AI" refers to functions and methods for adding or modifying the learning data of the chat generation AI based on the judgment results and improving the performance of the system.

[0339] This invention is a system that receives a question about an operation from a user, generates an answer to that question, determines whether the generated answer is YES or NO, and updates the chat generation AI based on the result of that determination. Specific embodiments of this system are described below.

[0340] Hardware and software used

[0341] Hardware: Servers, terminals

[0342] Software: Natural Language Processing (NLP) libraries (e.g., spaCy, NLTK), machine learning algorithms (e.g., TENSORFLOW®, PyTorch)

[0343] System configuration

[0344] 1. The user enters a question

[0345] The user types a question into an input field on the device, for example, "Is this item in stock?"

[0346] 2. The device sends a question to the server

[0347] The device sends the question entered by the user to the server. Specifically, it sends the question data to the server using an HTTP request.

[0348] 3. The server inputs the question as a prompt to the generative AI model.

[0349] The server inputs the received question as a prompt to the generative AI model. For example, the question "Is this product in stock?" is passed to the generative AI model.

[0350] 4. Generative AI models generate answers

[0351] The generative AI model generates an answer based on the prompt, for example, "Yes, we have it in stock."

[0352] 5. The server parses the generated answer

[0353] The server uses NLP technology to analyze the generated answer. Specifically, it determines whether the answer is positive (YES) or negative (NO). For example, it analyzes the answer "Yes, we have it in stock" and determines that it is YES.

[0354] 6. The server gives feedback on the decision result

[0355] The server feeds back the results of its judgment to the chat generation AI. Specifically, the judgment results are added to the training data and used to improve the performance of the chat generation AI. For example, the question "Is this product in stock?" and the answer "Yes, it is in stock" are added to the training data.

[0356] 7. The server updates the chat generation AI

[0357] The server updates the chat generation AI based on the feedback, so that if the same question is asked again, it can generate a more appropriate answer.

[0358] Specific examples

[0359] User asks: "Is this item in stock?"

[0360] Prompt for generative AI model: "Is this item in stock?"

[0361] Generated Answer: "Yes, we have it in stock"

[0362] Server decision: Determines that the answer is YES

[0363] Feedback: Add the question "Is this item in stock?" and the answer "Yes, it is in stock" to the training data

[0364] In this way, the system can generate appropriate answers to user questions and continuously learn to improve its performance. The flow of the identification process in the third embodiment will be described with reference to FIG.

[0365] Step 1:

[0366] The user enters a question.

[0367] The user enters a question into an input field on the device, for example, "Is this item in stock?" The entered question is temporarily stored in the device's memory.

[0368] Step 2:

[0369] The terminal sends a question to the server.

[0370] The terminal sends the question entered by the user to the server as an HTTP request. The input data is sent to the server in text format. The server receives this request and analyzes the question data.

[0371] Step 3:

[0372] The server inputs the question as a prompt into the generative AI model.

[0373] The server inputs the received question to the generative AI model as a prompt sentence. For example, the question "Is this product in stock?" is passed to the generative AI model. The input data is in text format and is converted into a format suitable for the generative AI model.

[0374] Step 4:

[0375] A generative AI model generates the answer.

[0376] The generative AI model generates an answer based on the prompt sentence, for example, "Yes, we have it in stock." The generated answer is returned to the server in text format.

[0377] Step 5:

[0378] The server parses the generated answer.

[0379] The server uses NLP technology to analyze the generated answer. Specifically, it determines whether the answer is affirmative (YES) or negative (NO). For example, it analyzes the answer "Yes, we have it in stock" and determines that it is YES. The input data is the generated answer, and the output data is the YES or NO judgment result.

[0380] Step 6:

[0381] The server will provide feedback on the results of the decision.

[0382] The server feeds back the judgment results to the chat generation AI. Specifically, the judgment results are added to the training data and used to improve the performance of the chat generation AI. For example, the question "Is this product in stock?" and the answer "Yes, it is in stock" are added to the training data. The input data are question and answer pairs, and the output data is the updated training data.

[0383] Step 7:

[0384] The server updates the chat generation AI.

[0385] The server updates the chat generation AI based on the feedback, so that it can generate more appropriate answers when the same question is asked again. The input data is updated learning data, and the output data is an improved chat generation AI.

[0386] (Application example 3)

[0387] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0388] Conventional dialogue generation AIs in information processing devices have low accuracy in responding to user questions, making it difficult to improve user satisfaction. Furthermore, they lack a means to determine whether a generated response is positive or negative, preventing effective updating of the learning data of dialogue generation AIs. Therefore, there is a need to provide appropriate responses to user questions and improve the performance of dialogue generation AIs.

[0389] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0390] In this invention, the server includes means for receiving questions from a user regarding operation of the information processing device, means for generating answers to the received questions, means for determining whether the generated answers are positive or negative, means for updating the dialogue generation artificial intelligence based on the determination result, and means for saving the determination result as feedback, thereby making it possible to provide appropriate answers to user questions and improve the performance of the dialogue generation artificial intelligence.

[0391] An "information processing device" is a device that inputs, processes, and outputs data, and includes smartphones, computers, etc.

[0392] "Question receiving means" refers to a function or device for receiving questions from users, and includes methods such as text input and voice input.

[0393] "Answer generation means" refers to a function or device for generating an appropriate answer to a received question.

[0394] The "judgment means" is a function or device for judging whether the generated answer is positive or negative.

[0395] "Dialogue generation artificial intelligence" is an artificial intelligence system for generating dialogue with users.

[0396] The "update means" refers to a function or device for updating the learning data of the dialogue generation artificial intelligence based on the judgment results.

[0397] The "feedback storage means" is a function or device for storing the judgment results as feedback.

[0398] "Character input" refers to a method of inputting characters using a keyboard or touch screen.

[0399] "Training data" is a data set used by artificial intelligence to learn.

[0400] A system for implementing this invention includes means for receiving questions from a user regarding the operation of an information processing device, means for generating answers to the received questions, means for determining whether the generated answers are positive or negative, means for updating the dialogue generation artificial intelligence based on the determination results, and means for saving the determination results as feedback.

[0401] Program processing explanation

[0402] Hardware and software used

[0403] Hardware: Smartphone

[0404] Software: Python, OpenAI API, TextBlob

[0405] Data processing and calculation

[0406] 1. Question input: The user inputs a question into the smartphone app. For example, "Can I return this item?"

[0407] 2. Answer generation using generative AI model: The server uses OpenAI's API to generate an answer to the question, for example, "Yes, you can return it within 30 days."

[0408] 3. Natural Language Processing: The server uses TextBlob to perform sentiment analysis on the generated answer and determine whether it is positive (YES) or negative (NO). For example, if the generated answer is "Yes, you can return it within 30 days," it is determined to be positive (YES).

[0409] 4. Save feedback: The server saves the question and answer pairs in a text file and uses them as training data for future use. For example, it saves data such as "Question: Can I return this product?", "Answer: Yes, you can return it within 30 days," and "Result: YES."

[0410] Specific examples

[0411] Question: "Can I return this item?"

[0412] Generated Answer: "Yes, you can return it within 30 days."

[0413] Judgment result: YES

[0414] Prompt Sentence Examples

[0415] User: Can I return this item?

[0416] AI: Yes, you can return it within 30 days.

[0417] In this way, the system can provide appropriate answers to user questions and improve the performance of dialogue generation artificial intelligence.

[0418] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0419] Step 1:

[0420] The user inputs a question using a smartphone device.

[0421] Input: A user types into an app on their smartphone, "Can I return this item?"

[0422] Output: The entered question is sent to the server as text data.

[0423] Specific operation: The user enters a question using the smartphone keyboard and presses the send button, which sends the question to the server.

[0424] Step 2:

[0425] The server uses a generative AI model to generate an answer to the question it receives.

[0426] Input: The server receives the question "Can I return this item?" submitted by the user.

[0427] Output: The answer "Yes, returns are accepted within 30 days" is generated using a generative AI model (OpenAI API).

[0428] Specific behavior: The server sends a question to the OpenAI API and receives the answer returned by the API.

[0429] Step 3:

[0430] The server analyzes the generated answer using natural language processing technology to determine whether it is positive or negative.

[0431] Input: The server receives the generated answer "Yes, you can return it within 30 days."

[0432] Output: The answer is determined to be affirmative (YES) using a TextBlob.

[0433] What it does: The server uses the TextBlob library to perform sentiment analysis on the answer and determines it to be positive by getting a positive sentiment score.

[0434] Step 4:

[0435] The server updates the learning data of the dialogue generation artificial intelligence based on the judgment results.

[0436] Input: Server receives question "Can I return this item?", answer "Yes, you can return it within 30 days", and verdict "YES".

[0437] Output: Question and answer pairs are saved as training data.

[0438] Specific operation: The server saves the question and answer pairs in a text file or database and uses them as learning data for future use.

[0439] Step 5:

[0440] The server stores the judgment result as feedback.

[0441] Input: The server receives the verdict "YES".

[0442] Output: The judgment result is saved as feedback data.

[0443] Specific operation: The server stores the feedback data in text files or a database to help improve the performance of the dialogue generation AI.

[0444] 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.

[0445] "Example 1"

[0446] As one embodiment of the present invention, a system is provided that includes means for receiving questions from users regarding smartphone operation, means for generating answers to the received questions, means for determining whether the generated answers are YES or NO, means for updating a chat generation AI based on the determination result, and an emotion engine for recognizing the user's emotions.

[0447] "Example 2"

[0448] The emotion engine analyzes emotions from user text input. For example, if a user types the question "How do I adjust the brightness of my smartphone screen?" with anger, the emotion engine will recognize that anger.

[0449] "Example 3"

[0450] The chat generation AI's update method updates the learning data of the chat generation AI based on the analysis results of the emotion engine. For example, if a user asks a question with anger, the learning data will be updated so that an answer that reflects that anger is generated.

[0451] "Example 4"

[0452] In this way, according to the present invention, more appropriate answers are generated that take the user's emotions into consideration, thereby improving user satisfaction. Furthermore, by reflecting the analysis results of the emotion engine in the training data, the system's ability to generate answers is improved.

[0453] The processing flow of each embodiment will be described below.

[0454] "Example 1"

[0455] Step 1: Receive questions from users regarding smartphone operation.

[0456] Step 2: Generate answers to the questions received.

[0457] Step 3: Determine whether the generated answer is YES or NO.

[0458] Step 4: Update the chat generation AI based on the results of the judgment.

[0459] Step 5: Use an emotion engine to recognize user emotions.

[0460] "Example 2"

[0461] Step 1: Analyze sentiment from user text input.

[0462] Step 2: Use the analysis results to recognize the user's emotions.

[0463] "Example 3"

[0464] Step 1: Update the learning data of the chat generation AI based on the analysis results of the emotion engine.

[0465] Step 2: Generate an answer to the user's question using the updated learning data.

[0466] Step 1: Generate answers that take user sentiment into account.

[0467] Step 2: Provide the generated answer to the user.

[0468] Step 3: Evaluate user satisfaction.

[0469] Example 1

[0470] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0471] Conventional question-answering systems for operating information processing devices have difficulty generating appropriate answers to user questions and lack a means to determine the accuracy of the generated answers, making it difficult to improve user satisfaction. Furthermore, the lack of a function to recognize the user's emotions and adjust the answers based on them has led to a problem of a poor user experience.

[0472] 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.

[0473] In this invention, the server includes means for receiving questions from a user regarding the operation of the information processing device, means for generating answers to the received questions, means for determining whether the generated answers are YES or NO, means for updating the dialogue generation AI based on the determination result, and means for recognizing the user's emotions, thereby enabling the server to provide appropriate and accurate answers to questions from the user and to respond in consideration of the user's emotions.

[0474] An "information processing device" is a device that inputs, processes, and outputs data, and includes computers, smartphones, etc.

[0475] "Means for receiving questions" refers to the interface or functionality for receiving text questions from users.

[0476] "Means for generating answers" refers to algorithms or artificial intelligence models that generate appropriate answers to received questions.

[0477] "Yes / No method" refers to the rules or algorithms used to evaluate the accuracy of the generated answer.

[0478] "Dialogue generation artificial intelligence" refers to an artificial intelligence system for generating dialogue with users.

[0479] "Means for updating the dialogue generation AI" refers to a function for updating the learning data and model of the dialogue generation AI based on the evaluation results of the generated answers.

[0480] "Emotion recognition means" refers to algorithms or engines that analyze and recognize user emotions from user input text or other data.

[0481] MODE FOR CARRYING OUT THE INVENTION

[0482] This invention is a system that receives questions from users about the operation of an information processing device, generates appropriate answers, evaluates the accuracy of the answers, and updates a dialogue generation artificial intelligence. Specific embodiments of this system are described below.

[0483] Enter and submit your question

[0484] A user inputs a question about the operation of an information processing device through a web-based interface. This interface is designed to allow the user to input a question in text format. For example, the user may input "How do I adjust the brightness of my smartphone screen?" This question is sent from the device to a server. The transmission uses the HTTPS protocol.

[0485] Question Analysis

[0486] The server analyzes the received question using natural language processing (NLP) techniques, such as tokenization, grammar analysis, and intent extraction. For example, a Python NLP library can be used for this analysis.

[0487] Generate answers

[0488] The server generates an answer using a generative AI model (e.g., OpenAI's GPT-4) based on the analysis results. The generative AI model generates an appropriate answer based on the content of the question. For example, it generates the answer "Open the Settings app, select Display Settings, and adjust the brightness."

[0489] Evaluating answers

[0490] The server determines whether the generated answer is YES or NO using pre-defined rules and algorithms. For example, if the generated answer is determined to be accurate, it will be evaluated as YES.

[0491] Updates to chat generation AI

[0492] The server adds answers that are evaluated as YES to the database of the dialogue generation AI. If the answer is evaluated as NO, the server provides feedback to the generation AI model and corrects the answer. This update can be performed using a database management system, for example.

[0493] emotion recognition

[0494] The server uses an emotion engine to analyze the user's emotions. For example, if it determines that the user is in a difficult situation, it will reflect this information in generating future answers. For example, an emotion analysis library can be used for this emotion recognition.

[0495] Submitting and viewing answers

[0496] The server sends the generated answer to the device, which then displays the received answer to the user. For example, the answer may say, "Open the Settings app, select Display Settings, and adjust brightness."

[0497] Specific examples

[0498] A user types "How do I adjust the brightness of my smartphone screen?" into a web-based interface. The device sends this question to the server, which uses a generative AI model to generate the answer "Open the Settings app, select Display Settings, and adjust brightness." The server evaluates this answer, determines it to be YES, and updates the dialog generator. Finally, the server sends this answer to the device, which displays it to the user.

[0499] Prompt Sentence Examples

[0500] "How do I adjust the brightness of my smartphone screen?"

[0501] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0502] Step 1:

[0503] A user accesses a web-based interface and enters a textual question about the operation of the information processing device, such as "How do I adjust the brightness of my smartphone screen?" The entered question is temporarily stored in the device's memory.

[0504] Step 2:

[0505] The terminal sends the question entered by the user to the server using the HTTPS protocol. The data sent is a text question. The server receives this data and proceeds to the next processing step.

[0506] Step 3:

[0507] The server analyzes the received question using natural language processing (NLP) technology. Specifically, it performs tokenization, grammar analysis, and intent extraction. The input is a text question, and the output is structured data of the analyzed question. For example, keywords such as "smartphone," "screen brightness," and "how to adjust" are extracted.

[0508] Step 4:

[0509] The server generates an answer using a generative AI model (e.g., OpenAI's GPT-4) based on the analysis results. The input is the structured data of the analyzed question, and the output is a text answer. For example, the generated answer might be "Open the Settings app, select Display Settings, and adjust brightness."

[0510] Step 5:

[0511] The server determines whether the generated answer is YES or NO. This determination is made using pre-defined rules and algorithms. The input is the generated answer, and the output is the evaluation result of YES or NO. For example, if the generated answer is judged to be accurate, it will be evaluated as YES.

[0512] Step 6:

[0513] The server adds answers evaluated as YES to the database of the dialogue generation AI. If the answer is evaluated as NO, it provides feedback to the generative AI model and corrects the answer. The input is the evaluation result and the generated answer, and the output is an updated database of the dialogue generation AI.

[0514] Step 7:

[0515] The server uses an emotion engine to analyze the user's emotions. The input is the user's input text, and the output is the user's emotional state. For example, if the server determines that the user is in a difficult situation, it will reflect that information in generating future answers.

[0516] Step 8:

[0517] The server sends the generated answer to the device. The device receives the answer and displays it to the user. The input is the generated answer, and the output is a text answer that is displayed to the user. For example, the answer might be "Open the Settings app, select Display Settings, and adjust brightness."

[0518] (Application example 1)

[0519] Next, a description will be given of Application Example 1 of Embodiment 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."

[0520] Conventional question-answering systems for operating information terminals have difficulty providing appropriate answers to user questions and have been unable to provide feedback that takes into account the user's emotions. As a result, the user experience deteriorates and the system's continued use rate decreases.

[0521] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. In this invention, the server includes means for receiving a question from a user regarding information terminal operation, means for generating an answer to the received question, means for determining whether the generated answer is YES or NO, means for updating the dialogue generation AI based on the determination result, and an emotion engine for recognizing the user's emotions. This makes it possible to provide an appropriate answer to the user's question and also provide feedback that takes the user's emotions into consideration.

[0522] An "information terminal" is an electronic device that can be operated by a user, such as a smartphone or tablet.

[0523] The "means for receiving a question" is an interface for receiving a textual question from a user as input.

[0524] A "means for generating an answer" is an algorithm or program for generating an appropriate answer to a received question.

[0525] A "means for determining whether it is YES or NO" is the logic or program used to determine whether the generated answer is positive or negative.

[0526] "Dialogue generation artificial intelligence" is an artificial intelligence system for generating dialogue with users.

[0527] The "means for updating the dialogue generation AI" is a mechanism for updating the learning data of the dialogue generation AI based on the judgment results.

[0528] An "emotion engine" is software that recognizes and analyzes emotions from user input and dialogue.

[0529] The system for implementing this invention receives questions from users about how to operate an information terminal, generates appropriate answers to those questions, and further recognizes the user's emotions and provides feedback. A specific embodiment of this system will be described below.

[0530] The server provides a web-based interface as a means for receiving questions from users regarding the operation of the information terminal. This interface is designed to allow users to enter questions in text format. For example, if a user enters the question "How do I change the password for my smartphone's lock screen?", this question is sent to the server.

[0531] The server uses a generative AI model to generate answers to received questions. The generative AI model contains algorithms for generating appropriate answers to questions. For example, it uses OpenAI's API to provide specific instructions for answering questions.

[0532] To determine whether the generated answer is a YES or NO, the server has logic to analyze the content of the answer. This logic determines whether the answer is positive or negative. For example, it analyzes answers such as "Yes, I can" or "No, I can't."

[0533] Furthermore, the server has a means for updating the dialogue generation AI based on the judgment results. This means feeds back the generated answers and their evaluation results to the dialogue generation AI as learning data. This allows the system to continuously learn and provide more appropriate answers.

[0534] The server also includes an emotion engine that recognizes user emotions. This emotion engine is software that recognizes and analyzes emotions from user input and dialogue. For example, it uses the TextBlob library to analyze the emotion of user input text.

[0535] As a concrete example, the following prompt sentence can be input to a generative AI model:

[0536] "Answer your smartphone security settings question: How do I change my smartphone lock screen password?"

[0537] An example of the generative AI model's response to this prompt is as follows:

[0538] To change your phone's lock screen password, follow these steps:

[0539] 1. Open the Settings app.

[0540] 2. Select "Security" or "Lock screen and security."

[0541] 3. Select the "Screen Lock Type".

[0542] 4. Enter your current password and set a new password.

[0543] 5. Save your settings.

[0544] In this way, when a user inputs a specific question, the server can provide an appropriate answer and provide feedback that takes the user's emotions into account, which is expected to improve the user experience and increase the rate at which the system is used.

[0545] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0546] Step 1:

[0547] A user accesses a web-based interface using an information terminal and enters a question in text format. The entered question is sent to the server. The input data is the question text entered by the user, and the output data is the question text sent to the server.

[0548] Step 2:

[0549] The server inputs the received question text into the generative AI model, which then executes an algorithm to generate an appropriate answer to the question. The input data is the user's question text, and the output data is the generated answer text.

[0550] Step 3:

[0551] The server analyzes the generated answer text and determines whether the answer is YES or NO. Specifically, it uses logic to analyze the content of the answer text to determine whether it is positive or negative. The input data is the generated answer text, and the output data is the YES or NO judgment result.

[0552] Step 4:

[0553] The server updates the learning data of the dialogue generation AI based on the judgment result. Specifically, it feeds back the generated answer and its evaluation result to the dialogue generation AI. The input data is the generated answer text and the judgment result, and the output data is the updated learning data.

[0554] Step 5:

[0555] The server uses an emotion engine to recognize emotions from user input and dialogue. Specifically, it uses the TextBlob library to analyze the emotions of the user's input text. The input data is the user's input text, and the output data is the result of the emotion analysis.

[0556] Step 6:

[0557] The server provides feedback to the user based on the results of the sentiment analysis. For example, if the user is satisfied, it displays "The user seems satisfied," and if the user is dissatisfied, it displays "The user may be dissatisfied." The input data is the result of the sentiment analysis, and the output data is the feedback message provided to the user.

[0558] Example 2

[0559] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0560] In conventional systems, generating answers to user questions relied on simple YES / NO decisions, making it difficult to provide appropriate answers that took into account the user's emotions and the context of the question. Furthermore, the training data needed to improve the quality of the generated answers was not updated efficiently. This resulted in a poor user experience and a loss of system reliability.

[0561] 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.

[0562] In this invention, the server includes means for receiving a question related to an operation from a user, means for generating an answer to the received question, means for determining whether the generated answer is YES or NO, means for updating the generative AI model based on the determination result, means for analyzing emotions from the user's input text, means for sending a question to the generative AI model based on the emotion analysis result, and means for providing the generated answer to the user. This makes it possible to provide an appropriate answer that takes into account the user's emotions and the context of the question, and to efficiently update the learning data of the generative AI model.

[0563] "User" means any person or entity that uses the System to enter questions and receive answers.

[0564] "Operational questions" refer to questions that users ask about how to operate a particular device or system.

[0565] "Means for receiving" refers to a combination of hardware and software for receiving input from a user.

[0566] "Means for generating answers" refers to algorithms or programs for generating appropriate answers to questions received from users.

[0567] "Means for determining whether the generated answer is YES or NO" refers to the logic or program for determining whether the generated answer is affirmative or negative.

[0568] A "generative AI model" refers to an artificial intelligence model that can learn from large amounts of text data and generate answers to questions in natural language.

[0569] "Means of updating" refers to the processes and programs used to update the training data of a generative AI model with new information.

[0570] "Means for analyzing emotions" refers to algorithms or programs used to identify emotions from user input text.

[0571] "Means for sending questions to a generative AI model based on the results of sentiment analysis" refers to a process or program for sending questions in an appropriate format to a generative AI model, taking into account the results of sentiment analysis.

[0572] "Means for providing" refers to a combination of hardware and software for displaying or communicating generated answers to a user.

[0573] This invention is a system that provides appropriate answers to user questions about operations. The system includes a server, a terminal, a generative AI model, and an emotion analysis engine.

[0574] Hardware and software used

[0575] Hardware: Servers, terminals

[0576] Software: Generative AI model (Generative Pre-training Transformer), sentiment analysis engine

[0577] Specific operation of the system

[0578] 1. User inputs a question

[0579] The user types a question into the device's input field and presses the send button, for example, "How do I adjust the brightness of my smartphone screen?"

[0580] 2. Emotion analysis

[0581] The server receives the user's input text and sends it to a sentiment analysis engine. The sentiment analysis engine analyzes the text and identifies the emotion. For example, if the user's question contains anger, the sentiment analysis engine will recognize that anger exists.

[0582] 3. Handling Questions

[0583] The server sends the user's question along with the sentiment analysis results to the generative AI model. For example, a question like "How do I adjust the brightness of my smartphone screen?" is sent to the generative AI model.

[0584] 4. Answer generation

[0585] The generative AI model generates answers in natural language based on knowledge learned from large amounts of text data, such as "You can adjust the screen brightness from the settings menu."

[0586] 5. Providing answers

[0587] The server sends the generated answer to the user's device, which then displays the answer. For example, the user's device might display the answer, "You can adjust the screen brightness from the settings menu."

[0588] Examples and prompts

[0589] Specific examples

[0590] User Question: "How do I adjust the brightness of my smartphone screen?"

[0591] Sentiment analysis result: Anger

[0592] Generated answer: "You can adjust the screen brightness from the settings menu."

[0593] Prompt Sentence Examples

[0594] Prompt: "How do I adjust the brightness of my smartphone screen?"

[0595] This system combines a generative AI model and a sentiment analysis engine to provide quick and appropriate answers to user questions. The sentiment analysis engine analyzes the user's emotions, and the generative AI model generates answers in natural language based on the analysis results. This makes it possible to provide appropriate answers that take into account the user's emotions and the context of the question, and to efficiently update the learning data of the generative AI model.

[0596] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0597] Step 1:

[0598] The user enters a question into the input field on the terminal and presses the send button.

[0599] Input: User question text (e.g., "How do I adjust the brightness of my smartphone screen?")

[0600] Output: Question text sent from terminal to server

[0601] Step 2:

[0602] The server receives the user's input text and sends it to the sentiment analysis engine.

[0603] Input: Question text sent from the terminal

[0604] Output: Text sent to sentiment analysis engine

[0605] Step 3:

[0606] A sentiment analysis engine analyzes the text and identifies sentiment.

[0607] Input: Question text sent by the server

[0608] Data processing: Using text analysis algorithms to identify sentiment

[0609] Output: Sentiment analysis result (e.g., anger)

[0610] Step 4:

[0611] The server sends the user's question along with the sentiment analysis results to the generative AI model.

[0612] Input: Sentiment analysis results and question text

[0613] Output: Sending questions and sentiment analysis results to the generative AI model

[0614] Step 5:

[0615] The generative AI model generates answers in natural language based on knowledge learned from large amounts of text data.

[0616] Input: Question text sent from the server and sentiment analysis results

[0617] Data calculation: The process of generating answers using generative AI models

[0618] Output: Generated answer text (e.g. "You can adjust the screen brightness from the Settings menu.")

[0619] Step 6:

[0620] The server sends the generated answer to the user's terminal.

[0621] Input: Answer text from the generative AI model

[0622] Output: Sends the answer text to the user's terminal

[0623] Step 7:

[0624] The user's device displays the answer.

[0625] Input: Answer text sent from the server

[0626] Output: The answer displayed on the device screen (e.g., "You can adjust the screen brightness from the Settings menu.")

[0627] (Application example 2)

[0628] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0629] Conventional chat generation AI systems generate responses without considering the user's emotions or urgency, which can lead to problems such as reduced user satisfaction and trust. Furthermore, they sometimes fail to respond appropriately to security-related questions, making it difficult to ensure user safety.

[0630] 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.

[0631] In this invention, the server includes means for receiving questions from users regarding smartphone operation, means for generating answers to the received questions, means for determining whether the generated answers are YES or NO, means for updating the chat generation AI based on the determination result, means for analyzing emotions from text input by the user, means for determining the level of urgency based on the emotion analysis result, and means for taking action according to the level of urgency. This makes it possible to provide appropriate answers that take into account the user's emotions and the level of urgency, and to respond quickly and appropriately to security-related questions.

[0632] "User" means any individual or entity that uses the System.

[0633] "Questions about smartphone operation" are inquiries from users about smartphone functions and settings.

[0634] A "means for receiving a question" is an interface for receiving text input from a user.

[0635] The "means for generating answers" is a chat generation AI that generates appropriate answers to received questions.

[0636] The "means for determining whether the generated answer is YES or NO" is an algorithm for determining whether the generated answer is affirmative or negative.

[0637] "Means for updating the chat generation AI" refers to the process for updating the learning data of the chat generation AI based on the judgment results.

[0638] The "means for analyzing emotions" is an emotion engine for analyzing emotions from the user's text input.

[0639] The "means for determining urgency" is an algorithm for assessing the user's urgency based on the results of emotion analysis.

[0640] "Means for taking appropriate action according to the level of urgency" refers to a process for taking appropriate action according to the level of urgency.

[0641] The system for implementing this invention receives questions from users about smartphone operation, generates appropriate answers, and responds according to the user's emotions and level of urgency. Specific embodiments of this system are described below.

[0642] System configuration

[0643] The system consists of the following main components:

[0644] 1. User terminal: A device such as a smartphone that provides an interface for users to enter questions.

[0645] 2. Server: Receives questions, generates answers, performs sentiment analysis and urgency determination.

[0646] 3. Chat generation AI: Uses Generative Pre-training Transformer (GPT) to generate answers to user questions.

[0647] 4. Emotion Engine: Analyzes emotions from user text input using natural language processing tools such as TextBlob.

[0648] 5. Urgency judgment algorithm: Evaluate the user's urgency based on the results of sentiment analysis.

[0649] Program processing

[0650] The server receives questions sent from the user's device. The received question is first analyzed by an emotion engine to evaluate the user's emotion. Next, an urgency judgment algorithm evaluates the user's level of urgency based on the emotion analysis results.

[0651] The chat generation AI then generates an answer to the question. The content of the answer is evaluated by a means to determine whether the answer is YES or NO. Based on the evaluation results, the learning data of the chat generation AI is updated.

[0652] Hardware and software used

[0653] Hardware: Smartphone (user device), server

[0654] Software: Python, OpenAI API (GPT-3), TextBlob

[0655] Specific examples

[0656] A user uses their smartphone to type the question, "I've seen some suspicious people around my house recently. What should I do?" This question is sent to the server and analyzed by the emotion engine. The emotion analysis reveals that the user is feeling anxious. The urgency judgment algorithm determines that this situation is of high urgency.

[0657] The chat generation AI then generates a response such as, "Please contact the police immediately. Also, please warn your neighbors." This response is returned to the user, and at the same time, the learning data of the chat generation AI is updated.

[0658] Example prompt sentence:

[0659] User: I've noticed some suspicious people around my house recently. What should I do?

[0660] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0661] Step 1:

[0662] The user uses a smartphone to input a question. For example, the user might input a question like, "I've seen some suspicious people around my house recently. What should I do?" This input is sent to the server as text data.

[0663] Step 2:

[0664] The server receives questions sent by users. It passes the received text data to the emotion engine. The emotion engine uses TextBlob to analyze emotions from the text data. The analysis results are output as the degree of positivity or negativity of the emotion and the urgency.

[0665] Step 3:

[0666] The server executes an urgency determination algorithm based on the emotion analysis results. It receives the emotion analysis results as input and evaluates the urgency. For example, if the emotion is negative and the urgency is high, it is judged to be "high urgency." This urgency evaluation result is output.

[0667] Step 4:

[0668] Based on the urgency assessment results, the server passes the question to the chat generation AI. The chat generation AI uses Generative Pre-training Transformer (GPT) to generate an appropriate answer to the question. The generated answer is output as text data.

[0669] Step 5:

[0670] The server evaluates the content of the answer using a means to determine whether the generated answer is YES or NO. For example, if the generated answer is "Please contact the police immediately," the server determines whether this is YES or NO. The evaluation result is output.

[0671] Step 6:

[0672] The server updates the learning data of the chat generation AI based on the evaluation results. It receives the evaluation results as input and updates the learning data of the AI ​​model. This update improves the accuracy of answer generation from the next time onwards.

[0673] Step 7:

[0674] The server then sends the generated answer to the user's device. The user receives the answer on their smartphone and it is displayed. For example, the answer may read, "Please contact the police immediately. Also, please warn your neighbors."

[0675] Example 3

[0676] Next, a description will be given of a third embodiment of the third embodiment. 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."

[0677] Conventional dialogue generation systems often provide inappropriate answers to user questions, resulting in low user satisfaction. Furthermore, it is difficult to generate answers that take the user's emotions into account, making it difficult to respond appropriately to the user's feelings. Furthermore, there is a lack of a way to determine whether the generated answers are positive or negative, which reduces the accuracy of the system.

[0678] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0679] In this invention, the server includes means for receiving questions related to operations from users, means for generating answers to the received questions, means for determining whether the generated answers are positive or negative, means for updating the dialogue generation AI based on the determination result, and means for analyzing the user's emotions using an emotion analysis engine and reflecting the results in the learning data. This makes it possible to provide appropriate answers to the user's questions that correspond to their emotions, thereby improving user satisfaction.

[0680] "User" means an individual or entity that utilizes the System to enter questions and receive answers.

[0681] "Questions about operations" are inquiries about specific operations or functions that users perform on the system.

[0682] The "receiving means" refers to a method or device by which the system receives a question from a user.

[0683] A "means for generating an answer" is a method or device for generating an appropriate answer to a received question.

[0684] A "generated answer" is a response that the system generates in response to a user's question.

[0685] A "means for determining whether a response is affirmative or negative" is a method or device for identifying whether the generated response is affirmative (YES) or negative (NO).

[0686] "Dialogue generation artificial intelligence" is an artificial intelligence system that generates appropriate answers to users' questions.

[0687] An "updating means" is a method or device for improving the training data or algorithms in order to improve the performance and accuracy of the system.

[0688] An "emotion analysis engine" is a technology that analyzes emotions from user input and reflects the results in the system.

[0689] "Training data" is a dataset used by the dialogue generation AI to learn, and includes question and answer pairs and the results of sentiment analysis.

[0690] MODE FOR CARRYING OUT THE INVENTION

[0691] This invention is a system that receives questions about operations from users, generates appropriate answers, and determines whether the answers are positive or negative. The system aims to provide more appropriate answers by updating the dialogue generation AI, analyzing the user's emotions, and reflecting them in the learning data.

[0692] Hardware and software used

[0693] The server uses the following hardware and software:

[0694] Hardware: High-performance server machine (e.g., Intel Xeon processor, 32GB RAM or more)

[0695] Software: Python, NLTK library, SpaCy, TensorFlow, PyTorch

[0696] Data processing and calculation

[0697] The server receives a question from the user and inputs it as a prompt into the generative AI model. The generative AI model generates an answer based on the prompt. The generated answer is analyzed using NLP technology to determine whether it is positive or negative. The result of the judgment is then added as feedback to the learning data of the dialogue generation AI. Furthermore, an emotion analysis engine is used to analyze the user's emotions, and the results are reflected in the learning data.

[0698] Specific examples

[0699] The user enters the question, "Can I return this item?" The device sends this question to the server. The server receives the question and inputs the prompt sentence, "User: Can I return this item?" into the generative AI model. The generative AI model generates the answer, "Yes, you can return it." The server analyzes this answer and determines it to be affirmative (YES). The server then adds this question and answer pair to the training data. Furthermore, the server uses a sentiment analysis engine to analyze the user's sentiment and reflects it in the training data. Finally, the server uses the updated dialogue generation AI to provide the user with the optimal answer, "Yes, you can return it."

[0700] Prompt Sentence Examples

[0701] User: Can I return this item?

[0702] Generative AI model: Yes, returns are possible.

[0703] In this way, the system can provide appropriate and emotionally appropriate answers to user questions, thereby improving user satisfaction. The flow of the identification process in the third embodiment will be described with reference to FIG.

[0704] Step 1:

[0705] The user enters a question.

[0706] The user uses the terminal to input a question. For example, the user inputs, "Can I return this product?" The input question is saved as text data on the terminal.

[0707] Step 2:

[0708] The terminal sends a question to the server.

[0709] The terminal sends the user's question to the server. HTTP or WebSocket is used as the communication protocol. The input is the user's question text, and the output is a transmission completion notification to the server.

[0710] Step 3:

[0711] The server receives the question and inputs it as a prompt to the generative AI model.

[0712] The server inputs the received question to the generative AI model as a prompt. For example, the prompt might be "User: Can I return this item?" The input is the user's question text, and the output is the prompt to the generative AI model.

[0713] Step 4:

[0714] A generative AI model generates the answer.

[0715] The generative AI model generates an answer based on the prompt text. For example, it generates an answer such as "Yes, returns are possible." The input is the prompt text, and the output is the generated answer text.

[0716] Step 5:

[0717] The server analyzes the generated answer and determines whether it is positive or negative.

[0718] The server uses NLP techniques to analyze the generated answer. For example, if the answer is "Yes, returns are allowed," the server determines this as a positive (YES) answer. The input is the generated answer text, and the output is a positive (YES) or negative (NO) decision result.

[0719] Step 6:

[0720] The server adds the judgment results as feedback to the learning data of the dialogue generation artificial intelligence.

[0721] The server adds the judgment result as feedback to the learning data of the dialogue generation AI. For example, the question and answer pair "Can I return this product?" and "Yes, you can return it" are added to the learning data. The input is the question and answer pair and the judgment result, and the output is the updated learning data.

[0722] Step 7:

[0723] The server uses an emotion analysis engine to analyze the user's emotions and reflects the results in the learning data.

[0724] The server uses a sentiment analysis engine to analyze the user's sentiment. For example, if a user asks a question with anger, the server updates the training data so that answers that reflect that anger are generated. The input is the user's question text and the sentiment analysis results, and the output is the updated training data.

[0725] Step 8:

[0726] The server uses the updated dialogue generation artificial intelligence to provide the user with the most appropriate answer.

[0727] The server uses the updated dialogue generation AI to provide the optimal answer to the user, thereby improving user satisfaction. The input is the user's question text and the updated learning data, and the output is the optimal answer text to the user.

[0728] (Application example 3)

[0729] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0730] Conventional content distribution services lack systems that effectively utilize user feedback to recommend content. Furthermore, personalized content recommendations that take user emotions into account are not implemented, resulting in a decline in user satisfaction. Furthermore, chat generation AI is not updated enough based on user feedback, limiting its ability to generate appropriate responses.

[0731] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means. In this invention, the server includes means for receiving a question from a user regarding smartphone operation, means for generating an answer to the received question, means for determining whether the generated answer is YES or NO, means for updating the chat generation AI based on the determination result, means for analyzing the user's feedback using natural language processing technology and determining whether the response is positive or negative, means for updating the content recommendation engine using the determination result as feedback, and means for analyzing the user's emotions and recommending content according to the emotions. This enables effective use of user feedback and personalized content recommendations that take the user's emotions into consideration. Furthermore, the chat generation AI is appropriately updated, improving its ability to generate more appropriate answers.

[0732] "User" means any individual or entity that uses the System.

[0733] "Questions about smartphone operation" are inquiries that users make to the system regarding how to use or functions of their smartphones.

[0734] "Means for receiving questions" refers to the interface or function that allows the system to receive questions from users.

[0735] A "means for generating an answer" is an algorithm or program for creating an appropriate answer to a received question.

[0736] A "generated answer" is a response that the system generates in response to a user's question.

[0737] The "means for determining whether it is YES or NO" is a natural language processing technique for determining whether the generated answer is affirmative (YES) or negative (NO).

[0738] "Chat generation AI" is an artificial intelligence system for generating dialogue with users.

[0739] The "means for updating the chat generation AI" is a function for correcting or adding learning data for the chat generation AI based on the judgment results.

[0740] "Feedback" refers to opinions and impressions provided by users regarding the content they have viewed.

[0741] "Natural language processing technology" is a technology that allows computers to analyze and understand human language.

[0742] The "means for determining whether the feedback is positive or negative" is an algorithm for determining whether the feedback is positive (YES) or negative (NO).

[0743] A "content recommendation engine" is a system for recommending appropriate content to users.

[0744] "Means for analyzing emotions" refers to technology for identifying emotions from user feedback.

[0745] "Means for recommending content according to emotions" is a function for selecting and recommending optimal content based on the user's emotions.

[0746] A system for implementing this invention includes means for receiving questions from users regarding smartphone operation, means for generating answers to the received questions, means for determining whether the generated answers are YES or NO, means for updating a chat generation AI based on the determination results, means for analyzing user feedback using natural language processing technology and determining whether the response is positive or negative, means for updating a content recommendation engine using the determination results as feedback, and means for analyzing user emotions and recommending content according to the emotions.

[0747] Hardware and software used

[0748] Hardware: Smartphone (iOS or ANDROID)

[0749] software:

[0750] Natural language processing libraries (e.g., spaCy, NLTK)

[0751] Machine learning frameworks (e.g. TensorFlow, PyTorch)

[0752] Sentiment analysis engine (e.g., IBM Watson(R), Microsoft(R) Azure(R) Text Analytics)

[0753] Data processing and calculation

[0754] Data collection

[0755] Collect feedback on the content you watch, which is saved in text format.

[0756] NLP analysis

[0757] Use a natural language processing library to analyze the feedback text and determine whether the text is positive (YES) or negative (NO).

[0758] Emotion analysis

[0759] Use a sentiment analysis engine to identify user sentiment from feedback text. Store sentiment data.

[0760] Machine Learning

[0761] The judgment results and sentiment data are input into a machine learning framework to update the content recommendation algorithm.

[0762] Content Recommendation

[0763] It uses an updated algorithm to recommend content based on user sentiment.

[0764] Specific examples

[0765] If a user provides feedback such as "This movie was great!", natural language processing technology will determine this feedback as positive (YES). Furthermore, a sentiment analysis engine will identify the user's emotion as "joy." Based on this information, the machine learning framework will update the content recommendation algorithm to recommend content related to "joy" to the user.

[0766] Example prompts to input to the generative AI model

[0767] "If a user gives feedback saying 'This movie was great!', would this be considered positive (YES)?"

[0768] If a user gives feedback saying, "This drama was boring," would that be considered negative (NO)?

[0769] In this way, by analyzing user feedback and building a content recommendation system that takes emotions into account, user satisfaction can be improved.

[0770] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0771] Step 1:

[0772] A user watches content using a smartphone and then enters feedback. The feedback is entered in text format and saved on the smartphone. The input data consists of the user's viewing history and feedback text.

[0773] Step 2:

[0774] The device sends the collected feedback text to the server, which then analyzes the received feedback text using a natural language processing library (e.g., spaCy, NLTK). The input data is the feedback text, and the output data is the judgment result, whether the feedback is positive (YES) or negative (NO).

[0775] Step 3:

[0776] The server uses a sentiment analysis engine (e.g., IBM Watson, Microsoft Azure Text Analytics) to identify the user's sentiment from the feedback text. The input data is the feedback text, and the output data is the user's sentiment (e.g., joy, anger, sadness).

[0777] Step 4:

[0778] The server inputs the judgment results and emotion data into a machine learning framework (e.g., TensorFlow, PyTorch) to update the content recommendation algorithm. The input data are the judgment results and emotion data, and the output data is the updated content recommendation model.

[0779] Step 5:

[0780] The server uses the updated content recommendation model to recommend content according to the user's emotions, where the input data is the user's emotion data and the output data is a list of recommended content.

[0781] Step 6:

[0782] The terminal displays the recommended content list received from the server to the user, and the user can select and view the recommended content. The input data is the recommended content list, and the output data is the user's viewing selection.

[0783] In this way, specific data processing and calculations are performed at each step, and user feedback is effectively utilized to achieve personalized content recommendations based on emotions.

[0784] 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.

[0785] 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> ) 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.

[0786] Another example of generative AI is Gemini (registered trademark) (Internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[0787] 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.

[0788] [Second embodiment]

[0789] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0790] 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.

[0791] 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).

[0792] 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.

[0793] 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.

[0794] 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).

[0795] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0796] 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.

[0797] 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.

[0798] 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.

[0799] 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.

[0800] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0801] "Example 1"

[0802] In one embodiment of the present invention, a web-based interface is provided as a means for receiving questions from users regarding smartphone operation. The interface is designed to allow users to input questions in text format. For example, if a user inputs the question, "How do I adjust the brightness of my smartphone screen?", the question is sent to the system.

[0803] "Example 2"

[0804] Next, a chat generation AI (Generative Pre-training Transformer) is used to generate answers to the received questions. Chat generation AI has the ability to learn from large amounts of text data and generate answers to questions in natural language. For example, in response to the above question, the chat generation AI would generate the answer, "You can adjust the screen brightness from the settings menu."

[0805] "Example 3"

[0806] The technology of natural language processing (NLP) is used as a means to determine whether the generated answer is YES or NO. Specifically, the generated answer is analyzed and its content is determined to be affirmative (YES) or negative (NO). For example, if the generated answer is "Yes, that is possible," this determination means determines that the answer is YES.

[0807] "Example 4"

[0808] Finally, a machine learning algorithm is used to update the chat generation AI based on the judgment result. Specifically, the judgment result is added to the learning data of the chat generation AI as feedback, improving the performance of the chat generation AI. For example, if the judgment result is YES, the question and answer pair is added to the learning data. This allows the chat generation AI to generate a more appropriate answer if the same question is asked again.

[0809] The processing flow of each embodiment will be described below.

[0810] "Example 1"

[0811] Step 1: A user inputs a smartphone operation question through a web-based interface, for example, "How do I adjust the brightness of my smartphone screen?"

[0812] Step 2: The system receives this question and sends it to the chat generation AI.

[0813] "Example 2"

[0814] Step 1: The chat generation AI analyzes the question and generates an appropriate answer, such as "You can adjust the screen brightness from the settings menu."

[0815] Step 2: The generated answer is sent to the system.

[0816] "Example 3"

[0817] Step 1: The system analyzes the generated answer and determines whether it is affirmative (YES) or negative (NO). For example, if the generated answer is "Yes, it is possible," the system determines that the answer is YES.

[0818] Step 2: The decision result is saved in the system.

[0819] "Example 4"

[0820] Step 1: The system adds the judgment results as feedback to the learning data of the chat generation AI.

[0821] Step 2: The chat generation AI is retrained using new training data, so that if the same question is asked again, the chat generation AI can generate a more appropriate answer.

[0822] Example 1

[0823] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0824] In conventional question-answering systems for operating information processing devices, users may not receive the appropriate answer even when they input a question. Furthermore, there is a lack of means to determine whether the generated answer is appropriate and to update the system, which leads to a problem of low user satisfaction. Furthermore, the interface for users to input questions can be difficult to use, which is an issue that does not improve the user experience.

[0825] 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.

[0826] In this invention, the server includes means for receiving a question from a user regarding the operation of an information processing device, means for generating an answer to the received question, means for determining whether the generated answer is YES or NO, means for updating the dialogue generation AI based on the determination result, means for providing a web-based interface for a user to input a question in text format, means for inputting the received question as a prompt sentence to the generative AI model, means for transmitting the generated answer to the user's terminal, and means for displaying the answer on the user's terminal. This allows a user to easily input a question and obtain an appropriate answer. Furthermore, the appropriateness of the generated answer can be determined and the system updated to improve the user experience.

[0827] A "user" is an individual or group that operates an information processing device and inputs a question.

[0828] An "information processing device" is an electronic device operated by a user, such as a smartphone or a personal computer.

[0829] The "means for receiving a question" is a function or device for receiving a question from a user as input.

[0830] The "means for generating an answer" is a function or device for creating an appropriate answer to a received question.

[0831] The "means for determining whether the generated answer is YES or NO" is a function or device for determining whether the generated answer is appropriate.

[0832] "Dialogue generation artificial intelligence" is an artificial intelligence system for generating dialogue with users.

[0833] The "means for updating the dialogue generation artificial intelligence" is a function or device for updating the data or algorithm of the dialogue generation artificial intelligence based on the appropriateness of the generated answers.

[0834] A "web-based interface" is a web page or application that a user accesses and enters a query over the Internet.

[0835] A "prompt" is a form of question that is input to a generative AI model.

[0836] A "generative artificial intelligence model" is an artificial intelligence model that generates answers in natural language to questions from users.

[0837] A "user terminal" is an information processing device used by a user.

[0838] The "means for displaying an answer" is a function or device for displaying the generated answer on the user's terminal.

[0839] This invention is a system that receives questions from users about the operation of information processing devices, generates appropriate answers, and provides them to the users. This system operates in cooperation with a server, a terminal, and a user.

[0840] System configuration

[0841] Hardware and Software

[0842] Server: A server with high-performance computing power is required to receive questions, run generative AI models, generate answers, and send them.

[0843] Terminal: An information processing device used by a user, such as a smartphone or PC. The terminal communicates with the server via a web browser.

[0844] Generative AI models: For example, using advanced natural language processing models such as OpenAI's GPT-4.

[0845] Data processing and calculation

[0846] Receiving questions

[0847] The user accesses the specified URL using the device's web browser. A web-based interface appears, and the user enters a text question, for example, "How do I adjust the brightness of my smartphone screen?"

[0848] Submit a Question

[0849] When a user enters a question and clicks the submit button, the device sends the question to the server as an HTTP request.

[0850] Parsing questions and generating prompts

[0851] The server analyzes the received question and converts it into a prompt suitable for the generative AI model, for example, "User asks: 'How do I adjust the brightness of my smartphone screen?'"

[0852] Generate answers

[0853] The server inputs a prompt into the generative AI model and generates an appropriate answer. The generative AI model generates an answer to the question based on the input prompt. For example, it generates an answer like, "To adjust the brightness of your smartphone screen, open the Settings app and select the 'Display' or 'Screen' option. From there, adjust the 'Brightness' slider to the desired brightness."

[0854] Submitting and viewing answers

[0855] The server sends the generated answer to the user's device as an HTTP response. The device displays the received answer on a web-based interface. The user can check the displayed answer and understand how to operate the information processing device.

[0856] Specific examples

[0857] Consider a case where a user types the question "How do I adjust the brightness of my smartphone screen?" This question is input to a generative AI model as a prompt like this:

[0858] Example prompt sentence:

[0859] A user asks: "How do I adjust the brightness of my smartphone screen?"

[0860] The generative AI model analyzes this prompt and generates the following answer:

[0861] To adjust the brightness of your phone's screen, open the Settings app and select the "Display" or "Screen" option. From there, adjust the "Brightness" slider to your desired brightness.

[0862] This response is sent to the user's device via the server and displayed to the user, who can then adjust the brightness of their smartphone screen.

[0863] In this way, users can easily enter their questions and get relevant answers, and the appropriateness of the generated answers can be assessed and the system updated to improve the user experience.

[0864] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0865] Step 1:

[0866] A user accesses a web-based interface.

[0867] Specific operation: The user opens a web browser on their smartphone or computer and accesses the specified URL.

[0868] Input: Access request from the user's device.

[0869] Output: A web-based interface is displayed on the user's terminal.

[0870] Step 2:

[0871] The user enters a question in text format.

[0872] What it does: A user types a question into a text box displayed on a web-based interface, for example, "How do I adjust the brightness of my smartphone screen?"

[0873] Input: The question text entered by the user.

[0874] Output: The question text is displayed on the interface.

[0875] Step 3:

[0876] The terminal sends the entered question to the server.

[0877] Specific operation: When the user clicks the submit button, the terminal sends the entered question to the server as an HTTP request.

[0878] Input: The question text entered by the user.

[0879] Output: An HTTP request containing the question text is sent to the server.

[0880] Step 4:

[0881] The server receives the question and inputs it as a prompt to the generative AI model.

[0882] How it works: The server analyzes the received question and converts it into a prompt suitable for the generative AI model, for example, "User asks: 'How do I adjust the brightness of my smartphone screen?'"

[0883] Input: The question text received as an HTTP request.

[0884] Output: A prompt to be fed into the generative AI model.

[0885] Step 5:

[0886] A generative AI model generates answers to questions.

[0887] Specific operation: The server inputs a prompt sentence into a generative AI model (e.g., OpenAI's GPT-4) and generates an appropriate answer.

[0888] Input: The prompt statement.

[0889] Output: The answer text generated by the generative AI model.

[0890] Step 6:

[0891] The server receives the generated response and sends it to the user's terminal.

[0892] Specific operation: The server sends the generated answer to the user's terminal as an HTTP response.

[0893] Input: Answer text generated by the generative AI model.

[0894] Output: HTTP response containing the answer text.

[0895] Step 7:

[0896] The terminal displays the answer to the user.

[0897] Specific operation: The user's device displays the answer received from the server on a web-based interface. The user can check the displayed answer and understand how to operate the information processing device.

[0898] Input: The answer text received from the server.

[0899] Output: Answer text displayed on the web-based interface.

[0900] (Application example 1)

[0901] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0902] While existing smartphone question-answering systems can provide appropriate answers to questions entered by users, they are unable to adequately respond to questions about electronic payments. Furthermore, the dialogue generation AI is not automatically updated to improve the quality of the generated answers, which can lead to a decline in the accuracy of the system.

[0903] 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.

[0904] In this invention, the server includes means for receiving questions from users regarding smartphone operation, means for generating answers to the received questions, means for determining whether the generated answers are YES or NO, means for updating the dialogue generation AI based on the determination result, means for receiving questions from users regarding electronic payments, means for generating answers to the received questions regarding electronic payments using a generative AI model, and means for displaying the generated answers to the users. This enables users to obtain appropriate answers to questions regarding electronic payments, and further enables the accuracy of the system to be improved by automatically updating the dialogue generation AI.

[0905] "User" means an individual or entity that uses the System to enter questions and receive answers.

[0906] "Smartphone operation" refers to operations and procedures related to smartphone functions and settings.

[0907] "Means for receiving questions" refers to the interface or function that allows the system to receive questions entered by the user.

[0908] "Means for generating answers" refers to algorithms or programs for generating appropriate answers to received questions.

[0909] "Means for determining whether the generated answer is YES or NO" refers to a function for determining whether the generated answer is appropriate.

[0910] "Dialogue generation artificial intelligence" refers to an artificial intelligence system for generating dialogue with users.

[0911] "Means for updating the dialogue generation AI" refers to a function for updating the learning data of the dialogue generation AI in order to improve the quality of the generated answers.

[0912] "Electronic payment" refers to payment transactions conducted over the Internet.

[0913] A "generative AI model" refers to a model that uses artificial intelligence to generate answers to questions.

[0914] "Means for displaying answers to the user" refers to the interface or functionality for visually presenting the generated answers to the user.

[0915] The system for carrying out this invention receives questions from a user regarding smartphone operation and electronic payment, generates appropriate answers, and displays them to the user. A specific embodiment of this system will be described below.

[0916] System Configuration

[0917] The system consists of the following main components:

[0918] 1. User device: The device through which the user enters their question, such as a smartphone or computer.

[0919] 2. Server: A central processing unit that receives questions, generates answers using generative AI models, and displays them to the user.

[0920] 3. Generative AI model: An artificial intelligence model for generating answers to questions, such as OpenAI's GPT-3.

[0921] Program processing

[0922] The server performs the process in the following steps.

[0923] 1. Receiving a question: The server receives a question sent from the user's terminal. The question is entered in text format.

[0924] 2. Answer generation: The received question is input into the generative AI model, which generates an appropriate answer. The generative AI model generates the best answer based on the content of the question.

[0925] 3. Displaying the answer: The generated answer is sent to the user's device and displayed to the user.

[0926] 4. Evaluate the answer: The server determines whether the generated answer is appropriate, for example, by rating it based on user feedback.

[0927] 5. AI Update: Based on the evaluation results, the learning data of the dialogue generation AI is updated, thereby improving the accuracy of the system.

[0928] Hardware and software used

[0929] Hardware: Cloud servers (e.g., Amazon Web Services or Google Cloud Platform)

[0930] Software: Flask (Python web framework), OpenAI API

[0931] Specific examples

[0932] When a user enters the question "How do I register a credit card?", the server processes the following:

[0933] 1. Receiving a question: The user's device sends the question "How do I register a credit card?" to the server.

[0934] 2. Generate an answer: The server inputs this question into a generative AI model and generates an answer like this:

[0935] To register a credit card, select "Payment Methods" from the app's settings menu and click "Add Credit Card." Then enter your card information and press the confirmation button.

[0936] 3. Displaying the answer: The generated answer is sent to the user's device and displayed to the user.

[0937] Prompt Sentence Examples

[0938] Electronic Payment Questions: How do I register my credit card?

[0939] answer:

[0940] In this way, users can get appropriate answers to questions about electronic payments, and the accuracy of the system can be improved by automatically updating the dialogue generation artificial intelligence.

[0941] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0942] Step 1:

[0943] The user uses the terminal to input a question. The input question is in text format, such as "How do I register a credit card?" The input question is sent from the terminal to the server.

[0944] Step 2:

[0945] The server receives the question sent from the terminal. The received question is saved as text data on the server. The input here is the question text from the user, and the output is the question data saved on the server.

[0946] Step 3:

[0947] The server inputs the received question into a generative AI model. For example, OpenAI's GPT-3 is used as the generative AI model. Based on the input question, the generative AI model generates an appropriate answer. Here, the input is the question text, and the output is the generated answer text.

[0948] Step 4:

[0949] The server receives the answer obtained from the generative AI model and sends it to the user's device. The user's device displays the received answer. The input here is the generated answer text, and the output is the answer displayed on the user's device.

[0950] Step 5:

[0951] The user reviews the displayed answers and provides feedback, such as a "YES" or "NO" rating, based on whether the answer is correct. The feedback is sent from the device to the server.

[0952] Step 6:

[0953] The server receives feedback from users and updates the learning data of the dialogue generation AI. The input here is user feedback, and the output is updated learning data. This improves the accuracy of the system.

[0954] In this way, users can receive appropriate answers to questions about electronic payments, and the accuracy of the system can be improved by automatically updating the dialogue generation artificial intelligence.

[0955] Example 2

[0956] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0957] Conventional information processing devices have had difficulty quickly providing appropriate answers to user questions. They also lacked effective means for improving the quality of generated answers. Furthermore, they lacked a mechanism for determining whether a generated answer was appropriate and updating the generative AI model based on the results.

[0958] 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.

[0959] In this invention, the server includes means for receiving a question from a user regarding the operation of an information processing device, means for preprocessing the received question, means for inputting the preprocessed question into a generative AI model, means for the generative AI model to generate an answer to the question, and means for transmitting the generated answer to the user. This makes it possible to provide a prompt and appropriate answer to a question from a user. It also provides an effective means for improving the quality of the generated answer, and the accuracy of the answer can be continuously improved by updating the generative AI model.

[0960] A "user" is an individual or group that operates an information processing device and inputs a question.

[0961] An "information processing device" is a device operated by a user, which inputs questions and receives answers.

[0962] The "means for receiving a question" is a function for receiving a question input by a user.

[0963] "Preprocessing means" is a function that processes received questions, such as normalizing them and deleting unnecessary information.

[0964] A "generative AI model" is an artificial intelligence model that learns from large amounts of text data and generates answers to questions in natural language.

[0965] "Means for inputting questions into the generative AI model" refers to a function for passing preprocessed questions to the generative AI model.

[0966] The "means for generating an answer" is the function that enables the generative AI model to generate an appropriate answer based on the question.

[0967] The "means for transmitting an answer to a user" is a function for transmitting the generated answer to the user's information processing device.

[0968] MODE FOR CARRYING OUT THE INVENTION

[0969] This invention relates to a system that provides prompt and appropriate answers to questions from users about the operation of information processing devices. In this system, a user inputs a question through an information processing device, and an answer to the question is generated using a generative AI model and provided to the user.

[0970] Hardware and software used

[0971] Hardware:

[0972] Information processing device: A device operated by a user (e.g., smartphone, tablet, PC, etc.)

[0973] Server: A server that receives questions, preprocesses them, feeds them into the generative AI model, and generates and sends answers.

[0974] software:

[0975] Generative AI models: Large-scale text generation models such as Generative Pre-training Transformer (GPT)

[0976] Preprocessing software: Software for normalizing text and removing unnecessary information

[0977] Data processing and calculation

[0978] The server processes the query received from the user in the following steps:

[0979] 1. Receiving Questions:

[0980] The server receives a natural language question submitted by the user, which is sent to the server as text data.

[0981] 2. Question preprocessing:

[0982] The server preprocesses the received query, which includes normalizing the text (e.g., converting uppercase to lowercase, removing unnecessary whitespace, etc.).

[0983] 3. Input to the generative AI model:

[0984] The server inputs the preprocessed questions into a generative AI model that has been trained from large amounts of text data and is capable of generating answers to natural language questions.

[0985] 4. Generate answers:

[0986] The generative AI model generates appropriate answers based on the questions entered. For example, if a user asks, "How do I adjust the screen brightness?", the model generates the answer, "You can adjust the screen brightness from the settings menu."

[0987] 5. Submit your response:

[0988] The server transmits the generated answer to the user, who can receive the answer through an information processing device.

[0989] Specific examples

[0990] Example prompt sentence:

[0991] How do I adjust the screen brightness?

[0992] Process flow:

[0993] 1. User action: The user opens a chat app on their smartphone and types, "How do I adjust the screen brightness?"

[0994] 2. Device behavior: The smartphone sends this question to the server as an HTTP request.

[0995] 3. Server action: The server receives the question and performs text normalization, for example converting "How do I adjust the brightness of my screen?" to "How do I adjust the brightness of my screen?"

[0996] 4. Server operation: The server inputs the preprocessed questions into the generative AI model.

[0997] 5. How the generative AI model works: The model generates the answer, "You can adjust the screen brightness from the settings menu."

[0998] 6. Server Action: The server receives the generated answer.

[0999] 7. Server operation: The server sends the answer to the user's smartphone as an HTTP response.

[1000] 8. User Action: The user checks the answer displayed on the smartphone screen.

[1001] In this way, users can enter questions in natural language and get appropriate answers using generative AI models.

[1002] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1003] Step 1:

[1004] The user enters a question.

[1005] The user inputs a question into an input field of the information processing device. For example, the user inputs "Please tell me how to adjust the screen brightness." The input question is saved as text data in the terminal.

[1006] Step 2:

[1007] The terminal sends a question to the server.

[1008] The terminal sends the question entered by the user to the server as an HTTP request. A communication process is performed to send the input data (question text) to the server. The input is the user's question text, and the output is an HTTP request to the server.

[1009] Step 3:

[1010] The server receives and pre-processes the query.

[1011] The server receives questions sent from the terminal. The received questions are stored as text data on the server. The server then normalizes the text, for example, converting uppercase to lowercase and removing unnecessary spaces. The input is the received question text, and the output is the preprocessed question text.

[1012] Step 4:

[1013] The server inputs the preprocessed questions into the generative AI model.

[1014] The server inputs the preprocessed questions into a generative AI model. The generative AI model learns from a large amount of text data and has the ability to generate answers to natural language questions. The input is the preprocessed question text, and the output is the input data for the generative AI model.

[1015] Step 5:

[1016] A generative AI model generates the answer.

[1017] A generative AI model generates an appropriate answer based on the input question. For example, if the question is "How do I adjust the screen brightness?", the model generates the answer "You can adjust the screen brightness from the settings menu." The input is the input data to the generative AI model, and the output is the generated answer text.

[1018] Step 6:

[1019] The server receives the generated answer.

[1020] The server receives the answer generated by the generative AI model. The received answer is stored on the server as text data. The input is the generated answer text, and the output is the received data to the server.

[1021] Step 7:

[1022] The server sends the response to the user's terminal.

[1023] The server sends the generated answer to the user's terminal as an HTTP response. The input is the generated answer text, and the output is the HTTP response to the user's terminal.

[1024] Step 8:

[1025] The user receives the answer.

[1026] The user receives the answer sent from the server through the terminal. The answer is displayed on the terminal screen and the user can check it. The input is the HTTP response from the server, and the output is the answer text displayed on the terminal.

[1027] (Application example 2)

[1028] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1029] In brick-and-mortar stores, customers need to be able to quickly and accurately resolve their questions about products and services. However, traditional methods require store staff to handle the inquiries, which can take a long time and lead to a decline in customer satisfaction. This also increases the burden on staff, making efficient operations difficult.

[1030] 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.

[1031] In this invention, the server includes means for receiving questions from users regarding smartphone operation, means for generating answers to the received questions, means for determining whether the generated answers are YES or NO, means for updating the chat generation AI based on the determination result, means for receiving questions from customers in a physical store and generating answers in real time, and means for providing the generated answers to customers. This allows customers to receive answers to their questions quickly and accurately, reducing the burden on store staff and enabling efficient store operations.

[1032] "User" refers to any individual or entity that uses the System.

[1033] "Smartphone operation" refers to a series of operations and inputs performed using a smartphone.

[1034] "Means for receiving questions" refers to the interface or function that allows the system to receive questions from users.

[1035] "Means for generating answers" refers to algorithms or functions for generating appropriate answers to received questions.

[1036] "Means for determining whether it is YES or NO" refers to a function for determining whether the generated answer is positive or negative.

[1037] "Chat generation AI" refers to artificial intelligence that learns from large amounts of text data and generates answers to questions in natural language.

[1038] "Means for updating the chat generation AI" refers to the function for updating the learning data and algorithms of the chat generation AI based on the judgment results.

[1039] "Brick and mortar store" refers to a commercial establishment located in a physical location.

[1040] "Customers" refers to consumers and users who visit physical stores.

[1041] "Means for generating answers in real time" refers to a function that allows you to instantly generate an answer the moment a question is received.

[1042] "Means for providing answers to customers" refers to interfaces and functions for communicating generated answers to customers.

[1043] A system for implementing this invention includes means for receiving questions from users regarding smartphone operation, means for generating answers to the received questions, means for determining whether the generated answers are YES or NO, means for updating the chat generation AI based on the determination result, means for receiving questions from customers in a physical store and generating answers in real time, and means for providing the generated answers to customers.

[1044] Program processing explanation

[1045] The server provides an interface for receiving questions from users. This interface is implemented as a smartphone application. When a user inputs a question, the question is sent to the server.

[1046] The server sends the received question to a generative AI model, which generates an appropriate answer. This generative AI model is implemented using OpenAI's API. The generated answer is analyzed by the server and determined to be either YES or NO.

[1047] Based on the results of the judgment, the server updates the learning data of the chat generation AI. This update process is carried out to improve the accuracy of the AI ​​model.

[1048] In a physical store, when a customer uses a smartphone app to enter a question, the question is sent to the server in real time, and the server instantly generates an answer and provides it to the customer. This process allows customers to get answers to their questions quickly and accurately.

[1049] Specific examples

[1050] For example, a customer opens a smartphone app in a physical store and enters the following question:

[1051] "Do you have this item in stock?"

[1052] The application sends the following prompt to the generative AI model:

[1053] Customer Question: Is this item in stock?

[1054] answer:

[1055] The generative AI model returns the following answer:

[1056] Yes, this item is in stock and can be found in-store in Aisle 3.

[1057] In this way, customers can get fast and accurate information.

[1058] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1059] Step 1:

[1060] The user opens the smartphone app and enters a question.

[1061] Input: A question typed by the user (e.g., "Do you have this item in stock?")

[1062] Output: Question text

[1063] What happens: The user types a question into the app's text input field and presses the submit button.

[1064] Step 2:

[1065] The terminal sends the question text to the server.

[1066] Input: Question text

[1067] Output: Question data sent to the server

[1068] Specific operation: The smartphone app sends the question text to the server as an HTTP request.

[1069] Step 3:

[1070] The server receives the question and sends a prompt to the generative AI model.

[1071] Input: Question data sent to the server

[1072] Output: The prompt sent to the generative AI model

[1073] Specific operation: The server analyzes the question data, generates a prompt (e.g., "Customer question: Is this item in stock?\nAnswer:"), and sends it to the generative AI model.

[1074] Step 4:

[1075] A generative AI model generates an answer based on the prompt.

[1076] Input: prompt statement

[1077] Output: The generated answer (e.g., "Yes, we have this item in stock. It's in Aisle 3 of the store.")

[1078] What it does: The generative AI model analyzes the prompt and generates an appropriate response.

[1079] Step 5:

[1080] The server receives the generated response and determines whether it is YES or NO.

[1081] Input: Generated answer

[1082] Output: YES or NO result

[1083] What happens: The server analyzes the generated answer and determines whether the answer is positive or negative.

[1084] Step 6:

[1085] The server updates the learning data of the chat generation AI based on the judgment results.

[1086] Input: YES or NO result

[1087] Output: Updated training data

[1088] Specific operation: Based on the judgment results, the server updates the learning data of the chat generation AI.

[1089] Step 7:

[1090] The server sends the generated response to the terminal.

[1091] Input: Generated answer

[1092] Output: Answer data sent to the device

[1093] Specific operation: The server sends the generated answer to the terminal as an HTTP response.

[1094] Step 8:

[1095] The terminal displays the answer to the user.

[1096] Input: Answer data sent to the terminal

[1097] Output: The answer displayed to the user

[1098] Specific operation: The smartphone app receives the response data and displays it on the user's screen.

[1099] Example 3

[1100] Next, a description will be given of Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1101] In conventional systems, generating answers to user questions and determining whether the answers are appropriate is often done manually, which is problematic in efficiency. Furthermore, there is no feedback loop to improve the quality of the generated answers, making it difficult to improve the system's performance.

[1102] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1103] In this invention, the server includes means for receiving questions from users regarding their operations, means for inputting the received questions as prompt sentences to the generative AI model, means for the generative AI model to generate answers, means for determining whether the generated answers are YES or NO, and means for updating the chat generation AI based on the determination result. This makes it possible to automatically generate prompt and appropriate answers to questions from users and continuously improve the quality of those answers.

[1104] A "user" is an entity that operates the system and inputs questions.

[1105] An "operational question" is a query about a specific operation or piece of information that a user inputs into the system.

[1106] "Means for receiving questions" refers to the functions and methods by which the system receives questions from users.

[1107] A "generative AI model" is an artificial intelligence algorithm or system that generates answers based on input prompts.

[1108] A "prompt" is a question or instruction text that is input into a generative AI model.

[1109] "Means for generating an answer" refers to the functions and methods by which a generative AI model creates an answer based on a prompt.

[1110] "Means for determining whether the answer is YES or NO" refers to a function or method for analyzing and determining whether the generated answer is affirmative (YES) or negative (NO).

[1111] "Chat generation AI" is an artificial intelligence system that generates appropriate answers to users' questions.

[1112] "Means for updating the chat generation AI" refers to functions and methods for adding or modifying the learning data of the chat generation AI based on the judgment results and improving the performance of the system.

[1113] This invention is a system that receives a question about an operation from a user, generates an answer to that question, determines whether the generated answer is YES or NO, and updates the chat generation AI based on the result of that determination. Specific embodiments of this system are described below.

[1114] Hardware and software used

[1115] Hardware: Servers, terminals

[1116] Software: Natural Language Processing (NLP) libraries (e.g., spaCy, NLTK), machine learning algorithms (e.g., TensorFlow, PyTorch)

[1117] System configuration

[1118] 1. The user enters a question

[1119] The user types a question into an input field on the device, for example, "Is this item in stock?"

[1120] 2. The device sends a question to the server

[1121] The device sends the question entered by the user to the server. Specifically, it sends the question data to the server using an HTTP request.

[1122] 3. The server inputs the question as a prompt to the generative AI model.

[1123] The server inputs the received question as a prompt to the generative AI model. For example, the question "Is this product in stock?" is passed to the generative AI model.

[1124] 4. Generative AI models generate answers

[1125] The generative AI model generates an answer based on the prompt, for example, "Yes, we have it in stock."

[1126] 5. The server parses the generated answer

[1127] The server uses NLP technology to analyze the generated answer. Specifically, it determines whether the answer is positive (YES) or negative (NO). For example, it analyzes the answer "Yes, we have it in stock" and determines that it is YES.

[1128] 6. The server gives feedback on the decision result

[1129] The server feeds back the results of its judgment to the chat generation AI. Specifically, the judgment results are added to the training data and used to improve the performance of the chat generation AI. For example, the question "Is this product in stock?" and the answer "Yes, it is in stock" are added to the training data.

[1130] 7. The server updates the chat generation AI

[1131] The server updates the chat generation AI based on the feedback, so that if the same question is asked again, it can generate a more appropriate answer.

[1132] Specific examples

[1133] User asks: "Is this item in stock?"

[1134] Prompt for generative AI model: "Is this item in stock?"

[1135] Generated Answer: "Yes, we have it in stock"

[1136] Server decision: Determines that the answer is YES

[1137] Feedback: Add the question "Is this item in stock?" and the answer "Yes, it is in stock" to the training data

[1138] In this way, the system can generate appropriate answers to user questions and continuously learn to improve its performance. The flow of the identification process in the third embodiment will be described with reference to FIG.

[1139] Step 1:

[1140] The user enters a question.

[1141] The user enters a question into an input field on the device, for example, "Is this item in stock?" The entered question is temporarily stored in the device's memory.

[1142] Step 2:

[1143] The terminal sends a question to the server.

[1144] The terminal sends the question entered by the user to the server as an HTTP request. The input data is sent to the server in text format. The server receives this request and analyzes the question data.

[1145] Step 3:

[1146] The server inputs the question as a prompt into the generative AI model.

[1147] The server inputs the received question to the generative AI model as a prompt sentence. For example, the question "Is this product in stock?" is passed to the generative AI model. The input data is in text format and is converted into a format suitable for the generative AI model.

[1148] Step 4:

[1149] A generative AI model generates the answer.

[1150] The generative AI model generates an answer based on the prompt sentence, for example, "Yes, we have it in stock." The generated answer is returned to the server in text format.

[1151] Step 5:

[1152] The server parses the generated answer.

[1153] The server uses NLP technology to analyze the generated answer. Specifically, it determines whether the answer is affirmative (YES) or negative (NO). For example, it analyzes the answer "Yes, we have it in stock" and determines that it is YES. The input data is the generated answer, and the output data is the YES or NO judgment result.

[1154] Step 6:

[1155] The server will provide feedback on the results of the decision.

[1156] The server feeds back the judgment results to the chat generation AI. Specifically, the judgment results are added to the training data and used to improve the performance of the chat generation AI. For example, the question "Is this product in stock?" and the answer "Yes, it is in stock" are added to the training data. The input data are question and answer pairs, and the output data is the updated training data.

[1157] Step 7:

[1158] The server updates the chat generation AI.

[1159] The server updates the chat generation AI based on the feedback, so that it can generate more appropriate answers when the same question is asked again. The input data is updated learning data, and the output data is an improved chat generation AI.

[1160] (Application example 3)

[1161] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1162] Conventional dialogue generation AIs in information processing devices have low accuracy in responding to user questions, making it difficult to improve user satisfaction. Furthermore, they lack a means to determine whether a generated response is positive or negative, preventing effective updating of the learning data of dialogue generation AIs. Therefore, there is a need to provide appropriate responses to user questions and improve the performance of dialogue generation AIs.

[1163] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1164] In this invention, the server includes means for receiving questions from a user regarding operation of the information processing device, means for generating answers to the received questions, means for determining whether the generated answers are positive or negative, means for updating the dialogue generation artificial intelligence based on the determination result, and means for saving the determination result as feedback, thereby making it possible to provide appropriate answers to user questions and improve the performance of the dialogue generation artificial intelligence.

[1165] An "information processing device" is a device that inputs, processes, and outputs data, and includes smartphones, computers, etc.

[1166] "Question receiving means" refers to a function or device for receiving questions from users, and includes methods such as text input and voice input.

[1167] "Answer generation means" refers to a function or device for generating an appropriate answer to a received question.

[1168] The "judgment means" is a function or device for judging whether the generated answer is positive or negative.

[1169] "Dialogue generation artificial intelligence" is an artificial intelligence system for generating dialogue with users.

[1170] The "update means" refers to a function or device for updating the learning data of the dialogue generation artificial intelligence based on the judgment results.

[1171] The "feedback storage means" is a function or device for storing the judgment results as feedback.

[1172] "Character input" refers to a method of inputting characters using a keyboard or touch screen.

[1173] "Training data" is a data set used by artificial intelligence to learn.

[1174] A system for implementing this invention includes means for receiving questions from a user regarding the operation of an information processing device, means for generating answers to the received questions, means for determining whether the generated answers are positive or negative, means for updating the dialogue generation artificial intelligence based on the determination results, and means for saving the determination results as feedback.

[1175] Program processing explanation

[1176] Hardware and software used

[1177] Hardware: Smartphone

[1178] Software: Python, OpenAI API, TextBlob

[1179] Data processing and calculation

[1180] 1. Question input: The user inputs a question into the smartphone app. For example, "Can I return this item?"

[1181] 2. Answer generation using generative AI model: The server uses OpenAI's API to generate an answer to the question, for example, "Yes, you can return it within 30 days."

[1182] 3. Natural Language Processing: The server uses TextBlob to perform sentiment analysis on the generated answer and determine whether it is positive (YES) or negative (NO). For example, if the generated answer is "Yes, you can return it within 30 days," it is determined to be positive (YES).

[1183] 4. Save feedback: The server saves the question and answer pairs in a text file and uses them as training data for future use. For example, it saves data such as "Question: Can I return this product?", "Answer: Yes, you can return it within 30 days," and "Result: YES."

[1184] Specific examples

[1185] Question: "Can I return this item?"

[1186] Generated Answer: "Yes, you can return it within 30 days."

[1187] Judgment result: YES

[1188] Prompt Sentence Examples

[1189] User: Can I return this item?

[1190] AI: Yes, you can return it within 30 days.

[1191] In this way, the system can provide appropriate answers to user questions and improve the performance of dialogue generation artificial intelligence.

[1192] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1193] Step 1:

[1194] The user inputs a question using a smartphone device.

[1195] Input: A user types into an app on their smartphone, "Can I return this item?"

[1196] Output: The entered question is sent to the server as text data.

[1197] Specific operation: The user enters a question using the smartphone keyboard and presses the send button, which sends the question to the server.

[1198] Step 2:

[1199] The server uses a generative AI model to generate an answer to the question it receives.

[1200] Input: The server receives the question "Can I return this item?" submitted by the user.

[1201] Output: The answer "Yes, returns are accepted within 30 days" is generated using a generative AI model (OpenAI API).

[1202] Specific behavior: The server sends a question to the OpenAI API and receives the answer returned by the API.

[1203] Step 3:

[1204] The server analyzes the generated answer using natural language processing technology to determine whether it is positive or negative.

[1205] Input: The server receives the generated answer "Yes, you can return it within 30 days."

[1206] Output: The answer is determined to be affirmative (YES) using a TextBlob.

[1207] What it does: The server uses the TextBlob library to perform sentiment analysis on the answer and determines it to be positive by getting a positive sentiment score.

[1208] Step 4:

[1209] The server updates the learning data of the dialogue generation artificial intelligence based on the judgment results.

[1210] Input: Server receives question "Can I return this item?", answer "Yes, you can return it within 30 days", and verdict "YES".

[1211] Output: Question and answer pairs are saved as training data.

[1212] Specific operation: The server saves the question and answer pairs in a text file or database and uses them as learning data for future use.

[1213] Step 5:

[1214] The server stores the judgment result as feedback.

[1215] Input: The server receives the verdict "YES".

[1216] Output: The judgment result is saved as feedback data.

[1217] Specific operation: The server stores the feedback data in text files or a database to help improve the performance of the dialogue generation AI.

[1218] 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.

[1219] "Example 1"

[1220] As one embodiment of the present invention, a system is provided that includes means for receiving questions from users regarding smartphone operation, means for generating answers to the received questions, means for determining whether the generated answers are YES or NO, means for updating a chat generation AI based on the determination result, and an emotion engine for recognizing the user's emotions.

[1221] "Example 2"

[1222] The emotion engine analyzes emotions from user text input. For example, if a user types the question "How do I adjust the brightness of my smartphone screen?" with anger, the emotion engine will recognize that anger.

[1223] "Example 3"

[1224] The chat generation AI's update method updates the learning data of the chat generation AI based on the analysis results of the emotion engine. For example, if a user asks a question with anger, the learning data will be updated so that an answer that reflects that anger is generated.

[1225] "Example 4"

[1226] In this way, according to the present invention, more appropriate answers are generated that take the user's emotions into consideration, thereby improving user satisfaction. Furthermore, by reflecting the analysis results of the emotion engine in the training data, the system's ability to generate answers is improved.

[1227] The processing flow of each embodiment will be described below.

[1228] "Example 1"

[1229] Step 1: Receive questions from users regarding smartphone operation.

[1230] Step 2: Generate answers to the questions received.

[1231] Step 3: Determine whether the generated answer is YES or NO.

[1232] Step 4: Update the chat generation AI based on the results of the judgment.

[1233] Step 5: Use an emotion engine to recognize user emotions.

[1234] "Example 2"

[1235] Step 1: Analyze sentiment from user text input.

[1236] Step 2: Use the analysis results to recognize the user's emotions.

[1237] "Example 3"

[1238] Step 1: Update the learning data of the chat generation AI based on the analysis results of the emotion engine.

[1239] Step 2: Generate an answer to the user's question using the updated learning data.

[1240] Step 1: Generate answers that take user sentiment into account.

[1241] Step 2: Provide the generated answer to the user.

[1242] Step 3: Evaluate user satisfaction.

[1243] Example 1

[1244] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1245] Conventional question-answering systems for operating information processing devices have difficulty generating appropriate answers to user questions and lack a means to determine the accuracy of the generated answers, making it difficult to improve user satisfaction. Furthermore, the lack of a function to recognize the user's emotions and adjust the answers based on them has led to a problem of a poor user experience.

[1246] 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.

[1247] In this invention, the server includes means for receiving questions from a user regarding the operation of the information processing device, means for generating answers to the received questions, means for determining whether the generated answers are YES or NO, means for updating the dialogue generation AI based on the determination result, and means for recognizing the user's emotions, thereby enabling the server to provide appropriate and accurate answers to questions from the user and to respond in consideration of the user's emotions.

[1248] An "information processing device" is a device that inputs, processes, and outputs data, and includes computers, smartphones, etc.

[1249] "Means for receiving questions" refers to the interface or functionality for receiving text questions from users.

[1250] "Means for generating answers" refers to algorithms or artificial intelligence models that generate appropriate answers to received questions.

[1251] "Yes / No method" refers to the rules or algorithms used to evaluate the accuracy of the generated answer.

[1252] "Dialogue generation artificial intelligence" refers to an artificial intelligence system for generating dialogue with users.

[1253] "Means for updating the dialogue generation AI" refers to a function for updating the learning data and model of the dialogue generation AI based on the evaluation results of the generated answers.

[1254] "Emotion recognition means" refers to algorithms or engines that analyze and recognize user emotions from user input text or other data.

[1255] MODE FOR CARRYING OUT THE INVENTION

[1256] This invention is a system that receives questions from users about the operation of an information processing device, generates appropriate answers, evaluates the accuracy of the answers, and updates a dialogue generation artificial intelligence. Specific embodiments of this system are described below.

[1257] Enter and submit your question

[1258] A user inputs a question about the operation of an information processing device through a web-based interface. This interface is designed to allow the user to input a question in text format. For example, the user may input "How do I adjust the brightness of my smartphone screen?" This question is sent from the device to a server. The transmission uses the HTTPS protocol.

[1259] Question Analysis

[1260] The server analyzes the received question using natural language processing (NLP) techniques, such as tokenization, grammar analysis, and intent extraction. For example, a Python NLP library can be used for this analysis.

[1261] Generate answers

[1262] The server generates an answer using a generative AI model (for example, OpenAI's GPT-4) based on the analysis results. The generative AI model generates an appropriate answer based on the content of the question. For example, it generates the answer "Open the Settings app, select Display Settings, and adjust the brightness."

[1263] Evaluating answers

[1264] The server determines whether the generated answer is YES or NO using pre-defined rules and algorithms. For example, if the generated answer is determined to be accurate, it will be evaluated as YES.

[1265] Updates to chat generation AI

[1266] The server adds answers that are evaluated as YES to the database of the dialogue generation AI. If the answer is evaluated as NO, the server provides feedback to the generation AI model and corrects the answer. This update can be performed using a database management system, for example.

[1267] emotion recognition

[1268] The server uses an emotion engine to analyze the user's emotions. For example, if it determines that the user is in a difficult situation, it will reflect this information in generating future answers. For example, an emotion analysis library can be used for this emotion recognition.

[1269] Submitting and viewing answers

[1270] The server sends the generated answer to the device, which then displays the received answer to the user. For example, the answer may say, "Open the Settings app, select Display Settings, and adjust brightness."

[1271] Specific examples

[1272] A user types "How do I adjust the brightness of my smartphone screen?" into a web-based interface. The device sends this question to the server, which uses a generative AI model to generate the answer "Open the Settings app, select Display Settings, and adjust brightness." The server evaluates this answer, determines it to be YES, and updates the dialog generator. Finally, the server sends this answer to the device, which displays it to the user.

[1273] Prompt Sentence Examples

[1274] "How do I adjust the brightness of my smartphone screen?"

[1275] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1276] Step 1:

[1277] A user accesses a web-based interface and enters a textual question about the operation of the information processing device, such as "How do I adjust the brightness of my smartphone screen?" The entered question is temporarily stored in the device's memory.

[1278] Step 2:

[1279] The terminal sends the question entered by the user to the server using the HTTPS protocol. The data sent is a text question. The server receives this data and proceeds to the next processing step.

[1280] Step 3:

[1281] The server analyzes the received question using natural language processing (NLP) technology. Specifically, it performs tokenization, grammar analysis, and intent extraction. The input is a text question, and the output is structured data of the analyzed question. For example, keywords such as "smartphone," "screen brightness," and "how to adjust" are extracted.

[1282] Step 4:

[1283] The server generates an answer using a generative AI model (e.g., OpenAI's GPT-4) based on the analysis results. The input is the structured data of the analyzed question, and the output is a text answer. For example, the generated answer might be "Open the Settings app, select Display Settings, and adjust brightness."

[1284] Step 5:

[1285] The server determines whether the generated answer is YES or NO. This determination is made using pre-defined rules and algorithms. The input is the generated answer, and the output is the evaluation result of YES or NO. For example, if the generated answer is judged to be accurate, it will be evaluated as YES.

[1286] Step 6:

[1287] The server adds answers evaluated as YES to the database of the dialogue generation AI. If the answer is evaluated as NO, it provides feedback to the generative AI model and corrects the answer. The input is the evaluation result and the generated answer, and the output is an updated database of the dialogue generation AI.

[1288] Step 7:

[1289] The server uses an emotion engine to analyze the user's emotions. The input is the user's input text, and the output is the user's emotional state. For example, if the server determines that the user is in a difficult situation, it will reflect that information in generating future answers.

[1290] Step 8:

[1291] The server sends the generated answer to the device. The device receives the answer and displays it to the user. The input is the generated answer, and the output is a text answer that is displayed to the user. For example, the answer might be "Open the Settings app, select Display Settings, and adjust brightness."

[1292] (Application example 1)

[1293] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1294] Conventional question-answering systems for operating information terminals have difficulty providing appropriate answers to user questions and have been unable to provide feedback that takes into account the user's emotions. As a result, the user experience deteriorates and the system's continued use rate decreases.

[1295] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. In this invention, the server includes means for receiving a question from a user regarding information terminal operation, means for generating an answer to the received question, means for determining whether the generated answer is YES or NO, means for updating the dialogue generation AI based on the determination result, and an emotion engine for recognizing the user's emotions. This makes it possible to provide an appropriate answer to the user's question and also provide feedback that takes the user's emotions into consideration.

[1296] An "information terminal" is an electronic device that can be operated by a user, such as a smartphone or tablet.

[1297] The "means for receiving a question" is an interface for receiving a textual question from a user as input.

[1298] A "means for generating an answer" is an algorithm or program for generating an appropriate answer to a received question.

[1299] A "means for determining whether it is YES or NO" is the logic or program used to determine whether the generated answer is positive or negative.

[1300] "Dialogue generation artificial intelligence" is an artificial intelligence system for generating dialogue with users.

[1301] The "means for updating the dialogue generation AI" is a mechanism for updating the learning data of the dialogue generation AI based on the judgment results.

[1302] An "emotion engine" is software that recognizes and analyzes emotions from user input and dialogue.

[1303] The system for implementing this invention receives questions from users about how to operate an information terminal, generates appropriate answers to those questions, and further recognizes the user's emotions and provides feedback. A specific embodiment of this system will be described below.

[1304] The server provides a web-based interface as a means for receiving questions from users regarding the operation of the information terminal. This interface is designed to allow users to enter questions in text format. For example, if a user enters the question "How do I change the password for my smartphone's lock screen?", this question is sent to the server.

[1305] The server uses a generative AI model to generate answers to received questions. The generative AI model contains algorithms for generating appropriate answers to questions. For example, it uses OpenAI's API to provide specific instructions for answering questions.

[1306] To determine whether the generated answer is a YES or NO, the server has logic to analyze the content of the answer. This logic determines whether the answer is positive or negative. For example, it analyzes answers such as "Yes, I can" or "No, I can't."

[1307] Furthermore, the server has a means for updating the dialogue generation AI based on the judgment results. This means feeds back the generated answers and their evaluation results to the dialogue generation AI as learning data. This allows the system to continuously learn and provide more appropriate answers.

[1308] The server also includes an emotion engine that recognizes user emotions. This emotion engine is software that recognizes and analyzes emotions from user input and dialogue. For example, it uses the TextBlob library to analyze the emotion of user input text.

[1309] As a concrete example, the following prompt sentence can be input to a generative AI model:

[1310] "Answer your smartphone security settings question: How do I change my smartphone lock screen password?"

[1311] An example of the generative AI model's response to this prompt is as follows:

[1312] To change your phone's lock screen password, follow these steps:

[1313] 1. Open the Settings app.

[1314] 2. Select "Security" or "Lock screen and security."

[1315] 3. Select the "Screen Lock Type".

[1316] 4. Enter your current password and set a new password.

[1317] 5. Save your settings.

[1318] In this way, when a user inputs a specific question, the server can provide an appropriate answer and provide feedback that takes the user's emotions into account, which is expected to improve the user experience and increase the rate at which the system is used.

[1319] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1320] Step 1:

[1321] A user accesses a web-based interface using an information terminal and enters a question in text format. The entered question is sent to the server. The input data is the question text entered by the user, and the output data is the question text sent to the server.

[1322] Step 2:

[1323] The server inputs the received question text into the generative AI model, which then executes an algorithm to generate an appropriate answer to the question. The input data is the user's question text, and the output data is the generated answer text.

[1324] Step 3:

[1325] The server analyzes the generated answer text and determines whether the answer is YES or NO. Specifically, it uses logic to analyze the content of the answer text to determine whether it is positive or negative. The input data is the generated answer text, and the output data is the YES or NO judgment result.

[1326] Step 4:

[1327] The server updates the learning data of the dialogue generation AI based on the judgment result. Specifically, it feeds back the generated answer and its evaluation result to the dialogue generation AI. The input data is the generated answer text and the judgment result, and the output data is the updated learning data.

[1328] Step 5:

[1329] The server uses an emotion engine to recognize emotions from user input and dialogue. Specifically, it uses the TextBlob library to analyze the emotions of the user's input text. The input data is the user's input text, and the output data is the result of the emotion analysis.

[1330] Step 6:

[1331] The server provides feedback to the user based on the results of the sentiment analysis. For example, if the user is satisfied, it displays "The user seems satisfied," and if the user is dissatisfied, it displays "The user may be dissatisfied." The input data is the result of the sentiment analysis, and the output data is the feedback message provided to the user.

[1332] Example 2

[1333] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1334] In conventional systems, generating answers to user questions relied on simple YES / NO decisions, making it difficult to provide appropriate answers that took into account the user's emotions and the context of the question. Furthermore, the training data needed to improve the quality of the generated answers was not updated efficiently. This resulted in a poor user experience and a loss of system reliability.

[1335] 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.

[1336] In this invention, the server includes means for receiving a question related to an operation from a user, means for generating an answer to the received question, means for determining whether the generated answer is YES or NO, means for updating the generative AI model based on the determination result, means for analyzing emotions from the user's input text, means for sending a question to the generative AI model based on the emotion analysis result, and means for providing the generated answer to the user. This makes it possible to provide an appropriate answer that takes into account the user's emotions and the context of the question, and to efficiently update the learning data of the generative AI model.

[1337] "User" means any person or entity that uses the System to enter questions and receive answers.

[1338] "Operational questions" refer to questions that users ask about how to operate a particular device or system.

[1339] "Means for receiving" refers to a combination of hardware and software for receiving input from a user.

[1340] "Means for generating answers" refers to algorithms or programs for generating appropriate answers to questions received from users.

[1341] "Means for determining whether the generated answer is YES or NO" refers to the logic or program for determining whether the generated answer is affirmative or negative.

[1342] A "generative AI model" refers to an artificial intelligence model that can learn from large amounts of text data and generate answers to questions in natural language.

[1343] "Means of updating" refers to the processes and programs used to update the training data of a generative AI model with new information.

[1344] "Means for analyzing emotions" refers to algorithms or programs used to identify emotions from user input text.

[1345] "Means for sending questions to a generative AI model based on the results of sentiment analysis" refers to a process or program for sending questions in an appropriate format to a generative AI model, taking into account the results of sentiment analysis.

[1346] "Means for providing" refers to a combination of hardware and software for displaying or communicating generated answers to a user.

[1347] This invention is a system that provides appropriate answers to user questions about operations. The system includes a server, a terminal, a generative AI model, and an emotion analysis engine.

[1348] Hardware and software used

[1349] Hardware: Servers, terminals

[1350] Software: Generative AI model (Generative Pre-training Transformer), sentiment analysis engine

[1351] Specific operation of the system

[1352] 1. User inputs a question

[1353] The user types a question into the device's input field and presses the send button, for example, "How do I adjust the brightness of my smartphone screen?"

[1354] 2. Emotion analysis

[1355] The server receives the user's input text and sends it to a sentiment analysis engine. The sentiment analysis engine analyzes the text and identifies the emotion. For example, if the user's question contains anger, the sentiment analysis engine will recognize that anger exists.

[1356] 3. Handling Questions

[1357] The server sends the user's question along with the sentiment analysis results to the generative AI model. For example, a question like "How do I adjust the brightness of my smartphone screen?" is sent to the generative AI model.

[1358] 4. Answer generation

[1359] The generative AI model generates answers in natural language based on knowledge learned from large amounts of text data, such as "You can adjust the screen brightness from the settings menu."

[1360] 5. Providing answers

[1361] The server sends the generated answer to the user's device, which then displays the answer. For example, the user's device might display the answer, "You can adjust the screen brightness from the settings menu."

[1362] Examples and prompts

[1363] Specific examples

[1364] User Question: "How do I adjust the brightness of my smartphone screen?"

[1365] Sentiment analysis result: Anger

[1366] Generated answer: "You can adjust the screen brightness from the settings menu."

[1367] Prompt Sentence Examples

[1368] Prompt: "How do I adjust the brightness of my smartphone screen?"

[1369] This system combines a generative AI model and a sentiment analysis engine to provide quick and appropriate answers to user questions. The sentiment analysis engine analyzes the user's emotions, and the generative AI model generates answers in natural language based on the analysis results. This makes it possible to provide appropriate answers that take into account the user's emotions and the context of the question, and to efficiently update the learning data of the generative AI model.

[1370] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1371] Step 1:

[1372] The user enters a question into the input field on the terminal and presses the send button.

[1373] Input: User question text (e.g., "How do I adjust the brightness of my smartphone screen?")

[1374] Output: Question text sent from terminal to server

[1375] Step 2:

[1376] The server receives the user's input text and sends it to the sentiment analysis engine.

[1377] Input: Question text sent from the terminal

[1378] Output: Text sent to sentiment analysis engine

[1379] Step 3:

[1380] A sentiment analysis engine analyzes the text and identifies sentiment.

[1381] Input: Question text sent by the server

[1382] Data processing: Using text analysis algorithms to identify sentiment

[1383] Output: Sentiment analysis result (e.g., anger)

[1384] Step 4:

[1385] The server sends the user's question along with the sentiment analysis results to the generative AI model.

[1386] Input: Sentiment analysis results and question text

[1387] Output: Sending questions and sentiment analysis results to the generative AI model

[1388] Step 5:

[1389] The generative AI model generates answers in natural language based on knowledge learned from large amounts of text data.

[1390] Input: Question text sent from the server and sentiment analysis results

[1391] Data calculation: The process of generating answers using generative AI models

[1392] Output: Generated answer text (e.g. "You can adjust the screen brightness from the Settings menu.")

[1393] Step 6:

[1394] The server sends the generated answer to the user's terminal.

[1395] Input: Answer text from the generative AI model

[1396] Output: Sends the answer text to the user's terminal

[1397] Step 7:

[1398] The user's device displays the answer.

[1399] Input: Answer text sent from the server

[1400] Output: The answer displayed on the device screen (e.g., "You can adjust the screen brightness from the Settings menu.")

[1401] (Application example 2)

[1402] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1403] Conventional chat generation AI systems generate responses without considering the user's emotions or urgency, which can lead to problems such as reduced user satisfaction and trust. Furthermore, they sometimes fail to respond appropriately to security-related questions, making it difficult to ensure user safety.

[1404] 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.

[1405] In this invention, the server includes means for receiving questions from users regarding smartphone operation, means for generating answers to the received questions, means for determining whether the generated answers are YES or NO, means for updating the chat generation AI based on the determination result, means for analyzing emotions from text input by the user, means for determining the level of urgency based on the emotion analysis result, and means for taking action according to the level of urgency. This makes it possible to provide appropriate answers that take into account the user's emotions and the level of urgency, and to respond quickly and appropriately to security-related questions.

[1406] "User" means any individual or entity that uses the System.

[1407] "Questions about smartphone operation" are inquiries from users about smartphone functions and settings.

[1408] A "means for receiving a question" is an interface for receiving text input from a user.

[1409] The "means for generating answers" is a chat generation AI that generates appropriate answers to received questions.

[1410] The "means for determining whether the generated answer is YES or NO" is an algorithm for determining whether the generated answer is affirmative or negative.

[1411] "Means for updating the chat generation AI" refers to the process for updating the learning data of the chat generation AI based on the judgment results.

[1412] The "means for analyzing emotions" is an emotion engine for analyzing emotions from the user's text input.

[1413] The "means for determining the level of urgency" is an algorithm for assessing the level of urgency of the user based on the results of emotion analysis.

[1414] "Means for taking appropriate action according to the level of urgency" refers to a process for taking appropriate action according to the level of urgency.

[1415] The system for implementing this invention receives questions from users about smartphone operation, generates appropriate answers, and responds according to the user's emotions and level of urgency. Specific embodiments of this system are described below.

[1416] System configuration

[1417] The system consists of the following main components:

[1418] 1. User terminal: A device such as a smartphone that provides an interface for users to enter questions.

[1419] 2. Server: Receives questions, generates answers, performs sentiment analysis and urgency determination.

[1420] 3. Chat generation AI: Uses Generative Pre-training Transformer (GPT) to generate answers to user questions.

[1421] 4. Emotion Engine: Analyzes emotions from user text input using natural language processing tools such as TextBlob.

[1422] 5. Urgency judgment algorithm: Evaluate the user's urgency based on the results of sentiment analysis.

[1423] Program processing

[1424] The server receives questions sent from the user's device. The received question is first analyzed by an emotion engine to evaluate the user's emotion. Next, an urgency judgment algorithm evaluates the user's level of urgency based on the emotion analysis results.

[1425] The chat generation AI then generates an answer to the question. The content of the answer is evaluated by a means to determine whether the answer is YES or NO. Based on the evaluation results, the learning data of the chat generation AI is updated.

[1426] Hardware and software used

[1427] Hardware: Smartphone (user device), server

[1428] Software: Python, OpenAI API (GPT-3), TextBlob

[1429] Specific examples

[1430] A user uses their smartphone to type the question, "I've seen some suspicious people around my house recently. What should I do?" This question is sent to the server and analyzed by the emotion engine. The emotion analysis reveals that the user is feeling anxious. The urgency judgment algorithm determines that this situation is of high urgency.

[1431] The chat generation AI then generates a response such as, "Please contact the police immediately. Also, please warn your neighbors." This response is returned to the user, and at the same time, the learning data of the chat generation AI is updated.

[1432] Example prompt sentence:

[1433] User: I've noticed some suspicious people around my house recently. What should I do?

[1434] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1435] Step 1:

[1436] The user uses a smartphone to input a question. For example, the user might input a question like, "I've seen some suspicious people around my house recently. What should I do?" This input is sent to the server as text data.

[1437] Step 2:

[1438] The server receives questions sent by users. It passes the received text data to the emotion engine. The emotion engine uses TextBlob to analyze emotions from the text data. The analysis results are output as the degree of positivity or negativity of the emotion and the urgency.

[1439] Step 3:

[1440] The server executes an urgency determination algorithm based on the emotion analysis results. It receives the emotion analysis results as input and evaluates the urgency. For example, if the emotion is negative and the urgency is high, it is judged to be "high urgency." This urgency evaluation result is output.

[1441] Step 4:

[1442] Based on the urgency assessment results, the server passes the question to the chat generation AI. The chat generation AI uses Generative Pre-training Transformer (GPT) to generate an appropriate answer to the question. The generated answer is output as text data.

[1443] Step 5:

[1444] The server evaluates the content of the answer using a means to determine whether the generated answer is YES or NO. For example, if the generated answer is "Please contact the police immediately," the server determines whether this is YES or NO. The evaluation result is output.

[1445] Step 6:

[1446] The server updates the learning data of the chat generation AI based on the evaluation results. It receives the evaluation results as input and updates the learning data of the AI ​​model. This update improves the accuracy of answer generation from the next time onwards.

[1447] Step 7:

[1448] The server then sends the generated answer to the user's device. The user receives the answer on their smartphone and it is displayed. For example, the answer may read, "Please contact the police immediately. Also, please warn your neighbors."

[1449] Example 3

[1450] Next, a description will be given of Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1451] Conventional dialogue generation systems often provide inappropriate answers to user questions, resulting in low user satisfaction. Furthermore, it is difficult to generate answers that take the user's emotions into account, making it difficult to respond appropriately to the user's feelings. Furthermore, there is a lack of a way to determine whether the generated answers are positive or negative, which reduces the accuracy of the system.

[1452] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1453] In this invention, the server includes means for receiving questions related to operations from users, means for generating answers to the received questions, means for determining whether the generated answers are positive or negative, means for updating the dialogue generation AI based on the determination result, and means for analyzing the user's emotions using an emotion analysis engine and reflecting the results in the learning data. This makes it possible to provide appropriate answers to the user's questions that correspond to their emotions, thereby improving user satisfaction.

[1454] "User" means an individual or entity that utilizes the System to enter questions and receive answers.

[1455] "Questions about operations" are inquiries about specific operations or functions that users perform on the system.

[1456] The "receiving means" refers to a method or device by which the system receives a question from a user.

[1457] A "means for generating an answer" is a method or device for generating an appropriate answer to a received question.

[1458] A "generated answer" is a response that the system generates in response to a user's question.

[1459] A "means for determining whether a response is affirmative or negative" is a method or device for identifying whether the generated response is affirmative (YES) or negative (NO).

[1460] "Dialogue generation artificial intelligence" is an artificial intelligence system that generates appropriate answers to users' questions.

[1461] An "updating means" is a method or device for improving the training data or algorithms in order to improve the performance and accuracy of the system.

[1462] An "emotion analysis engine" is a technology that analyzes emotions from user input and reflects the results in the system.

[1463] "Training data" is a dataset used by the dialogue generation AI to learn, and includes question and answer pairs and the results of sentiment analysis.

[1464] MODE FOR CARRYING OUT THE INVENTION

[1465] This invention is a system that receives questions about operations from users, generates appropriate answers, and determines whether the answers are positive or negative. The system aims to provide more appropriate answers by updating the dialogue generation AI, analyzing the user's emotions, and reflecting them in the learning data.

[1466] Hardware and software used

[1467] The server uses the following hardware and software:

[1468] Hardware: High-performance server machine (e.g., Intel Xeon processor, 32GB RAM or more)

[1469] Software: Python, NLTK library, SpaCy, TensorFlow, PyTorch

[1470] Data processing and calculation

[1471] The server receives a question from the user and inputs it as a prompt into the generative AI model. The generative AI model generates an answer based on the prompt. The generated answer is analyzed using NLP technology to determine whether it is positive or negative. The result of the judgment is then added as feedback to the learning data of the dialogue generation AI. Furthermore, an emotion analysis engine is used to analyze the user's emotions, and the results are reflected in the learning data.

[1472] Specific examples

[1473] The user enters the question, "Can I return this item?" The device sends this question to the server. The server receives the question and inputs the prompt sentence, "User: Can I return this item?" into the generative AI model. The generative AI model generates the answer, "Yes, you can return it." The server analyzes this answer and determines it to be affirmative (YES). The server then adds this question and answer pair to the training data. Furthermore, the server uses a sentiment analysis engine to analyze the user's sentiment and reflects it in the training data. Finally, the server uses the updated dialogue generation AI to provide the user with the optimal answer, "Yes, you can return it."

[1474] Prompt Sentence Examples

[1475] User: Can I return this item?

[1476] Generative AI model: Yes, returns are possible.

[1477] In this way, the system can provide appropriate and emotionally appropriate answers to user questions, thereby improving user satisfaction. The flow of the identification process in the third embodiment will be described with reference to FIG.

[1478] Step 1:

[1479] The user enters a question.

[1480] The user uses the terminal to input a question. For example, the user inputs, "Can I return this product?" The input question is saved as text data on the terminal.

[1481] Step 2:

[1482] The terminal sends a question to the server.

[1483] The terminal sends the user's question to the server. HTTP or WebSocket is used as the communication protocol. The input is the user's question text, and the output is a transmission completion notification to the server.

[1484] Step 3:

[1485] The server receives the question and inputs it as a prompt to the generative AI model.

[1486] The server inputs the received question to the generative AI model as a prompt. For example, the prompt might be "User: Can I return this item?" The input is the user's question text, and the output is the prompt to the generative AI model.

[1487] Step 4:

[1488] A generative AI model generates the answer.

[1489] The generative AI model generates an answer based on the prompt text. For example, it generates an answer such as "Yes, returns are possible." The input is the prompt text, and the output is the generated answer text.

[1490] Step 5:

[1491] The server analyzes the generated answer and determines whether it is positive or negative.

[1492] The server uses NLP techniques to analyze the generated answer. For example, if the answer is "Yes, returns are allowed," the server determines this as a positive (YES) answer. The input is the generated answer text, and the output is a positive (YES) or negative (NO) decision result.

[1493] Step 6:

[1494] The server adds the judgment results as feedback to the learning data of the dialogue generation artificial intelligence.

[1495] The server adds the judgment result as feedback to the learning data of the dialogue generation AI. For example, the question and answer pair "Can I return this product?" and "Yes, you can return it" are added to the learning data. The input is the question and answer pair and the judgment result, and the output is the updated learning data.

[1496] Step 7:

[1497] The server uses an emotion analysis engine to analyze the user's emotions and reflects the results in the learning data.

[1498] The server uses a sentiment analysis engine to analyze the user's sentiment. For example, if a user asks a question with anger, the server updates the training data so that answers that reflect that anger are generated. The input is the user's question text and the sentiment analysis results, and the output is the updated training data.

[1499] Step 8:

[1500] The server uses the updated dialogue generation artificial intelligence to provide the user with the most appropriate answer.

[1501] The server uses the updated dialogue generation AI to provide the optimal answer to the user, thereby improving user satisfaction. The input is the user's question text and the updated learning data, and the output is the optimal answer text to the user.

[1502] (Application example 3)

[1503] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1504] Conventional content distribution services lack systems that effectively utilize user feedback to recommend content. Furthermore, personalized content recommendations that take user emotions into account are not implemented, resulting in a decline in user satisfaction. Furthermore, chat generation AI is not updated enough based on user feedback, limiting its ability to generate appropriate responses.

[1505] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means. In this invention, the server includes means for receiving a question from a user regarding smartphone operation, means for generating an answer to the received question, means for determining whether the generated answer is YES or NO, means for updating the chat generation AI based on the determination result, means for analyzing the user's feedback using natural language processing technology and determining whether the response is positive or negative, means for updating the content recommendation engine using the determination result as feedback, and means for analyzing the user's emotions and recommending content according to the emotions. This enables effective use of user feedback and personalized content recommendations that take the user's emotions into consideration. Furthermore, the chat generation AI is appropriately updated, improving its ability to generate more appropriate answers.

[1506] "User" means any individual or entity that uses the System.

[1507] "Questions about smartphone operation" are inquiries that users make to the system regarding how to use or functions of their smartphones.

[1508] "Means for receiving questions" refers to the interface or function that allows the system to receive questions from users.

[1509] A "means for generating an answer" is an algorithm or program for creating an appropriate answer to a received question.

[1510] A "generated answer" is a response that the system generates in response to a user's question.

[1511] The "means for determining whether it is YES or NO" is a natural language processing technique for determining whether the generated answer is affirmative (YES) or negative (NO).

[1512] "Chat generation AI" is an artificial intelligence system for generating dialogue with users.

[1513] The "means for updating the chat generation AI" is a function for correcting or adding learning data for the chat generation AI based on the judgment results.

[1514] "Feedback" refers to opinions and impressions provided by users regarding the content they have viewed.

[1515] "Natural language processing technology" is a technology that allows computers to analyze and understand human language.

[1516] The "means for determining whether the feedback is positive or negative" is an algorithm for determining whether the feedback is positive (YES) or negative (NO).

[1517] A "content recommendation engine" is a system for recommending appropriate content to users.

[1518] "Means for analyzing emotions" refers to technology for identifying emotions from user feedback.

[1519] "Means for recommending content according to emotions" is a function for selecting and recommending optimal content based on the user's emotions.

[1520] A system for implementing this invention includes means for receiving questions from users regarding smartphone operation, means for generating answers to the received questions, means for determining whether the generated answers are YES or NO, means for updating a chat generation AI based on the determination results, means for analyzing user feedback using natural language processing technology and determining whether the response is positive or negative, means for updating a content recommendation engine using the determination results as feedback, and means for analyzing user emotions and recommending content according to the emotions.

[1521] Hardware and software used

[1522] Hardware: Smartphone (iOS or Android)

[1523] software:

[1524] Natural language processing libraries (e.g., spaCy, NLTK)

[1525] Machine learning frameworks (e.g. TensorFlow, PyTorch)

[1526] Sentiment analysis engine (e.g. IBM Watson, Microsoft Azure Text Analytics)

[1527] Data processing and calculation

[1528] Data collection

[1529] Collect feedback on the content you watch, which is saved in text format.

[1530] NLP analysis

[1531] Use a natural language processing library to analyze the feedback text and determine whether the text is positive (YES) or negative (NO).

[1532] Emotion analysis

[1533] Use a sentiment analysis engine to identify user sentiment from feedback text. Store sentiment data.

[1534] Machine Learning

[1535] The judgment results and sentiment data are input into a machine learning framework to update the content recommendation algorithm.

[1536] Content Recommendation

[1537] It uses an updated algorithm to recommend content based on user sentiment.

[1538] Specific examples

[1539] If a user provides feedback such as "This movie was great!", natural language processing technology will determine this feedback as positive (YES). Furthermore, a sentiment analysis engine will identify the user's emotion as "joy." Based on this information, the machine learning framework will update the content recommendation algorithm to recommend content related to "joy" to the user.

[1540] Example prompts to input to the generative AI model

[1541] "If a user gives feedback saying 'This movie was great!', would this be considered positive (YES)?"

[1542] If a user gives feedback saying, "This drama was boring," would that be considered negative (NO)?

[1543] In this way, by analyzing user feedback and building a content recommendation system that takes emotions into account, user satisfaction can be improved.

[1544] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1545] Step 1:

[1546] A user watches content using a smartphone and then enters feedback. The feedback is entered in text format and saved on the smartphone. The input data consists of the user's viewing history and feedback text.

[1547] Step 2:

[1548] The device sends the collected feedback text to the server, which then analyzes the received feedback text using a natural language processing library (e.g., spaCy, NLTK). The input data is the feedback text, and the output data is the judgment result, whether the feedback is positive (YES) or negative (NO).

[1549] Step 3:

[1550] The server uses a sentiment analysis engine (e.g., IBM Watson, Microsoft Azure Text Analytics) to identify the user's sentiment from the feedback text. The input data is the feedback text, and the output data is the user's sentiment (e.g., joy, anger, sadness).

[1551] Step 4:

[1552] The server inputs the judgment results and emotion data into a machine learning framework (e.g., TensorFlow, PyTorch) to update the content recommendation algorithm. The input data are the judgment results and emotion data, and the output data is the updated content recommendation model.

[1553] Step 5:

[1554] The server uses the updated content recommendation model to recommend content according to the user's emotions, where the input data is the user's emotion data and the output data is a list of recommended content.

[1555] Step 6:

[1556] The terminal displays the recommended content list received from the server to the user, and the user can select and view the recommended content. The input data is the recommended content list, and the output data is the user's viewing selection.

[1557] In this way, specific data processing and calculations are performed at each step, and user feedback is effectively utilized to achieve personalized content recommendations based on emotions.

[1558] 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.

[1559] 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> ) 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.

[1560] Another example of generative AI is Gemini (internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[1561] 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.

[1562] [Third embodiment]

[1563] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1564] 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.

[1565] 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).

[1566] 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.

[1567] 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.

[1568] 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).

[1569] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1570] 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.

[1571] 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.

[1572] 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.

[1573] 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.

[1574] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1575] "Example 1"

[1576] In one embodiment of the present invention, a web-based interface is provided as a means for receiving questions from users regarding smartphone operation. The interface is designed to allow users to input questions in text format. For example, if a user inputs the question, "How do I adjust the brightness of my smartphone screen?", the question is sent to the system.

[1577] "Example 2"

[1578] Next, a chat generation AI (Generative Pre-training Transformer) is used to generate answers to the received questions. Chat generation AI has the ability to learn from large amounts of text data and generate answers to questions in natural language. For example, in response to the above question, the chat generation AI would generate the answer, "You can adjust the screen brightness from the settings menu."

[1579] "Example 3"

[1580] The technology of natural language processing (NLP) is used as a means to determine whether the generated answer is YES or NO. Specifically, the generated answer is analyzed and its content is determined to be affirmative (YES) or negative (NO). For example, if the generated answer is "Yes, that is possible," this determination means determines that the answer is YES.

[1581] "Example 4"

[1582] Finally, a machine learning algorithm is used to update the chat generation AI based on the judgment result. Specifically, the judgment result is added to the learning data of the chat generation AI as feedback, improving the performance of the chat generation AI. For example, if the judgment result is YES, the question and answer pair is added to the learning data. This allows the chat generation AI to generate a more appropriate answer if the same question is asked again.

[1583] The processing flow of each embodiment will be described below.

[1584] "Example 1"

[1585] Step 1: A user inputs a smartphone operation question through a web-based interface, for example, "How do I adjust the brightness of my smartphone screen?"

[1586] Step 2: The system receives this question and sends it to the chat generation AI.

[1587] "Example 2"

[1588] Step 1: The chat generation AI analyzes the question and generates an appropriate answer, such as "You can adjust the screen brightness from the settings menu."

[1589] Step 2: The generated answer is sent to the system.

[1590] "Example 3"

[1591] Step 1: The system analyzes the generated answer and determines whether it is affirmative (YES) or negative (NO). For example, if the generated answer is "Yes, it is possible," the system determines that the answer is YES.

[1592] Step 2: The decision result is saved in the system.

[1593] "Example 4"

[1594] Step 1: The system adds the judgment results as feedback to the learning data of the chat generation AI.

[1595] Step 2: The chat generation AI is retrained using new training data, so that if the same question is asked again, the chat generation AI can generate a more appropriate answer.

[1596] Example 1

[1597] Next, a description will be given of Example 1 of Form 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."

[1598] In conventional question-answering systems for operating information processing devices, users may not receive the appropriate answer even when they input a question. Furthermore, there is a lack of means to determine whether the generated answer is appropriate and to update the system, which leads to a problem of low user satisfaction. Furthermore, the interface for users to input questions can be difficult to use, which is an issue that does not improve the user experience.

[1599] 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.

[1600] In this invention, the server includes means for receiving a question from a user regarding the operation of an information processing device, means for generating an answer to the received question, means for determining whether the generated answer is YES or NO, means for updating the dialogue generation AI based on the determination result, means for providing a web-based interface for a user to input a question in text format, means for inputting the received question as a prompt sentence to the generative AI model, means for transmitting the generated answer to the user's terminal, and means for displaying the answer on the user's terminal. This allows a user to easily input a question and obtain an appropriate answer. Furthermore, the appropriateness of the generated answer can be determined and the system updated to improve the user experience.

[1601] A "user" is an individual or group that operates an information processing device and inputs a question.

[1602] An "information processing device" is an electronic device operated by a user, such as a smartphone or a personal computer.

[1603] The "means for receiving a question" is a function or device for receiving a question from a user as input.

[1604] The "means for generating an answer" is a function or device for creating an appropriate answer to a received question.

[1605] The "means for determining whether the generated answer is YES or NO" is a function or device for determining whether the generated answer is appropriate.

[1606] "Dialogue generation artificial intelligence" is an artificial intelligence system for generating dialogue with users.

[1607] The "means for updating the dialogue generation artificial intelligence" is a function or device for updating the data or algorithm of the dialogue generation artificial intelligence based on the appropriateness of the generated answers.

[1608] A "web-based interface" is a web page or application that a user accesses and enters a query over the Internet.

[1609] A "prompt" is a form of question that is input to a generative AI model.

[1610] A "generative artificial intelligence model" is an artificial intelligence model that generates answers in natural language to questions from users.

[1611] A "user terminal" is an information processing device used by a user.

[1612] The "means for displaying an answer" is a function or device for displaying the generated answer on the user's terminal.

[1613] This invention is a system that receives questions from users about the operation of information processing devices, generates appropriate answers, and provides them to the users. This system operates in cooperation with a server, a terminal, and a user.

[1614] System configuration

[1615] Hardware and Software

[1616] Server: A server with high-performance computing power is required to receive questions, run generative AI models, generate answers, and send them.

[1617] Terminal: An information processing device used by a user, such as a smartphone or PC. The terminal communicates with the server via a web browser.

[1618] Generative AI models: For example, using advanced natural language processing models such as OpenAI's GPT-4.

[1619] Data processing and calculation

[1620] Receiving questions

[1621] The user accesses the specified URL using the device's web browser. A web-based interface appears, and the user enters a text question, for example, "How do I adjust the brightness of my smartphone screen?"

[1622] Submit a Question

[1623] When a user enters a question and clicks the submit button, the device sends the question to the server as an HTTP request.

[1624] Parsing questions and generating prompts

[1625] The server analyzes the received question and converts it into a prompt suitable for the generative AI model, for example, "User asks: 'How do I adjust the brightness of my smartphone screen?'"

[1626] Generate answers

[1627] The server inputs a prompt into the generative AI model and generates an appropriate answer. The generative AI model generates an answer to the question based on the input prompt. For example, it generates an answer like, "To adjust the brightness of your smartphone screen, open the Settings app and select the 'Display' or 'Screen' option. From there, adjust the 'Brightness' slider to the desired brightness."

[1628] Submitting and viewing answers

[1629] The server sends the generated answer to the user's device as an HTTP response. The device displays the received answer on a web-based interface. The user can check the displayed answer and understand how to operate the information processing device.

[1630] Specific examples

[1631] Consider a case where a user types the question "How do I adjust the brightness of my smartphone screen?" This question is input to a generative AI model as a prompt like this:

[1632] Example prompt sentence:

[1633] A user asks: "How do I adjust the brightness of my smartphone screen?"

[1634] The generative AI model analyzes this prompt and generates the following answer:

[1635] To adjust the brightness of your phone's screen, open the Settings app and select the "Display" or "Screen" option. From there, adjust the "Brightness" slider to your desired brightness.

[1636] This response is sent to the user's device via the server and displayed to the user, who can then adjust the brightness of their smartphone screen.

[1637] In this way, users can easily enter their questions and get relevant answers, and the appropriateness of the generated answers can be assessed and the system updated to improve the user experience.

[1638] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1639] Step 1:

[1640] A user accesses a web-based interface.

[1641] Specific operation: The user opens a web browser on their smartphone or computer and accesses the specified URL.

[1642] Input: Access request from the user's device.

[1643] Output: A web-based interface is displayed on the user's terminal.

[1644] Step 2:

[1645] The user enters a question in text format.

[1646] What it does: A user types a question into a text box displayed on a web-based interface, for example, "How do I adjust the brightness of my smartphone screen?"

[1647] Input: The question text entered by the user.

[1648] Output: The question text is displayed on the interface.

[1649] Step 3:

[1650] The terminal sends the entered question to the server.

[1651] Specific operation: When the user clicks the submit button, the terminal sends the entered question to the server as an HTTP request.

[1652] Input: The question text entered by the user.

[1653] Output: An HTTP request containing the question text is sent to the server.

[1654] Step 4:

[1655] The server receives the question and inputs it as a prompt to the generative AI model.

[1656] How it works: The server analyzes the received question and converts it into a prompt suitable for the generative AI model, for example, "User asks: 'How do I adjust the brightness of my smartphone screen?'"

[1657] Input: The question text received as an HTTP request.

[1658] Output: A prompt to be fed into the generative AI model.

[1659] Step 5:

[1660] A generative AI model generates answers to questions.

[1661] Specific operation: The server inputs a prompt sentence into a generative AI model (e.g., OpenAI's GPT-4) and generates an appropriate answer.

[1662] Input: The prompt statement.

[1663] Output: The answer text generated by the generative AI model.

[1664] Step 6:

[1665] The server receives the generated response and sends it to the user's terminal.

[1666] Specific operation: The server sends the generated answer to the user's terminal as an HTTP response.

[1667] Input: Answer text generated by the generative AI model.

[1668] Output: HTTP response containing the answer text.

[1669] Step 7:

[1670] The terminal displays the answer to the user.

[1671] Specific operation: The user's device displays the answer received from the server on a web-based interface. The user can check the displayed answer and understand how to operate the information processing device.

[1672] Input: The answer text received from the server.

[1673] Output: Answer text displayed on the web-based interface.

[1674] (Application example 1)

[1675] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1676] While existing smartphone question-answering systems can provide appropriate answers to questions entered by users, they are unable to adequately respond to questions about electronic payments. Furthermore, the dialogue generation AI is not automatically updated to improve the quality of the generated answers, which can lead to a decline in the accuracy of the system.

[1677] 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.

[1678] In this invention, the server includes means for receiving questions from users regarding smartphone operation, means for generating answers to the received questions, means for determining whether the generated answers are YES or NO, means for updating the dialogue generation AI based on the determination result, means for receiving questions from users regarding electronic payments, means for generating answers to the received questions regarding electronic payments using a generative AI model, and means for displaying the generated answers to the users. This enables users to obtain appropriate answers to questions regarding electronic payments, and further enables the accuracy of the system to be improved by automatically updating the dialogue generation AI.

[1679] "User" means an individual or entity that uses the System to enter questions and receive answers.

[1680] "Smartphone operation" refers to operations and procedures related to smartphone functions and settings.

[1681] "Means for receiving questions" refers to the interface or function that allows the system to receive questions entered by the user.

[1682] "Means for generating answers" refers to algorithms or programs for generating appropriate answers to received questions.

[1683] "Means for determining whether the generated answer is YES or NO" refers to a function for determining whether the generated answer is appropriate.

[1684] "Dialogue generation artificial intelligence" refers to an artificial intelligence system for generating dialogue with users.

[1685] "Means for updating the dialogue generation AI" refers to a function for updating the learning data of the dialogue generation AI in order to improve the quality of the generated answers.

[1686] "Electronic payment" refers to payment transactions conducted over the Internet.

[1687] A "generative AI model" refers to a model that uses artificial intelligence to generate answers to questions.

[1688] "Means for displaying answers to the user" refers to the interface or functionality for visually presenting the generated answers to the user.

[1689] The system for carrying out this invention receives questions from a user regarding smartphone operation and electronic payment, generates appropriate answers, and displays them to the user. A specific embodiment of this system will be described below.

[1690] System Configuration

[1691] The system consists of the following main components:

[1692] 1. User device: The device through which the user enters their question, such as a smartphone or computer.

[1693] 2. Server: A central processing unit that receives questions, generates answers using generative AI models, and displays them to the user.

[1694] 3. Generative AI model: An artificial intelligence model for generating answers to questions, such as OpenAI's GPT-3.

[1695] Program processing

[1696] The server performs the process in the following steps.

[1697] 1. Receiving a question: The server receives a question sent from the user's terminal. The question is entered in text format.

[1698] 2. Answer generation: The received question is input into the generative AI model, which generates an appropriate answer. The generative AI model generates the best answer based on the content of the question.

[1699] 3. Displaying the answer: The generated answer is sent to the user's device and displayed to the user.

[1700] 4. Evaluate the answer: The server determines whether the generated answer is appropriate, for example, by rating it based on user feedback.

[1701] 5. AI Update: Based on the evaluation results, the learning data of the dialogue generation AI is updated, thereby improving the accuracy of the system.

[1702] Hardware and software used

[1703] Hardware: Cloud servers (e.g., Amazon Web Services or Google Cloud Platform)

[1704] Software: Flask (Python web framework), OpenAI API

[1705] Specific examples

[1706] When a user enters the question "How do I register a credit card?", the server processes the following:

[1707] 1. Receiving a question: The user's device sends the question "How do I register a credit card?" to the server.

[1708] 2. Generate an answer: The server inputs this question into a generative AI model and generates an answer like this:

[1709] To register a credit card, select "Payment Methods" from the app's settings menu and click "Add Credit Card." Then enter your card information and press the confirmation button.

[1710] 3. Displaying the answer: The generated answer is sent to the user's device and displayed to the user.

[1711] Prompt Sentence Examples

[1712] Electronic Payment Questions: How do I register my credit card?

[1713] answer:

[1714] In this way, users can get appropriate answers to questions about electronic payments, and the accuracy of the system can be improved by automatically updating the dialogue generation artificial intelligence.

[1715] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1716] Step 1:

[1717] The user uses the terminal to input a question. The input question is in text format, such as "How do I register a credit card?" The input question is sent from the terminal to the server.

[1718] Step 2:

[1719] The server receives the question sent from the terminal. The received question is saved as text data on the server. The input here is the question text from the user, and the output is the question data saved on the server.

[1720] Step 3:

[1721] The server inputs the received question into a generative AI model. For example, OpenAI's GPT-3 is used as the generative AI model. Based on the input question, the generative AI model generates an appropriate answer. Here, the input is the question text, and the output is the generated answer text.

[1722] Step 4:

[1723] The server receives the answer obtained from the generative AI model and sends it to the user's device. The user's device displays the received answer. The input here is the generated answer text, and the output is the answer displayed on the user's device.

[1724] Step 5:

[1725] The user reviews the displayed answers and provides feedback, such as a "YES" or "NO" rating, based on whether the answer is correct. The feedback is sent from the device to the server.

[1726] Step 6:

[1727] The server receives feedback from users and updates the learning data of the dialogue generation AI. The input here is user feedback, and the output is updated learning data. This improves the accuracy of the system.

[1728] In this way, users can receive appropriate answers to questions about electronic payments, and the accuracy of the system can be improved by automatically updating the dialogue generation artificial intelligence.

[1729] Example 2

[1730] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1731] Conventional information processing devices have had difficulty quickly providing appropriate answers to user questions. They also lacked effective means for improving the quality of generated answers. Furthermore, they lacked a mechanism for determining whether a generated answer was appropriate and updating the generative AI model based on the results.

[1732] 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.

[1733] In this invention, the server includes means for receiving a question from a user regarding the operation of an information processing device, means for preprocessing the received question, means for inputting the preprocessed question into a generative AI model, means for the generative AI model to generate an answer to the question, and means for transmitting the generated answer to the user. This makes it possible to provide a prompt and appropriate answer to a question from a user. It also provides an effective means for improving the quality of the generated answer, and the accuracy of the answer can be continuously improved by updating the generative AI model.

[1734] A "user" is an individual or group that operates an information processing device and inputs a question.

[1735] An "information processing device" is a device operated by a user, which inputs questions and receives answers.

[1736] The "means for receiving a question" is a function for receiving a question input by a user.

[1737] "Preprocessing means" is a function that processes received questions, such as normalizing them and deleting unnecessary information.

[1738] A "generative AI model" is an artificial intelligence model that learns from large amounts of text data and generates answers to questions in natural language.

[1739] "Means for inputting questions into the generative AI model" refers to a function for passing preprocessed questions to the generative AI model.

[1740] The "means for generating an answer" is the function that enables the generative AI model to generate an appropriate answer based on the question.

[1741] The "means for transmitting an answer to a user" is a function for transmitting the generated answer to the user's information processing device.

[1742] MODE FOR CARRYING OUT THE INVENTION

[1743] This invention relates to a system that provides prompt and appropriate answers to questions from users about the operation of information processing devices. In this system, a user inputs a question through an information processing device, and an answer to the question is generated using a generative AI model and provided to the user.

[1744] Hardware and software used

[1745] Hardware:

[1746] Information processing device: A device operated by a user (e.g., smartphone, tablet, PC, etc.)

[1747] Server: A server that receives questions, preprocesses them, feeds them into the generative AI model, and generates and sends answers.

[1748] software:

[1749] Generative AI models: Large-scale text generation models such as Generative Pre-training Transformer (GPT)

[1750] Preprocessing software: Software for normalizing text and removing unnecessary information

[1751] Data processing and calculation

[1752] The server processes the query received from the user in the following steps:

[1753] 1. Receiving Questions:

[1754] The server receives a natural language question submitted by the user, which is sent to the server as text data.

[1755] 2. Question preprocessing:

[1756] The server preprocesses the received query, which includes normalizing the text (e.g., converting uppercase to lowercase, removing unnecessary whitespace, etc.).

[1757] 3. Input to the generative AI model:

[1758] The server inputs the preprocessed questions into a generative AI model that has been trained from large amounts of text data and is capable of generating answers to natural language questions.

[1759] 4. Generate answers:

[1760] The generative AI model generates appropriate answers based on the questions entered. For example, if a user asks, "How do I adjust the screen brightness?", the model generates the answer, "You can adjust the screen brightness from the settings menu."

[1761] 5. Submit your response:

[1762] The server transmits the generated answer to the user, who can receive the answer through an information processing device.

[1763] Specific examples

[1764] Example prompt sentence:

[1765] How do I adjust the screen brightness?

[1766] Process flow:

[1767] 1. User action: The user opens a chat app on their smartphone and types, "How do I adjust the screen brightness?"

[1768] 2. Device behavior: The smartphone sends this question to the server as an HTTP request.

[1769] 3. Server action: The server receives the question and performs text normalization, for example converting "How do I adjust the brightness of my screen?" to "How do I adjust the brightness of my screen?"

[1770] 4. Server operation: The server inputs the preprocessed questions into the generative AI model.

[1771] 5. How the generative AI model works: The model generates the answer, "You can adjust the screen brightness from the settings menu."

[1772] 6. Server Action: The server receives the generated answer.

[1773] 7. Server operation: The server sends the answer to the user's smartphone as an HTTP response.

[1774] 8. User Action: The user checks the answer displayed on the smartphone screen.

[1775] In this way, users can enter questions in natural language and get appropriate answers using generative AI models.

[1776] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1777] Step 1:

[1778] The user enters a question.

[1779] The user inputs a question into an input field of the information processing device. For example, the user inputs "Please tell me how to adjust the screen brightness." The input question is saved as text data in the terminal.

[1780] Step 2:

[1781] The terminal sends a question to the server.

[1782] The terminal sends the question entered by the user to the server as an HTTP request. A communication process is performed to send the input data (question text) to the server. The input is the user's question text, and the output is an HTTP request to the server.

[1783] Step 3:

[1784] The server receives and pre-processes the query.

[1785] The server receives questions sent from the terminal. The received questions are stored as text data on the server. The server then normalizes the text, for example, converting uppercase to lowercase and removing unnecessary spaces. The input is the received question text, and the output is the preprocessed question text.

[1786] Step 4:

[1787] The server inputs the preprocessed questions into the generative AI model.

[1788] The server inputs the preprocessed questions into a generative AI model. The generative AI model learns from a large amount of text data and has the ability to generate answers to natural language questions. The input is the preprocessed question text, and the output is the input data for the generative AI model.

[1789] Step 5:

[1790] A generative AI model generates the answer.

[1791] A generative AI model generates an appropriate answer based on the input question. For example, if the question is "How do I adjust the screen brightness?", the model generates the answer "You can adjust the screen brightness from the settings menu." The input is the input data to the generative AI model, and the output is the generated answer text.

[1792] Step 6:

[1793] The server receives the generated answer.

[1794] The server receives the answer generated by the generative AI model. The received answer is stored on the server as text data. The input is the generated answer text, and the output is the received data to the server.

[1795] Step 7:

[1796] The server sends the response to the user's terminal.

[1797] The server sends the generated answer to the user's terminal as an HTTP response. The input is the generated answer text, and the output is the HTTP response to the user's terminal.

[1798] Step 8:

[1799] The user receives the answer.

[1800] The user receives the answer sent from the server through the terminal. The answer is displayed on the terminal screen and the user can check it. The input is the HTTP response from the server, and the output is the answer text displayed on the terminal.

[1801] (Application example 2)

[1802] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1803] In brick-and-mortar stores, customers need to be able to quickly and accurately resolve their questions about products and services. However, traditional methods require store staff to handle the inquiries, which can take a long time and lead to a decline in customer satisfaction. This also increases the burden on staff, making efficient operations difficult.

[1804] 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.

[1805] In this invention, the server includes means for receiving questions from users regarding smartphone operation, means for generating answers to the received questions, means for determining whether the generated answers are YES or NO, means for updating the chat generation AI based on the determination result, means for receiving questions from customers in a physical store and generating answers in real time, and means for providing the generated answers to customers. This allows customers to receive answers to their questions quickly and accurately, reducing the burden on store staff and enabling efficient store operations.

[1806] "User" refers to any individual or entity that uses the System.

[1807] "Smartphone operation" refers to a series of operations and inputs performed using a smartphone.

[1808] "Means for receiving questions" refers to the interface or function that allows the system to receive questions from users.

[1809] "Means for generating answers" refers to algorithms or functions for generating appropriate answers to received questions.

[1810] "Means for determining whether it is YES or NO" refers to a function for determining whether the generated answer is positive or negative.

[1811] "Chat generation AI" refers to artificial intelligence that learns from large amounts of text data and generates answers to questions in natural language.

[1812] "Means for updating the chat generation AI" refers to the function for updating the learning data and algorithms of the chat generation AI based on the judgment results.

[1813] "Brick and mortar store" refers to a commercial establishment located in a physical location.

[1814] "Customers" refers to consumers and users who visit physical stores.

[1815] "Means for generating answers in real time" refers to a function that allows you to instantly generate an answer the moment a question is received.

[1816] "Means for providing answers to customers" refers to interfaces and functions for communicating generated answers to customers.

[1817] A system for implementing this invention includes means for receiving questions from users regarding smartphone operation, means for generating answers to the received questions, means for determining whether the generated answers are YES or NO, means for updating the chat generation AI based on the determination result, means for receiving questions from customers in a physical store and generating answers in real time, and means for providing the generated answers to customers.

[1818] Program processing explanation

[1819] The server provides an interface for receiving questions from users. This interface is implemented as a smartphone application. When a user inputs a question, the question is sent to the server.

[1820] The server sends the received question to a generative AI model, which generates an appropriate answer. This generative AI model is implemented using OpenAI's API. The generated answer is analyzed by the server and determined to be either YES or NO.

[1821] Based on the results of the judgment, the server updates the learning data of the chat generation AI. This update process is carried out to improve the accuracy of the AI ​​model.

[1822] In a physical store, when a customer uses a smartphone app to enter a question, the question is sent to the server in real time, and the server instantly generates an answer and provides it to the customer. This process allows customers to get answers to their questions quickly and accurately.

[1823] Specific examples

[1824] For example, a customer opens a smartphone app in a physical store and enters the following question:

[1825] "Do you have this item in stock?"

[1826] The application sends the following prompt to the generative AI model:

[1827] Customer Question: Is this item in stock?

[1828] answer:

[1829] The generative AI model returns the following answer:

[1830] Yes, this item is in stock and can be found in store in Aisle 3.

[1831] In this way, customers can get fast and accurate information.

[1832] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1833] Step 1:

[1834] The user opens the smartphone app and enters a question.

[1835] Input: A question typed by the user (e.g., "Do you have this item in stock?")

[1836] Output: Question text

[1837] What happens: The user types a question into the app's text input field and presses the submit button.

[1838] Step 2:

[1839] The terminal sends the question text to the server.

[1840] Input: Question text

[1841] Output: Question data sent to the server

[1842] Specific operation: The smartphone app sends the question text to the server as an HTTP request.

[1843] Step 3:

[1844] The server receives the question and sends a prompt to the generative AI model.

[1845] Input: Question data sent to the server

[1846] Output: The prompt sent to the generative AI model

[1847] Specific operation: The server analyzes the question data, generates a prompt (e.g., "Customer question: Is this item in stock?\nAnswer:"), and sends it to the generative AI model.

[1848] Step 4:

[1849] A generative AI model generates an answer based on the prompt.

[1850] Input: prompt statement

[1851] Output: The generated answer (e.g., "Yes, we have this item in stock. It's in Aisle 3 of the store.")

[1852] What it does: The generative AI model analyzes the prompt and generates an appropriate response.

[1853] Step 5:

[1854] The server receives the generated response and determines whether it is YES or NO.

[1855] Input: Generated answer

[1856] Output: YES or NO result

[1857] What happens: The server analyzes the generated answer and determines whether the answer is positive or negative.

[1858] Step 6:

[1859] The server updates the learning data of the chat generation AI based on the judgment results.

[1860] Input: YES or NO result

[1861] Output: Updated training data

[1862] Specific operation: Based on the judgment results, the server updates the learning data of the chat generation AI.

[1863] Step 7:

[1864] The server sends the generated response to the terminal.

[1865] Input: Generated answer

[1866] Output: Answer data sent to the device

[1867] Specific operation: The server sends the generated answer to the terminal as an HTTP response.

[1868] Step 8:

[1869] The terminal displays the answer to the user.

[1870] Input: Answer data sent to the terminal

[1871] Output: The answer displayed to the user

[1872] Specific operation: The smartphone app receives the response data and displays it on the user's screen.

[1873] Example 3

[1874] Next, a third embodiment of the third embodiment will be described. 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."

[1875] In conventional systems, generating answers to user questions and determining whether the answers are appropriate is often done manually, which is problematic in efficiency. Furthermore, there is no feedback loop to improve the quality of the generated answers, making it difficult to improve the system's performance.

[1876] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1877] In this invention, the server includes means for receiving questions from users regarding their operations, means for inputting the received questions as prompt sentences to the generative AI model, means for the generative AI model to generate answers, means for determining whether the generated answers are YES or NO, and means for updating the chat generation AI based on the determination result. This makes it possible to automatically generate prompt and appropriate answers to questions from users and continuously improve the quality of those answers.

[1878] A "user" is an entity that operates the system and inputs questions.

[1879] An "operational question" is a query about a specific operation or piece of information that a user inputs into the system.

[1880] "Means for receiving questions" refers to the functions and methods by which the system receives questions from users.

[1881] A "generative AI model" is an artificial intelligence algorithm or system that generates answers based on input prompts.

[1882] A "prompt" is a question or instruction text that is input into a generative AI model.

[1883] "Means for generating an answer" refers to the functions and methods by which a generative AI model creates an answer based on a prompt.

[1884] "Means for determining whether the answer is YES or NO" refers to a function or method for analyzing and determining whether the generated answer is affirmative (YES) or negative (NO).

[1885] "Chat generation AI" is an artificial intelligence system that generates appropriate answers to users' questions.

[1886] "Means for updating the chat generation AI" refers to functions and methods for adding or modifying the learning data of the chat generation AI based on the judgment results and improving the performance of the system.

[1887] This invention is a system that receives a question about an operation from a user, generates an answer to that question, determines whether the generated answer is YES or NO, and updates the chat generation AI based on the result of that determination. Specific embodiments of this system are described below.

[1888] Hardware and software used

[1889] Hardware: Servers, terminals

[1890] Software: Natural Language Processing (NLP) libraries (e.g., spaCy, NLTK), machine learning algorithms (e.g., TensorFlow, PyTorch)

[1891] System configuration

[1892] 1. The user enters a question

[1893] The user types a question into an input field on the device, for example, "Is this item in stock?"

[1894] 2. The device sends a question to the server

[1895] The device sends the question entered by the user to the server. Specifically, it sends the question data to the server using an HTTP request.

[1896] 3. The server inputs the question as a prompt to the generative AI model.

[1897] The server inputs the received question as a prompt to the generative AI model. For example, the question "Is this product in stock?" is passed to the generative AI model.

[1898] 4. Generative AI models generate answers

[1899] The generative AI model generates an answer based on the prompt, for example, "Yes, we have it in stock."

[1900] 5. The server parses the generated answer

[1901] The server uses NLP technology to analyze the generated answer. Specifically, it determines whether the answer is positive (YES) or negative (NO). For example, it analyzes the answer "Yes, we have it in stock" and determines that it is YES.

[1902] 6. The server gives feedback on the decision result

[1903] The server feeds back the results of its judgment to the chat generation AI. Specifically, the judgment results are added to the training data and used to improve the performance of the chat generation AI. For example, the question "Is this product in stock?" and the answer "Yes, it is in stock" are added to the training data.

[1904] 7. The server updates the chat generation AI

[1905] The server updates the chat generation AI based on the feedback, so that if the same question is asked again, it can generate a more appropriate answer.

[1906] Specific examples

[1907] User asks: "Is this item in stock?"

[1908] Prompt for generative AI model: "Is this item in stock?"

[1909] Generated Answer: "Yes, we have it in stock"

[1910] Server decision: Determines that the answer is YES

[1911] Feedback: Add the question "Is this item in stock?" and the answer "Yes, it's in stock" to the training data

[1912] In this way, the system can generate appropriate answers to user questions and continuously learn to improve its performance. The flow of the identification process in the third embodiment will be described with reference to FIG.

[1913] Step 1:

[1914] The user enters a question.

[1915] The user enters a question into an input field on the device, for example, "Is this item in stock?" The entered question is temporarily stored in the device's memory.

[1916] Step 2:

[1917] The terminal sends a question to the server.

[1918] The terminal sends the question entered by the user to the server as an HTTP request. The input data is sent to the server in text format. The server receives this request and analyzes the question data.

[1919] Step 3:

[1920] The server inputs the question as a prompt into the generative AI model.

[1921] The server inputs the received question as a prompt to the generative AI model. For example, the question "Is this product in stock?" is passed to the generative AI model. The input data is in text format and is converted into a format suitable for the generative AI model.

[1922] Step 4:

[1923] A generative AI model generates the answer.

[1924] The generative AI model generates an answer based on the prompt sentence, for example, "Yes, we have it in stock." The generated answer is returned to the server in text format.

[1925] Step 5:

[1926] The server parses the generated answer.

[1927] The server uses NLP technology to analyze the generated answer. Specifically, it determines whether the answer is affirmative (YES) or negative (NO). For example, it analyzes the answer "Yes, we have it in stock" and determines that it is YES. The input data is the generated answer, and the output data is the YES or NO judgment result.

[1928] Step 6:

[1929] The server will provide feedback on the results of the decision.

[1930] The server feeds back the judgment results to the chat generation AI. Specifically, the judgment results are added to the training data and used to improve the performance of the chat generation AI. For example, the question "Is this product in stock?" and the answer "Yes, it is in stock" are added to the training data. The input data are question and answer pairs, and the output data is the updated training data.

[1931] Step 7:

[1932] The server updates the chat generation AI.

[1933] The server updates the chat generation AI based on the feedback, so that it can generate more appropriate answers when the same question is asked again. The input data is updated learning data, and the output data is an improved chat generation AI.

[1934] (Application example 3)

[1935] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1936] Conventional dialogue generation AIs in information processing devices have low accuracy in responding to user questions, making it difficult to improve user satisfaction. Furthermore, they lack a means to determine whether a generated response is positive or negative, preventing effective updating of the learning data of dialogue generation AIs. Therefore, there is a need to provide appropriate responses to user questions and improve the performance of dialogue generation AIs.

[1937] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1938] In this invention, the server includes means for receiving questions from a user regarding operation of the information processing device, means for generating answers to the received questions, means for determining whether the generated answers are positive or negative, means for updating the dialogue generation artificial intelligence based on the determination result, and means for saving the determination result as feedback, thereby making it possible to provide appropriate answers to user questions and improve the performance of the dialogue generation artificial intelligence.

[1939] An "information processing device" is a device that inputs, processes, and outputs data, and includes smartphones, computers, etc.

[1940] "Question receiving means" refers to a function or device for receiving questions from users, and includes methods such as text input and voice input.

[1941] "Answer generation means" refers to a function or device for generating an appropriate answer to a received question.

[1942] The "judgment means" is a function or device for judging whether the generated answer is positive or negative.

[1943] "Dialogue generation artificial intelligence" is an artificial intelligence system for generating dialogue with users.

[1944] The "update means" refers to a function or device for updating the learning data of the dialogue generation artificial intelligence based on the judgment results.

[1945] The "feedback storage means" is a function or device for storing the judgment results as feedback.

[1946] "Character input" refers to a method of inputting characters using a keyboard or touch screen.

[1947] "Training data" is a data set used by artificial intelligence to learn.

[1948] A system for implementing this invention includes means for receiving questions from a user regarding the operation of an information processing device, means for generating answers to the received questions, means for determining whether the generated answers are positive or negative, means for updating the dialogue generation artificial intelligence based on the determination results, and means for saving the determination results as feedback.

[1949] Program processing explanation

[1950] Hardware and software used

[1951] Hardware: Smartphone

[1952] Software: Python, OpenAI API, TextBlob

[1953] Data processing and calculation

[1954] 1. Question input: The user inputs a question into the smartphone app. For example, "Can I return this item?"

[1955] 2. Answer generation using generative AI model: The server uses OpenAI's API to generate an answer to the question, for example, "Yes, you can return it within 30 days."

[1956] 3. Natural Language Processing: The server uses TextBlob to perform sentiment analysis on the generated answer and determine whether it is positive (YES) or negative (NO). For example, if the generated answer is "Yes, you can return it within 30 days," it is determined to be positive (YES).

[1957] 4. Save feedback: The server saves the question and answer pairs in a text file and uses them as training data for future use. For example, it saves data such as "Question: Can I return this product?", "Answer: Yes, you can return it within 30 days," and "Result: YES."

[1958] Specific examples

[1959] Question: "Can I return this item?"

[1960] Generated Answer: "Yes, you can return it within 30 days."

[1961] Judgment result: YES

[1962] Prompt Sentence Examples

[1963] User: Can I return this item?

[1964] AI: Yes, you can return it within 30 days.

[1965] In this way, the system can provide appropriate answers to user questions and improve the performance of dialogue generation artificial intelligence.

[1966] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1967] Step 1:

[1968] The user inputs a question using a smartphone device.

[1969] Input: A user types into an app on their smartphone, "Can I return this item?"

[1970] Output: The entered question is sent to the server as text data.

[1971] Specific operation: The user enters a question using the smartphone keyboard and presses the send button, which sends the question to the server.

[1972] Step 2:

[1973] The server uses a generative AI model to generate an answer to the question it receives.

[1974] Input: The server receives the question "Can I return this item?" submitted by the user.

[1975] Output: The answer "Yes, returns are accepted within 30 days" is generated using a generative AI model (OpenAI API).

[1976] Specific behavior: The server sends a question to the OpenAI API and receives the answer returned by the API.

[1977] Step 3:

[1978] The server analyzes the generated response using natural language processing technology to determine whether it is positive or negative.

[1979] Input: The server receives the generated answer "Yes, you can return it within 30 days."

[1980] Output: The answer is determined to be affirmative (YES) using a TextBlob.

[1981] What happens: The server uses the TextBlob library to perform sentiment analysis on the answer and determines it to be positive by getting a positive sentiment score.

[1982] Step 4:

[1983] The server updates the learning data of the dialogue generation artificial intelligence based on the judgment results.

[1984] Input: Server receives question "Can I return this item?", answer "Yes, you can return it within 30 days", and verdict "YES".

[1985] Output: Question and answer pairs are saved as training data.

[1986] Specific operation: The server saves the question and answer pairs in a text file or database and uses them as learning data for future use.

[1987] Step 5:

[1988] The server stores the judgment result as feedback.

[1989] Input: The server receives the verdict "YES".

[1990] Output: The judgment result is saved as feedback data.

[1991] Specific operation: The server stores the feedback data in text files or a database to help improve the performance of the dialogue generation AI.

[1992] 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.

[1993] "Example 1"

[1994] As one embodiment of the present invention, a system is provided that includes means for receiving questions from users regarding smartphone operation, means for generating answers to the received questions, means for determining whether the generated answers are YES or NO, means for updating a chat generation AI based on the determination result, and an emotion engine for recognizing the user's emotions.

[1995] "Example 2"

[1996] The emotion engine analyzes emotions from user text input. For example, if a user types the question "How do I adjust the brightness of my smartphone screen?" with anger, the emotion engine will recognize that anger.

[1997] "Example 3"

[1998] The chat generation AI's update method updates the learning data of the chat generation AI based on the analysis results of the emotion engine. For example, if a user asks a question with anger, the learning data will be updated so that an answer that reflects that anger is generated.

[1999] "Example 4"

[2000] In this way, according to the present invention, more appropriate answers are generated that take the user's emotions into consideration, thereby improving user satisfaction. Furthermore, by reflecting the analysis results of the emotion engine in the training data, the system's ability to generate answers is improved.

[2001] The processing flow of each embodiment will be described below.

[2002] "Example 1"

[2003] Step 1: Receive questions from users regarding smartphone operation.

[2004] Step 2: Generate answers to the questions received.

[2005] Step 3: Determine whether the generated answer is YES or NO.

[2006] Step 4: Update the chat generation AI based on the results of the judgment.

[2007] Step 5: Use an emotion engine to recognize user emotions.

[2008] "Example 2"

[2009] Step 1: Analyze sentiment from user text input.

[2010] Step 2: Use the analysis results to recognize the user's emotions.

[2011] "Example 3"

[2012] Step 1: Update the learning data of the chat generation AI based on the analysis results of the emotion engine.

[2013] Step 2: Generate an answer to the user's question using the updated learning data.

[2014] Step 1: Generate answers that take user sentiment into account.

[2015] Step 2: Provide the generated answer to the user.

[2016] Step 3: Evaluate user satisfaction.

[2017] Example 1

[2018] Next, a description will be given of Example 1 of Form 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."

[2019] Conventional question-answering systems for operating information processing devices have difficulty generating appropriate answers to user questions and lack a means to determine the accuracy of the generated answers, making it difficult to improve user satisfaction. Furthermore, the lack of a function to recognize the user's emotions and adjust the answers based on them has led to a problem of a poor user experience.

[2020] 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.

[2021] In this invention, the server includes means for receiving questions from a user regarding the operation of the information processing device, means for generating answers to the received questions, means for determining whether the generated answers are YES or NO, means for updating the dialogue generation AI based on the determination result, and means for recognizing the user's emotions, thereby enabling the server to provide appropriate and accurate answers to questions from the user and to respond in consideration of the user's emotions.

[2022] An "information processing device" is a device that inputs, processes, and outputs data, and includes computers, smartphones, etc.

[2023] "Means for receiving questions" refers to the interface or functionality for receiving text questions from users.

[2024] "Means for generating answers" refers to algorithms or artificial intelligence models that generate appropriate answers to received questions.

[2025] "Yes / No method" refers to the rules or algorithms used to evaluate the accuracy of the generated answer.

[2026] "Dialogue generation artificial intelligence" refers to an artificial intelligence system for generating dialogue with users.

[2027] "Means for updating the dialogue generation AI" refers to a function for updating the learning data and model of the dialogue generation AI based on the evaluation results of the generated answers.

[2028] "Emotion recognition means" refers to algorithms or engines that analyze and recognize user emotions from user input text or other data.

[2029] MODE FOR CARRYING OUT THE INVENTION

[2030] This invention is a system that receives questions from users about the operation of an information processing device, generates appropriate answers, evaluates the accuracy of the answers, and updates a dialogue generation artificial intelligence. Specific embodiments of this system are described below.

[2031] Enter and submit your question

[2032] A user inputs a question about the operation of an information processing device through a web-based interface. This interface is designed to allow the user to input a question in text format. For example, the user may input "How do I adjust the brightness of my smartphone screen?" This question is sent from the device to a server. The transmission uses the HTTPS protocol.

[2033] Question Analysis

[2034] The server analyzes the received question using natural language processing (NLP) techniques, such as tokenization, grammar analysis, and intent extraction. For example, a Python NLP library can be used for this analysis.

[2035] Generate answers

[2036] The server generates an answer using a generative AI model (for example, OpenAI's GPT-4) based on the analysis results. The generative AI model generates an appropriate answer based on the content of the question. For example, it generates the answer "Open the Settings app, select Display Settings, and adjust the brightness."

[2037] Evaluating answers

[2038] The server determines whether the generated answer is YES or NO using pre-defined rules and algorithms. For example, if the generated answer is determined to be accurate, it will be evaluated as YES.

[2039] Updates to chat generation AI

[2040] The server adds answers that are evaluated as YES to the database of the dialogue generation AI. If the answer is evaluated as NO, the server provides feedback to the generation AI model and corrects the answer. This update can be performed using a database management system, for example.

[2041] emotion recognition

[2042] The server uses an emotion engine to analyze the user's emotions. For example, if it determines that the user is in a difficult situation, it will reflect this information in generating future answers. For example, an emotion analysis library can be used for this emotion recognition.

[2043] Submitting and viewing answers

[2044] The server sends the generated answer to the device, which then displays the received answer to the user. For example, the answer may say, "Open the Settings app, select Display Settings, and adjust brightness."

[2045] Specific examples

[2046] A user types "How do I adjust the brightness of my smartphone screen?" into a web-based interface. The device sends this question to the server, which uses a generative AI model to generate the answer: "Open the Settings app, select Display Settings, and adjust brightness." The server evaluates this answer, determines it to be YES, and updates the dialog generator. Finally, the server sends this answer to the device, which displays it to the user.

[2047] Prompt Sentence Examples

[2048] "How do I adjust the brightness of my smartphone screen?"

[2049] The flow of the identification process in the first embodiment will be described with reference to FIG.

[2050] Step 1:

[2051] A user accesses a web-based interface and enters a textual question about the operation of the information processing device, such as "How do I adjust the brightness of my smartphone screen?" The entered question is temporarily stored in the device's memory.

[2052] Step 2:

[2053] The terminal sends the question entered by the user to the server using the HTTPS protocol. The data sent is a text question. The server receives this data and proceeds to the next processing step.

[2054] Step 3:

[2055] The server analyzes the received question using natural language processing (NLP) technology. Specifically, it performs tokenization, grammar analysis, and intent extraction. The input is a text question, and the output is structured data of the analyzed question. For example, keywords such as "smartphone," "screen brightness," and "how to adjust" are extracted.

[2056] Step 4:

[2057] The server generates an answer using a generative AI model (e.g., OpenAI's GPT-4) based on the analysis results. The input is the structured data of the analyzed question, and the output is a text answer. For example, the generated answer might be "Open the Settings app, select Display Settings, and adjust brightness."

[2058] Step 5:

[2059] The server determines whether the generated answer is YES or NO. This determination is made using pre-defined rules and algorithms. The input is the generated answer, and the output is the evaluation result of YES or NO. For example, if the generated answer is judged to be accurate, it will be evaluated as YES.

[2060] Step 6:

[2061] The server adds answers evaluated as YES to the database of the dialogue generation AI. If the answer is evaluated as NO, it provides feedback to the generative AI model and corrects the answer. The input is the evaluation result and the generated answer, and the output is an updated database of the dialogue generation AI.

[2062] Step 7:

[2063] The server uses an emotion engine to analyze the user's emotions. The input is the user's input text, and the output is the user's emotional state. For example, if the server determines that the user is in a difficult situation, it will reflect that information in generating future answers.

[2064] Step 8:

[2065] The server sends the generated answer to the device. The device receives the answer and displays it to the user. The input is the generated answer, and the output is a text answer that is displayed to the user. For example, the answer might be "Open the Settings app, select Display Settings, and adjust brightness."

[2066] (Application example 1)

[2067] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[2068] Conventional question-answering systems for operating information terminals have difficulty providing appropriate answers to user questions and have been unable to provide feedback that takes into account the user's emotions. As a result, the user experience deteriorates and the system's continued use rate decreases.

[2069] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. In this invention, the server includes means for receiving a question from a user regarding information terminal operation, means for generating an answer to the received question, means for determining whether the generated answer is YES or NO, means for updating the dialogue generation AI based on the determination result, and an emotion engine for recognizing the user's emotions. This makes it possible to provide an appropriate answer to the user's question and also provide feedback that takes the user's emotions into consideration.

[2070] An "information terminal" is an electronic device that can be operated by a user, such as a smartphone or tablet.

[2071] The "means for receiving a question" is an interface for receiving a textual question from a user as input.

[2072] A "means for generating an answer" is an algorithm or program for generating an appropriate answer to a received question.

[2073] A "means for determining whether it is YES or NO" is the logic or program used to determine whether the generated answer is positive or negative.

[2074] "Dialogue generation artificial intelligence" is an artificial intelligence system for generating dialogue with users.

[2075] The "means for updating the dialogue generation AI" is a mechanism for updating the learning data of the dialogue generation AI based on the judgment results.

[2076] An "emotion engine" is software that recognizes and analyzes emotions from user input and dialogue.

[2077] The system for implementing this invention receives questions from users about how to operate an information terminal, generates appropriate answers to those questions, and further recognizes the user's emotions and provides feedback. A specific embodiment of this system will be described below.

[2078] The server provides a web-based interface as a means for receiving questions from users regarding the operation of the information terminal. This interface is designed to allow users to enter questions in text format. For example, if a user enters the question "How do I change the password for my smartphone's lock screen?", this question is sent to the server.

[2079] The server uses a generative AI model to generate answers to received questions. The generative AI model contains algorithms for generating appropriate answers to questions. For example, it uses OpenAI's API to provide specific instructions for answering questions.

[2080] To determine whether the generated answer is a YES or NO, the server has logic to analyze the content of the answer. This logic determines whether the answer is positive or negative. For example, it analyzes answers such as "Yes, I can" or "No, I can't."

[2081] Furthermore, the server has a means for updating the dialogue generation AI based on the judgment results. This means feeds back the generated answers and their evaluation results to the dialogue generation AI as learning data. This allows the system to continuously learn and provide more appropriate answers.

[2082] The server also includes an emotion engine that recognizes user emotions. This emotion engine is software that recognizes and analyzes emotions from user input and dialogue. For example, it uses the TextBlob library to analyze the emotion of user input text.

[2083] As a concrete example, the foll...

Claims

1. A means for receiving a question from a user regarding the operation of the information processing device; means for generating answers to received questions; means for determining whether the generated answer is YES or NO; A means for updating an AI dialogue generation system based on the result of the judgment; means for providing a web-based interface for a user to input a question in text form; A means for inputting a prompt sentence to the dialogue generation system AI, the prompt sentence instructing the dialogue generation system AI to generate an answer to a received question; A means for transmitting the generated answer to a user's terminal; A means for displaying the generated answer on the user's device; A system including:

2. 2. The system of claim 1, wherein the means for receiving a question enables receiving the question by text input.

3. The system according to claim 1 , wherein the means for updating the dialogue generation AI updates the learning data of the dialogue generation AI based on the judgment result of the generated answer.

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