system

Abstract

We provide the system. [Solution] A means of receiving questions entered by the user, A means for analyzing the aforementioned questions and generating answers, A means of displaying the generated answer, A means for the user to evaluate whether the aforementioned answer is "slightly off" from the question, A means of providing prizes based on the aforementioned evaluation, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In a conventional question-and-answer system, the answers obtained by a user for a question may not always be satisfactory. In such cases, the user's satisfaction decreases, which also affects the continued use of the system. In particular, the answers to specialized questions may be "somewhat off", and there is a need for a method that can appropriately address the dissatisfaction of users in such cases.

Means for Solving the Problems

[0005] The present invention solves the above problems by providing a system that includes means for receiving a question entered by a user, means for analyzing the question and generating an answer, means for displaying the generated answer, means for the user to evaluate whether the answer is "slightly off" from the question, and means for providing a prize based on the evaluation. Specifically, the accuracy of the answer is improved by including means for using natural language processing to analyze the received question. Furthermore, by including means for receiving evaluations entered by the user on the generated answer, the system provides a prize based on user feedback and improves user satisfaction.

[0006] A "user" refers to a person who uses a system to input questions and receive answers.

[0007] A "question" refers to a technical inquiry that a user enters into the system.

[0008] "Means of receiving" refers to the technical means by which the system takes in questions entered by the user.

[0009] "Means of analysis" refers to the processing methods used to understand the input question and generate an answer.

[0010] "Answer" refers to the response generated by the system in response to a user's question.

[0011] "Generating means" refers to technical means of creating appropriate answers based on analyzed questions.

[0012] "Means of display" refers to the technical means of providing the generated answers to the user in a visible format.

[0013] "Means of evaluation" refers to methods that allow users to judge and evaluate whether the generated answers are appropriate to the question.

[0014] "Means for providing prizes" refers to technical means for providing rewards or prizes to users based on user evaluations.

[0015] "System" refers to a series of devices and software that consistently perform inquiry reception, analysis, answer generation, display, evaluation, and prize provision.

[0016] "Natural language processing" refers to technologies and methods for a computer to understand, analyze, and generate human language.

[0017] "Feedback" refers to evaluations and reactions made by users to the system.

Brief Description of the Drawings

[0018] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

[0019] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0020] First, the language used in the following description will be explained.

[0021] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), etc.

[0022] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0023] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0024] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0025] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0026] [First Embodiment]

[0027] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0028] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0029] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0030] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0031] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0032] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0033] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0034] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0036] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0037] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0038] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0039] The system of this invention involves a user inputting a specialized question, to which a server generates an answer. Furthermore, the terminal displays the generated answer to the user, who then evaluates its appropriateness. Based on this evaluation, the server provides a prize.

[0040] Enter and submit your question

[0041] The user enters a specialized question using a dedicated terminal or computer. The terminal then sends this question to the server.

[0042] Question analysis and answer generation

[0043] The server analyzes the received question using natural language processing technology. Based on the analyzed question, the server uses a database and an AI engine to generate an appropriate answer.

[0044] Submit and display of responses

[0045] The server sends the generated response to the terminal. The terminal displays this response to the user.

[0046] Evaluation of the answer

[0047] The user reviews the answer displayed on their device and evaluates whether the answer is "slightly off" from the question. The evaluation is in the format of "yes / no". This evaluation information is then sent back from the device to the server.

[0048] Prize distribution

[0049] The server processes the received evaluation information. If the evaluation is "yes," the server provides the user with an appropriate reward. For example, this could involve sending a gift code to the user's email address or mailing a physical reward.

[0050] Specific example

[0051] Example 1: Question about the Earth's radius

[0052] The user enters the question "What is the radius of the Earth?" into the device. The device sends this question to the server. The server analyzes the question, generates the answer "The radius of the Earth is approximately 6,371 kilometers," and sends it to the device. The device displays the generated answer. The user reviews the answer and, if they rate it as "slightly off," sends the rating information from the device to the server. The server receives this rating and provides the user with a prize (e.g., a digital gift certificate).

[0053] This system can improve user satisfaction compared to conventional question-answering systems, and can maintain user satisfaction by offering prizes even when the answers to specialized questions are not appropriate.

[0054] The following describes the processing flow.

[0055] Step 1:

[0056] The user uses a terminal to enter a specialized question. For example, the user might enter the question, "What is the radius of the Earth?"

[0057] Step 2:

[0058] The terminal sends the user-entered questions to the server. Specifically, it sends the question data to the server as an HTTP request or WebSocket message.

[0059] Step 3:

[0060] The server receives questions sent from the terminal. It uses the receiving method to retrieve the question data.

[0061] Step 4:

[0062] The server analyzes received questions using natural language processing. This analysis utilizes techniques such as morphological analysis and contextual understanding.

[0063] Step 5:

[0064] The server generates appropriate answers based on the analyzed questions. It builds the answer data by referencing AI models and databases.

[0065] Step 6:

[0066] The server sends the generated response to the terminal. The response data is sent as an HTTP response or WebSocket message.

[0067] Step 7:

[0068] The terminal displays the responses received from the server to the user. Specifically, it displays the response data on the screen for the user to see.

[0069] Step 8:

[0070] Users review the displayed answers and rate their appropriateness. The rating is given as either "yes" or "no".

[0071] Step 9:

[0072] The user enters the evaluation result into the device. For example, if the result is "slightly off," they select "Yes."

[0073] Step 10:

[0074] The terminal sends the user's evaluation results to the server. The evaluation data is sent to the server as an HTTP request or WebSocket message.

[0075] Step 11:

[0076] The server receives the evaluation results sent from the terminal. It analyzes the evaluation data and verifies its contents.

[0077] Step 12:

[0078] If the rating is "yes," the server will execute the process of providing a prize. For example, it might send a digital gift certificate to the user's email address.

[0079] Step 13:

[0080] The server logs information about the prizes provided, thereby maintaining a history of prize distribution.

[0081] Through this process, users can receive answers to their expert questions, evaluate the appropriateness of those answers, and receive prizes if necessary.

[0082] (Example 1)

[0083] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0084] Traditional question-answering systems often fail to provide appropriate responses when automatically generated answers do not meet user expectations. This leads to decreased user satisfaction. Furthermore, the lack of a standardized method for users to evaluate the quality of the provided answers makes system improvement difficult.

[0085] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0086] In this invention, the server includes means for receiving information entered by a user, means for analyzing the information and generating a response, means for displaying the generated response, means for the user to evaluate whether the response is "slightly off" from the information, and means for providing a reward based on the evaluation. This makes it possible to provide real-time feedback on the quality of the system's response based on the user's evaluation and improve user satisfaction.

[0087] A "user" is an individual or group that uses a system to input information and receive a response.

[0088] "Information" refers to the questions and data that users input into the system.

[0089] A "server" is a computer system that receives information, analyzes it, and generates a response.

[0090] A "response" is the answer or result that a server generates based on the information it has analyzed.

[0091] "Evaluation" is the user's judgment of the appropriateness of the generated response.

[0092] "Rewards" refer to incentives or prizes provided to users based on evaluation information.

[0093] A "generative AI model" is an artificial intelligence technology used to analyze information and generate responses.

[0094] This invention provides a system in which a user inputs a specialized question, a server generates an answer to that question, a terminal displays the generated answer to the user, and the user evaluates the appropriateness of the answer, thereby providing the user with a prize.

[0095] Enter and submit your question

[0096] Users enter questions using a dedicated terminal or computer. For example, users can use a form on a web browser or a mobile application. The terminal sends this question to the server as an HTTP request. Specific hardware for this purpose includes the user's PC, tablet, smartphone, etc. For example, a user might enter "What is the radius of the Earth?"

[0097] Question analysis and answer generation

[0098] The server receives HTTP requests sent from terminals and extracts the text data of the questions. This text data is then analyzed using natural language processing techniques. Specifically, the server uses a generative AI model (e.g., GPT-4®). This model allows the server to analyze the received questions and generate appropriate answers. For example, in response to the question, "What is the radius of the Earth?", it would generate the answer, "The radius of the Earth is approximately 6,371 kilometers." The hardware used includes a dedicated server machine, and the software includes instances of the generative AI model.

[0099] Submit and display of responses

[0100] The server sends the generated response to the device. The generated response is sent in a standard format such as JSON. The device receives this response and displays it on the user's screen. For example, the response is displayed on the application screen of the user's smartphone.

[0101] Evaluation of the answer

[0102] The user reviews the displayed answer and evaluates its accuracy. This evaluation is done in the form of "yes / no". For example, if a user sees the answer "The Earth's radius is approximately 6,371 kilometers" and feels it is "slightly off," they select "yes." This evaluation information is then sent back to the server from the device.

[0103] Prize distribution

[0104] The server analyzes the evaluation information received from the user and provides appropriate rewards. If the evaluation is "yes," it can, for example, send a digital gift code to the user's email address or arrange for a physical product to be mailed to them.

[0105] Specific example

[0106] As a concrete example, consider a case where a user inputs the question, "What is the radius of the Earth?" The device sends this question to the server, which analyzes the question and uses a generative AI model (e.g., GPT-4) to generate the answer, "The radius of the Earth is approximately 6,371 kilometers." The answer is sent to the device and displayed to the user. If the user rates the answer as "slightly off," the rating information is sent from the device to the server, and the server provides an appropriate prize.

[0107] An example of a prompt for a generative AI model might be: "The user asked the question, 'What is the radius of the Earth?' Please generate the best possible answer to this question. The answer should be accurate and based on scientific data."

[0108] This system can improve user satisfaction compared to conventional question-answering systems. Furthermore, even if the answer to a specialized question is inappropriate, user satisfaction can be maintained by offering a prize.

[0109] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0110] Step 1: The user enters and submits the question.

[0111] Users enter questions using a dedicated terminal or computer. The input interface may include a form on a web browser or a mobile application. The question entered by the user (input) is sent to the terminal as text data. For example, if a user enters "What is the radius of the Earth?", the terminal sends this question to the server as an HTTP request in JSON format (output).

[0112] Step 2: The server receives and parses the question.

[0113] The server receives an HTTP request sent from the terminal (input). The received text data is passed to a natural language processing engine (e.g., the generative AI model GPT-4) to analyze the question. The server extracts keywords and contextual information from the question. This analysis process involves data processing to understand the context of the input question and generate the optimal response (data processing). For example, the keyword "radius of the Earth" is extracted (output).

[0114] Step 3: The server generates the answer.

[0115] The server uses a generative AI model based on the analyzed question to generate an appropriate answer. In this process, it retrieves relevant information from databases and the internet, integrates it, and generates the answer (data calculation). For example, it might generate the answer, "The Earth's radius is approximately 6,371 kilometers" (output). The generated answer is prepared as a JSON response.

[0116] Step 4: The server sends the response to the terminal.

[0117] The server sends the generated response to the terminal (input). The response sent to the terminal as an HTTP response must be in a format that the user can easily understand. Specifically, it is structured data in JSON format (output).

[0118] Step 5: The device displays the answer.

[0119] The terminal displays the response received from the server to the user (input). The user can visually confirm the response using the terminal's interface (e.g., a web page or application's text view) (output). For example, the terminal might display the text, "The Earth's radius is approximately 6,371 kilometers."

[0120] Step 6: Users rate the responses.

[0121] The user reviews the displayed response and evaluates its appropriateness (input). The evaluation is in a binary format of "yes / no". Based on the user's perception of the appropriateness of the response, they select one and send it to their device (output). For example, if the user feels it is "slightly off," they would select "yes."

[0122] Step 7: The device sends the evaluation to the server.

[0123] The terminal sends the user's rating to the server (input). The rating information is then sent back to the server as an HTTP request. For example, if "Yes" is selected, that information is sent to the server in a structured format (such as JSON) (output).

[0124] Step 8: The server provides rewards based on the evaluation information.

[0125] The server analyzes the received evaluation information and provides appropriate rewards (input). If the evaluation is "yes," the server arranges to send a digital gift code to the user via email or to mail a physical item (output). For example, a digital gift certificate may be sent to the user's email address.

[0126] This process allows users to obtain answers to their questions, evaluate the appropriateness of those answers, and receive appropriate rewards.

[0127] (Application Example 1)

[0128] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0129] Conventional question-answering systems suffer from a problem where inadequate answers to specialized questions lead to decreased user satisfaction and a lack of repeat business. This is particularly problematic in physical stores, where the inability of customers to receive immediate and accurate answers to their questions degrades the customer experience. Furthermore, incorrect answers can increase user distrust and potentially damage the store's reputation. This invention aims to solve these problems and improve user satisfaction.

[0130] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0131] In this invention, the server includes means for receiving a question entered by a user, means for analyzing the question and generating an answer, means for displaying the generated answer, means for the user to evaluate whether the answer is "slightly off" from the question, means for providing a prize based on the evaluation, means for providing the user with additional prompts to correct or supplement the generated answer, and means for providing points or coupons to the user based on the evaluation result. As a result, the user's satisfaction will increase by obtaining an accurate answer, and their willingness to reuse the service will increase by earning points or coupons according to their evaluation.

[0132] A "question" is text entered by a user to ask for specific information or knowledge they are seeking to resolve.

[0133] "Analysis" is the process of understanding an input question using natural language processing technology and extracting appropriate information.

[0134] An "answer" is the information or knowledge generated in response to an analyzed question and presented to the user.

[0135] "Display" refers to the act of showing the generated response to the user's device in a format that the user can see.

[0136] "Evaluation" is the act of a user determining whether the provided answer is appropriate to the question and providing that result as feedback.

[0137] "Prizes" refer to added value such as points, coupons, and gift certificates that are provided based on the user's evaluation results.

[0138] A "prompt statement" is an additional input statement provided to improve the accuracy of the generated response.

[0139] "Points" are numerical rewards awarded based on user ratings, and can be accumulated and exchanged for coupons or products.

[0140] A "coupon" is a voucher that allows a user to receive a monetary discount or benefit based on their evaluation results.

[0141] This invention is a system in which a user inputs a specialized question, a server analyzes it, generates an answer, and provides it to the user. Furthermore, the user evaluates the appropriateness of the answer, and a prize is offered based on that evaluation. This system is particularly intended for use in physical stores.

[0142] Hardware and software to be used

[0143] Hardware: Smartphones, servers

[0144] Software: Python, OpenAI® API, Requests library (for HTTP communication), Flask (server-side framework)

[0145] System flow

[0146] 1. Enter and submit your question.

[0147] Users use their smartphones to enter specialized questions about products. These questions are sent from the smartphone to the server. For example, a user might enter the question, "Is this product vegan-friendly?"

[0148] 2. Question analysis and answer generation

[0149] The server analyzes the received question using natural language processing techniques and generates an answer using a generative AI model (e.g., OpenAI API). For example, the server might generate the answer "This product is completely vegan."

[0150] 3. Submitting and displaying responses

[0151] The generated response is sent from the server to the smartphone and displayed on the user's screen. The user then reviews the response.

[0152] 4. Evaluation of the responses

[0153] Users rate whether their answer is appropriate to the question using a "yes / no" format. For example, if a user rates an answer as "yes," this rating is sent back to the server from their smartphone.

[0154] 5. Provision of prizes

[0155] The server provides rewards such as points and coupons based on user ratings. For example, if the rating is "yes," the user will be awarded "10 points."

[0156] Specific example

[0157] Suppose a user enters the question, "Is this product gluten-free?", and the server generates the answer, "This product is gluten-free." If the user rates this answer as "yes," the rating information is sent to the server, and the server provides the user with a digital coupon.

[0158] Example of a prompt

[0159] Question: Is this product vegan-friendly?

[0160] answer:

[0161] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0162] Step 1:

[0163] Input: The user enters the question on their smartphone.

[0164] Operation: An interface for entering a question appears on the smartphone. The user enters "Is this product vegan-friendly?".

[0165] Output: The entered question is captured by the application on the smartphone and prepared as a request to be sent to the server.

[0166] Step 2:

[0167] Input: User questions sent from a smartphone to the server.

[0168] Operation: A request containing a question arrives at the server. The server receives this question and parses its content using natural language processing. Specifically, it uses the OpenAI API to send a prompt for the question to an AI model and retrieves its answer.

[0169] Output: The generated response is returned to the server as "This product is completely vegan."

[0170] Step 3:

[0171] Input: The response returned by the AI ​​model.

[0172] Operation: The server prepares a request to send the generated response to the smartphone and displays the response to the user.

[0173] Output: The user's smartphone displays the response, "This product is completely vegan."

[0174] Step 4:

[0175] Input: User ratings for the displayed answers.

[0176] Operation: Users rate the displayed answers with "yes" or "no". A button is displayed on the rating interface, and the user presses the rating button.

[0177] Output: The evaluation result is sent from the smartphone to the server as "Yes".

[0178] Step 5:

[0179] Input: Evaluation results sent to the server.

[0180] Operation: The server receives the evaluation results and processes the provision of points or coupons to users who were evaluated as "yes". Specifically, it either adds points to the user's account or generates and sends a coupon to the user.

[0181] Output: The user will be awarded "10 points". Additionally, a digital coupon will be sent to the user's email address if necessary.

[0182] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0183] The system of this invention involves a user inputting a specialized question, to which a server generates an answer. Furthermore, the terminal displays the generated answer to the user, who then evaluates its appropriateness. Based on this evaluation, the server provides a prize. In addition, the invention incorporates an emotion engine that recognizes the user's emotions, and the system operates while taking the user's emotions into consideration.

[0184] Enter and submit your question

[0185] The user enters a specialized question using a dedicated terminal or computer. The terminal then sends this question to the server.

[0186] Question analysis and answer generation

[0187] The server analyzes the received question using natural language processing technology. Based on the analyzed question, the server uses a database and an AI engine to generate an appropriate answer.

[0188] Emotional analysis

[0189] The server uses an emotion engine to recognize the user's emotions based on the user's input and related data. For example, it analyzes emotions from positive or negative expressions contained in the input.

[0190] Submit and display of responses

[0191] The server sends the generated response and sentiment analysis results to the terminal. The terminal displays this response and sentiment analysis results to the user.

[0192] Evaluation of the answer

[0193] The user reviews the answer displayed on their device and evaluates whether the answer is "slightly off" from the question. The evaluation is in the format of "yes / no". This evaluation information is then sent back from the device to the server.

[0194] Prize distribution

[0195] The server processes the received evaluation information and sentiment analysis results. If the evaluation is "yes," it adjusts the type of prize based on the sentiment analysis results and provides the user with an appropriate prize. For example, if the user indicates negative emotions, it provides a specific prize that promotes positive emotions.

[0196] Specific example

[0197] Example 1: Question about the Earth's radius

[0198] The user enters the question, "What is the radius of the Earth?" into the device. The device sends this question to the server. The server analyzes the question and generates an answer, "The radius of the Earth is approximately 6,371 kilometers," and uses an emotion engine to analyze the user's emotions from the input. For example, if the question, "What is the radius of the Earth?", is entered in a calm tone, the server recognizes the user's emotions as neutral. The server sends the answer and the emotion analysis results to the device, which then displays them to the user.

[0199] The user reviews the response and, if they rate it as "slightly off," enters that rating into the device. The device sends the rating result to the server. Based on the rating result and sentiment analysis results, the server provides the user with a digital gift certificate; however, in this case, since the sentiment was neutral, a standard prize is provided.

[0200] This system can improve user satisfaction by considering both the accuracy of responses and user emotions. In particular, if a user expresses negative emotions, providing a special reward that addresses those emotions can help maintain user satisfaction.

[0201] The following describes the processing flow.

[0202] Step 1:

[0203] The user uses a terminal to enter a specialized question. For example, the user might enter the question, "What is the radius of the Earth?"

[0204] Step 2:

[0205] The terminal sends the user-entered questions to the server. Specifically, it sends the question data to the server as an HTTP request or WebSocket message.

[0206] Step 3:

[0207] The server receives questions sent from the terminal. It uses the receiving method to retrieve the question data.

[0208] Step 4:

[0209] The server analyzes received questions using natural language processing. This analysis utilizes techniques such as morphological analysis and contextual understanding.

[0210] Step 5:

[0211] The server generates appropriate answers based on the analyzed questions. It builds the answer data by referencing AI models and databases.

[0212] Step 6:

[0213] In addition to the generated responses, the server uses an emotion engine to analyze the user's emotions. It detects emotions such as positive, negative, or neutral based on the user's questions and context.

[0214] Step 7:

[0215] The server sends the generated response and sentiment analysis results to the terminal. The response data and sentiment data are sent as HTTP responses or WebSocket messages.

[0216] Step 8:

[0217] The terminal displays the user the responses and sentiment analysis results received from the server. Specifically, it displays the response data on the screen, as well as the sentiment analysis results.

[0218] Step 9:

[0219] Users review the displayed answers and rate their appropriateness. The rating is given as either "yes" or "no".

[0220] Step 10:

[0221] The user enters the evaluation result into the device. For example, if the result is "slightly off," they select "Yes."

[0222] Step 11:

[0223] The terminal sends the user's evaluation results to the server. The evaluation data is sent to the server as an HTTP request or WebSocket message.

[0224] Step 12:

[0225] The server receives the evaluation results sent from the terminal. It analyzes the evaluation data and verifies its contents.

[0226] Step 13:

[0227] If the evaluation is "yes," the server executes the process of providing a prize. At this time, the type of prize is adjusted based on the sentiment analysis results. For example, if the user has expressed negative emotions, a specific prize that promotes positive emotions will be provided.

[0228] Step 14:

[0229] The server logs information about the prizes provided, thereby maintaining a history of prize distribution. Through this process, users can receive answers to their expert questions, evaluate the appropriateness of those answers, and receive prizes if necessary.

[0230] As a concrete example, let's consider the case where a user enters the question, "What is the radius of the Earth?"

[0231] 1. The user enters "What is the radius of the Earth?" into the device.

[0232] 2. The device sends this question data to the server.

[0233] 3. The server receives the question data and analyzes it using natural language processing.

[0234] 4. The server generates the answer, "The Earth's radius is approximately 6,371 kilometers."

[0235] 5. The server uses an emotion engine and recognizes that the user's emotions are neutral because the input is in a calm tone.

[0236] 6. The server sends the response data and sentiment analysis results to the terminal.

[0237] 7. The device displays these to the user.

[0238] 8. The user reviews the answer and rates it as "slightly off."

[0239] 9. The user enters "Yes" as the evaluation result on the device.

[0240] 10. The device sends this evaluation result to the server.

[0241] 11. The server receives the evaluation result and, since it is "yes," decides to provide the prize.

[0242] 12. The server provides the user with an appropriate prize (e.g., a digital gift certificate) based on the user's rating and sentiment analysis results.

[0243] 13. The server logs information about the prizes provided.

[0244] In this way, we can create a system that evaluates the accuracy of the user's answers to their questions and provides prizes while also taking their emotions into consideration.

[0245] (Example 2)

[0246] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 will be referred to as the "terminal".

[0247] Traditional question-answering systems struggle to provide accurate answers to user-generated questions and fail to consider user emotions and feedback, thus failing to improve user satisfaction. Furthermore, they lacked mechanisms to evaluate user satisfaction with answers and provide appropriate rewards based on that, making it difficult to maintain user engagement. New systems and methods are needed to address these challenges.

[0248] The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving a question entered by the user, means for analyzing the question and generating an answer, means for displaying the generated answer and sentiment analysis results, means for the user to evaluate whether the answer is "slightly off" from the question, and means for providing a prize based on the evaluation and sentiment analysis results. This not only provides accurate answers to the user's questions but also enables responses that take into account the user's emotions and feedback, thereby improving user satisfaction. Furthermore, by providing appropriate prizes based on the evaluation results, it is possible to increase user engagement.

[0249] A "user" refers to a person who uses the system to input questions and receive answers.

[0250] A "question" refers to information or a question that a user enters into a system.

[0251] "Means of receiving" refers to devices and software that send user questions and evaluations to a server and receive that information on the server.

[0252] "Means of analysis" refers to devices and software that use natural language processing techniques to understand received questions and generate appropriate answers.

[0253] "Answer" refers to the information or response text that the server generates in response to an analyzed question.

[0254] "Means of display" refers to devices or software that visually present generated responses and sentiment analysis results to the user.

[0255] "Means of evaluation" refers to devices or software that allow users to determine whether the displayed answers are appropriate and to input the results.

[0256] "Means of providing prizes" refers to devices or software that provide rewards or benefits to users based on user ratings and sentiment analysis results.

[0257] "Emotion analysis results" refer to data about the emotional state identified by the server based on the user's questions and feedback, after the server has analyzed the user's emotions.

[0258] "Natural language processing" refers to the technology that enables computers to understand, interpret, and manipulate human language.

[0259] A "server" refers to a computer system that receives and analyzes user inquiries, provides answers and sentiment analysis, and sends back the results.

[0260] A "terminal" refers to a device or equipment used by a user to input questions, display responses and sentiment analysis results from a server, and submit evaluations.

[0261] The present invention provides a system in which a user inputs a specialized question, and a server generates an appropriate answer to that question. Furthermore, the server has a function to analyze the user's emotions and provide the user with a prize based on that analysis. This system aims not only to improve user satisfaction but also to increase user engagement. Its embodiments are described in detail below.

[0262] Hardware and software to be used

[0263] User's device: An electronic device such as a personal computer or smartphone. Questions are entered, displayed, and evaluated through a dedicated application or web browser.

[0264] Server: A high-performance computer system. It analyzes questions and generates answers, analyzes sentiment, and processes evaluation results.

[0265] Natural language processing engine: Software for analyzing questions. For example, Google's Cloud Natural Language API is a natural language processing API.

[0266] Database: A data source for searching and generating answers to questions. For example, the Wikipedia API as an external API.

[0267] AI engine: An artificial intelligence model for generating answers. For example, GPT-4.

[0268] Emotion engine: Software used to analyze a user's emotions. For example, Microsoft® Azure® Emotion API.

[0269] Specific operation of the system

[0270] 1. Enter and submit your question:

[0271] Users enter questions into a dedicated application or input form on a web browser using a PC or smartphone. For example, they might enter "What is the radius of the Earth?"

[0272] The terminal sends this entered question to the server over the internet, specifically using an HTTP POST request.

[0273] 2. Question analysis and answer generation:

[0274] The server analyzes the received question using natural language processing technology (Google Cloud Natural Language API). This analysis helps understand the intent and content of the question.

[0275] Based on the analysis results, the server uses a database (Wikipedia API) and an AI engine (GPT-4) to generate appropriate answers. For example, it might generate an answer such as, "The Earth's radius is approximately 6,371 kilometers."

[0276] 3. Analysis of emotions:

[0277] The server analyzes the user's input using an emotion engine (Microsoft Azure Emotion API) to determine the user's emotion. For example, if the question "What is the radius of the Earth?" is entered in a calm tone, the server will recognize the user's emotion as neutral.

[0278] 4. Submitting and viewing responses:

[0279] The server sends the generated response and sentiment analysis results to the terminal.

[0280] The device displays the received response and sentiment analysis results to the user. For example, it might display "The Earth's radius is approximately 6,371 kilometers" along with the sentiment analysis results.

[0281] 5. Evaluation of Answers:

[0282] The user checks the answer displayed on the terminal and evaluates whether the answer is appropriate for the question. For example, answer "Yes" or "No" to the question "Is this answer appropriate?".

[0283] The terminal sends the user's evaluation information back to the server. The evaluation data is sent using an HTTP POST request.

[0284] 6. Provision of Prizes:

[0285] The server performs processing based on the received evaluation information and sentiment analysis results. If the evaluation is "Yes", the type of prize is adjusted based on the sentiment analysis results.

[0286] The server executes the process of providing the selected prize to the user. For example, it sends a digital gift voucher via email or provides a download link.

[0287] Specific Example

[0288] The user enters "What is the radius of the Earth?" on the terminal. The terminal sends this question to the server. The server analyzes the question and generates an answer "The radius of the Earth is about 6,371 kilometers". Furthermore, using the sentiment engine, the user's sentiment is recognized as neutral. The server sends the answer and the sentiment analysis result to the terminal, and the terminal displays this to the user. The user checks the answer and evaluates it as "Yes". The terminal sends the evaluation result to the server. The server provides a digital gift voucher to the user based on the evaluation result and the sentiment analysis result.

[0289] Examples of Prompt Sentences:

[0290] Examples of Prompt Sentences for Generating System Descriptions:

[0291] "This system works by having the user input a specialized question, and the server generates the answer. The server uses natural language processing technology to analyze the question and generates the answer using a database and an AI engine. Furthermore, it uses an emotion engine to recognize the user's emotions and provides an appropriate prize based on the results. The terminal displays the generated answer and the emotion analysis results to the user, who evaluates whether the answer is appropriate. The evaluation results are sent back to the server, which then provides the user with a prize based on that information."

[0292] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0293] Step 1:

[0294] The user enters the question using their device. Specifically, the user enters a question such as "What is the radius of the Earth?" in text format into an input form on a dedicated application or web browser. This input is temporarily stored on the device as data to be sent to the server.

[0295] Input: User's question text

[0296] Output: Question data for server transmission

[0297] Step 2:

[0298] The terminal generates an HTTP POST request to send the entered question data to the server. Specifically, it includes the question text in the HTTP request body and sends the request to a specific endpoint on the server.

[0299] Input: Question data entered by the user

[0300] Output: HTTP request sent to the server

[0301] Step 3:

[0302] The server extracts question data from the received HTTP request and analyzes the question using a natural language processing engine (Google Cloud Natural Language API). Through this analysis, the intention and content of the question are grasped.

[0303] Input: Question data sent from the terminal

[0304] Output: Analyzed question data

[0305] Step 4:

[0306] Based on the analyzed question data, the server uses a database (Wikipedia API) and an AI engine (GPT-4) to generate an answer. For example, it generates an answer such as "The radius of the Earth is approximately 6,371 kilometers." In this process, a pre-trained AI model is utilized.

[0307] Input: Analyzed question data

[0308] Output: Generated answer data

[0309] Step 5:

[0310] The server analyzes the user's input text with an emotion engine (Microsoft Azure Emotion API) to determine the user's emotional state. For example, it identifies emotional states such as positive, negative, or neutral.

[0311] Input: The user's question text

[0312] Output: Emotion analysis result data

[0313] Step 6:

[0314] The server generates an HTTP response to send the generated response data and sentiment analysis results to the terminal. The response includes the response and sentiment analysis results.

[0315] Input: Generated response data and sentiment analysis result data

[0316] Output: HTTP response sent to the terminal

[0317] Step 7:

[0318] The device analyzes the HTTP response received from the server and displays the response data and sentiment analysis results to the user. Specifically, the screen displays "The Earth's radius is approximately 6,371 kilometers," along with the sentiment analysis results.

[0319] Input: HTTP response sent from the server

[0320] Output: Responses and sentiment analysis results displayed to the user

[0321] Step 8:

[0322] The user reviews the answers displayed on their device and evaluates their appropriateness using a "yes / no" format. For example, they might answer "yes" to the question, "Is this answer appropriate?"

[0323] Input: Select user rating

[0324] Output: Evaluation data entered into the terminal

[0325] Step 9:

[0326] The device generates an HTTP POST request to send the user's rating data back to the server. The request includes the user's rating information.

[0327] Input: Evaluation data entered by the user.

[0328] Output: HTTP request sent to the server

[0329] Step 10:

[0330] The server processes the prize distribution based on the received evaluation information and sentiment analysis results. If the evaluation is "yes," it selects an appropriate prize based on the sentiment analysis results and provides it to the user. For example, it may send a digital gift certificate via email or provide a download link.

[0331] Input: User rating data and sentiment analysis results data

[0332] Output: Prizes provided to the user

[0333] (Application Example 2)

[0334] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0335] Traditional question-answering systems only provide appropriate answers to user-entered questions, lacking feedback and sentiment analysis to enhance user satisfaction. This creates a risk of inaccurate answers or responses that disregard user feelings. Furthermore, they fail to offer incentives that reflect user satisfaction.

[0336] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0337] In this invention, the server includes means for receiving a question entered by a user, means for analyzing the question and generating an answer, and means for displaying the generated answer and sentiment analysis results. This enables the provision of appropriate answers to the user's questions and the provision of incentives based on sentiment analysis.

[0338] A "user" is a party who uses this system to input questions and receive answers.

[0339] A "question" refers to the content of an inquiry entered by a user, seeking specialized information or knowledge.

[0340] "Means" refers to the technical components used to perform a specific function or task.

[0341] "Analysis" is the process of analyzing an input question using techniques such as natural language processing.

[0342] "Answer" refers to the information provided by the system in response to the analyzed question.

[0343] "Emotion analysis" is the process of recognizing and analyzing a user's emotions based on their input and other factors.

[0344] "Evaluation" is the act of a user judging the appropriateness of the answer they have provided and providing feedback to the system.

[0345] "Prizes" refers to rewards or incentives provided by the system based on user ratings and sentiment analysis results.

[0346] "Emotional analysis results" refer to information about the user's emotional state obtained based on emotional analysis.

[0347] "Generation" refers to the process by which the system creates new answers or sentiment analysis results based on the analysis results.

[0348] To realize this invention, it is necessary to use a cloud server, a user terminal, and related software.

[0349] Hardware configuration

[0350] Cloud Server: Responsible for data processing and storage. Utilizes cloud services such as AWS® and Google Cloud Platform.

[0351] User terminal: A device used for entering questions, viewing answers, and entering ratings. Primarily a smartphone is used.

[0352] Software Configuration

[0353] 1. Natural Language Processing (NLP) Techniques:

[0354] It uses TENSORFLOW® and spaCy to analyze questions and generate appropriate answers.

[0355] 2. Emotion analysis engine:

[0356] The IBM Watson® Emotion Analysis API is used to analyze the user's emotions.

[0357] 3. Database:

[0358] Use Firebase Firestore to store and manage question, answer, and rating data.

[0359] Program processing

[0360] The server receives questions entered by users and analyzes them using natural language processing technology. Based on the analyzed questions, the server utilizes a database and generative AI models to generate appropriate answers.

[0361] Next, an emotion analysis engine is used to analyze the user's emotions from their input. For example, the positive or negative expressions included in the input are used to recognize the user's emotional state.

[0362] The server sends the generated response and sentiment analysis results to the user's terminal. The user's terminal displays the response and sentiment analysis results to the user. The user reviews the displayed response and evaluates whether it is appropriate. The user's evaluation is in the form of "yes / no," and the evaluation information is sent back from the user's terminal to the server.

[0363] The server processes the received evaluation information and sentiment analysis results. If the evaluation is "yes," it adjusts the type of prize based on the sentiment analysis results. For example, if the user indicates negative emotions, it provides a specific prize that promotes positive emotions.

[0364] Specific example

[0365] Let's say a user launches a smartphone app and types, "How do I use an electronic payment app?" A cloud server receives the question and analyzes it using TensorFlow or spaCy. Based on the analysis, the server generates an answer, such as, "To use an electronic payment app, open the app, select a payment option, and enter the appropriate information."

[0366] Next, the server uses the IBM Watson Emotion Analysis API to analyze the user's input and recognizes that the user is feeling slightly anxious. This response and the emotion analysis result are sent to the user's terminal, where the user reviews the displayed content on the screen and evaluates whether it is appropriate. If the evaluation is "yes" and the emotion analysis result indicates slight anxiety, a reward (for example, a discount coupon usable on the next visit) is offered to alleviate that anxiety.

[0367] Example of a prompt

[0368] "Please enter your question about electronic payment apps. We will also perform sentiment analysis based on your answers. We will evaluate the accuracy of your answers and offer prizes accordingly."

[0369] In this way, by analyzing the user's emotional state and providing corresponding responses and incentives, it becomes possible to improve user satisfaction.

[0370] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0371] Step 1:

[0372] The user launches the app on their smartphone and enters a question. The entered information is sent from the user's device to the server. The questions entered here are specialized inquiries and are sent in text format.

[0373] Step 2:

[0374] The server analyzes the received question and generates an answer. In this process, the server uses natural language processing technologies such as TensorFlow and spaCy to analyze the question and generate an appropriate response. The generated answer is stored on the server in text format.

[0375] Step 3:

[0376] The server performs sentiment analysis based on the analyzed questions and generated answers. Using the IBM Watson Emotion Analysis API, it analyzes the user's emotions from the question input. The sentiment analysis results are output as specific emotional states such as positive, negative, or neutral.

[0377] Step 4:

[0378] The server sends the generated response and sentiment analysis results to the user's terminal. The user's terminal displays the received response and sentiment analysis results on its screen. The content displayed here shows the response to the question and the user's emotional state.

[0379] Step 5:

[0380] The user reviews the displayed answers and evaluates their appropriateness. The evaluation is entered into the user's terminal in the format of "yes / no". The evaluation results are then sent back to the server from the user's terminal.

[0381] Step 6:

[0382] The server processes the provision of prizes based on the received evaluation information and sentiment analysis results. If the evaluation is "yes" and the sentiment analysis result is negative, it determines and provides a prize (e.g., a discount coupon) to promote positive emotions in the user. The details of the prize provided are recorded in the database.

[0383] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0384] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0385] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0386] [Second Embodiment]

[0387] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0388] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0389] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0390] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0391] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0392] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0393] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0394] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0395] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0396] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0397] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0398] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0399] The system of this invention involves a user inputting a specialized question, to which a server generates an answer. Furthermore, the terminal displays the generated answer to the user, who then evaluates its appropriateness. Based on this evaluation, the server provides a prize.

[0400] Enter and submit your question

[0401] The user enters a specialized question using a dedicated terminal or computer. The terminal then sends this question to the server.

[0402] Question analysis and answer generation

[0403] The server analyzes the received question using natural language processing technology. Based on the analyzed question, the server uses a database and an AI engine to generate an appropriate answer.

[0404] Submit and display of responses

[0405] The server sends the generated response to the terminal. The terminal displays this response to the user.

[0406] Evaluation of the answer

[0407] The user reviews the answer displayed on their device and evaluates whether the answer is "slightly off" from the question. The evaluation is in the format of "yes / no". This evaluation information is then sent back from the device to the server.

[0408] Prize distribution

[0409] The server processes the received evaluation information. If the evaluation is "yes," the server provides the user with an appropriate reward. For example, this could involve sending a gift code to the user's email address or mailing a physical reward.

[0410] Specific example

[0411] Example 1: Question about the Earth's radius

[0412] The user enters the question "What is the radius of the Earth?" into the device. The device sends this question to the server. The server analyzes the question, generates the answer "The radius of the Earth is approximately 6,371 kilometers," and sends it to the device. The device displays the generated answer. The user reviews the answer and, if they rate it as "slightly off," sends the rating information from the device to the server. The server receives this rating and provides the user with a prize (e.g., a digital gift certificate).

[0413] This system can improve user satisfaction compared to conventional question-answering systems, and can maintain user satisfaction by offering prizes even when the answers to specialized questions are not appropriate.

[0414] The following describes the processing flow.

[0415] Step 1:

[0416] The user uses a terminal to enter a specialized question. For example, the user might enter the question, "What is the radius of the Earth?"

[0417] Step 2:

[0418] The terminal sends the user-entered questions to the server. Specifically, it sends the question data to the server as an HTTP request or WebSocket message.

[0419] Step 3:

[0420] The server receives questions sent from the terminal. It uses the receiving method to retrieve the question data.

[0421] Step 4:

[0422] The server analyzes received questions using natural language processing. This analysis utilizes techniques such as morphological analysis and contextual understanding.

[0423] Step 5:

[0424] The server generates appropriate answers based on the analyzed questions. It builds the answer data by referencing AI models and databases.

[0425] Step 6:

[0426] The server sends the generated response to the terminal. The response data is sent as an HTTP response or WebSocket message.

[0427] Step 7:

[0428] The terminal displays the responses received from the server to the user. Specifically, it displays the response data on the screen for the user to see.

[0429] Step 8:

[0430] Users review the displayed answers and rate their appropriateness. The rating is given as either "yes" or "no".

[0431] Step 9:

[0432] The user enters the evaluation result into the device. For example, if the result is "slightly off," they select "Yes."

[0433] Step 10:

[0434] The terminal sends the user's evaluation results to the server. The evaluation data is sent to the server as an HTTP request or WebSocket message.

[0435] Step 11:

[0436] The server receives the evaluation results sent from the terminal. It analyzes the evaluation data and verifies its contents.

[0437] Step 12:

[0438] If the rating is "yes," the server will execute the process of providing a prize. For example, it might send a digital gift certificate to the user's email address.

[0439] Step 13:

[0440] The server logs information about the prizes provided, thereby maintaining a history of prize distribution.

[0441] Through this process, users can receive answers to their expert questions, evaluate the appropriateness of those answers, and receive prizes if necessary.

[0442] (Example 1)

[0443] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0444] Traditional question-answering systems often fail to provide appropriate responses when automatically generated answers do not meet user expectations. This leads to decreased user satisfaction. Furthermore, the lack of a standardized method for users to evaluate the quality of the provided answers makes system improvement difficult.

[0445] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0446] In this invention, the server includes means for receiving information entered by a user, means for analyzing the information and generating a response, means for displaying the generated response, means for the user to evaluate whether the response is "slightly off" from the information, and means for providing a reward based on the evaluation. This makes it possible to provide real-time feedback on the quality of the system's response based on the user's evaluation and improve user satisfaction.

[0447] A "user" is an individual or group that uses a system to input information and receive a response.

[0448] "Information" refers to the questions and data that users input into the system.

[0449] A "server" is a computer system that receives information, analyzes it, and generates a response.

[0450] A "response" is the answer or result that a server generates based on the information it has analyzed.

[0451] "Evaluation" is the user's judgment of the appropriateness of the generated response.

[0452] "Rewards" refer to incentives or prizes provided to users based on evaluation information.

[0453] A "generative AI model" is an artificial intelligence technology used to analyze information and generate responses.

[0454] This invention provides a system in which a user inputs a specialized question, a server generates an answer to that question, a terminal displays the generated answer to the user, and the user evaluates the appropriateness of the answer, thereby providing the user with a prize.

[0455] Enter and submit your question

[0456] Users enter questions using a dedicated terminal or computer. For example, users can use a form on a web browser or a mobile application. The terminal sends this question to the server as an HTTP request. Specific hardware for this purpose includes the user's PC, tablet, smartphone, etc. For example, a user might enter "What is the radius of the Earth?"

[0457] Question analysis and answer generation

[0458] The server receives HTTP requests sent from terminals and extracts the text data of the questions. This text data is then analyzed using natural language processing techniques. Specifically, the server uses a generative AI model (e.g., GPT-4). This model allows the server to analyze the received questions and generate appropriate answers. For example, in response to the question "What is the radius of the Earth?", it would generate the answer "The radius of the Earth is approximately 6,371 kilometers." The hardware used includes a dedicated server machine, and the software includes instances of the generative AI model.

[0459] Submit and display of responses

[0460] The server sends the generated response to the device. The generated response is sent in a standard format such as JSON. The device receives this response and displays it on the user's screen. For example, the response is displayed on the application screen of the user's smartphone.

[0461] Evaluation of the answer

[0462] The user reviews the displayed answer and evaluates its accuracy. This evaluation is done in the form of "yes / no". For example, if a user sees the answer "The Earth's radius is approximately 6,371 kilometers" and feels it is "slightly off," they select "yes." This evaluation information is then sent back to the server from the device.

[0463] Prize distribution

[0464] The server analyzes the evaluation information received from the user and provides appropriate rewards. If the evaluation is "yes," it can, for example, send a digital gift code to the user's email address or arrange for a physical product to be mailed to them.

[0465] Specific example

[0466] As a concrete example, consider a case where a user inputs the question, "What is the radius of the Earth?" The device sends this question to the server, which analyzes the question and uses a generative AI model (e.g., GPT-4) to generate the answer, "The radius of the Earth is approximately 6,371 kilometers." The answer is sent to the device and displayed to the user. If the user rates the answer as "slightly off," the rating information is sent from the device to the server, and the server provides an appropriate prize.

[0467] An example of a prompt for a generative AI model might be: "The user asked the question, 'What is the radius of the Earth?' Please generate the best possible answer to this question. The answer should be accurate and based on scientific data."

[0468] This system can improve user satisfaction compared to conventional question-answering systems. Furthermore, even if the answer to a specialized question is inappropriate, user satisfaction can be maintained by offering a prize.

[0469] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0470] Step 1: The user enters and submits the question.

[0471] Users enter questions using a dedicated terminal or computer. The input interface may include a form on a web browser or a mobile application. The question entered by the user (input) is sent to the terminal as text data. For example, if a user enters "What is the radius of the Earth?", the terminal sends this question to the server as an HTTP request in JSON format (output).

[0472] Step 2: The server receives and parses the question.

[0473] The server receives an HTTP request sent from the terminal (input). The received text data is passed to a natural language processing engine (e.g., the generative AI model GPT-4) to analyze the question. The server extracts keywords and contextual information from the question. This analysis process involves data processing to understand the context of the input question and generate the optimal response (data processing). For example, the keyword "radius of the Earth" is extracted (output).

[0474] Step 3: The server generates the answer.

[0475] The server uses a generative AI model based on the analyzed question to generate an appropriate answer. In this process, it retrieves relevant information from databases and the internet, integrates it, and generates the answer (data calculation). For example, it might generate the answer, "The Earth's radius is approximately 6,371 kilometers" (output). The generated answer is prepared as a JSON response.

[0476] Step 4: The server sends the response to the terminal.

[0477] The server sends the generated response to the terminal (input). The response sent to the terminal as an HTTP response must be in a format that the user can easily understand. Specifically, it is structured data in JSON format (output).

[0478] Step 5: The device displays the answer.

[0479] The terminal displays the response received from the server to the user (input). The user can visually confirm the response using the terminal's interface (e.g., a web page or application's text view) (output). For example, the terminal might display the text, "The Earth's radius is approximately 6,371 kilometers."

[0480] Step 6: Users rate the responses.

[0481] The user reviews the displayed response and evaluates its appropriateness (input). The evaluation is in a binary format of "yes / no". Based on the user's perception of the appropriateness of the response, they select one and send it to their device (output). For example, if the user feels it is "slightly off," they would select "yes."

[0482] Step 7: The device sends the evaluation to the server.

[0483] The terminal sends the user's rating to the server (input). The rating information is then sent back to the server as an HTTP request. For example, if "Yes" is selected, that information is sent to the server in a structured format (such as JSON) (output).

[0484] Step 8: The server provides rewards based on the evaluation information.

[0485] The server analyzes the received evaluation information and provides appropriate rewards (input). If the evaluation is "yes," the server arranges to send a digital gift code to the user via email or to mail a physical item (output). For example, a digital gift certificate may be sent to the user's email address.

[0486] This process allows users to obtain answers to their questions, evaluate the appropriateness of those answers, and receive appropriate rewards.

[0487] (Application Example 1)

[0488] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0489] Conventional question-answering systems suffer from a problem where inadequate answers to specialized questions lead to decreased user satisfaction and a lack of repeat business. This is particularly problematic in physical stores, where the inability of customers to receive immediate and accurate answers to their questions degrades the customer experience. Furthermore, incorrect answers can increase user distrust and potentially damage the store's reputation. This invention aims to solve these problems and improve user satisfaction.

[0490] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0491] In this invention, the server includes means for receiving a question entered by a user, means for analyzing the question and generating an answer, means for displaying the generated answer, means for the user to evaluate whether the answer is "slightly off" from the question, means for providing a prize based on the evaluation, means for providing the user with additional prompts to correct or supplement the generated answer, and means for providing points or coupons to the user based on the evaluation result. As a result, the user's satisfaction will increase by obtaining an accurate answer, and their willingness to reuse the service will increase by earning points or coupons according to their evaluation.

[0492] A "question" is text entered by a user to ask for specific information or knowledge they are seeking to resolve.

[0493] "Analysis" is the process of understanding an input question using natural language processing technology and extracting appropriate information.

[0494] An "answer" is the information or knowledge generated in response to an analyzed question and presented to the user.

[0495] "Display" refers to the act of showing the generated response to the user's device in a format that the user can see.

[0496] "Evaluation" is the act of a user determining whether the provided answer is appropriate to the question and providing that result as feedback.

[0497] "Prizes" refer to added value such as points, coupons, and gift certificates that are provided based on the user's evaluation results.

[0498] A "prompt statement" is an additional input statement provided to improve the accuracy of the generated response.

[0499] "Points" are numerical rewards awarded based on user ratings, and can be accumulated and exchanged for coupons or products.

[0500] A "coupon" is a voucher that allows a user to receive a monetary discount or benefit based on their evaluation results.

[0501] This invention is a system in which a user inputs a specialized question, a server analyzes it, generates an answer, and provides it to the user. Furthermore, the user evaluates the appropriateness of the answer, and a prize is offered based on that evaluation. This system is particularly intended for use in physical stores.

[0502] Hardware and software to be used

[0503] Hardware: Smartphones, servers

[0504] Software: Python, OpenAI API, Requests library (for HTTP communication), Flask (server-side framework)

[0505] System flow

[0506] 1. Enter and submit your question.

[0507] Users use their smartphones to enter specialized questions about products. These questions are sent from the smartphone to the server. For example, a user might enter the question, "Is this product vegan-friendly?"

[0508] 2. Question analysis and answer generation

[0509] The server analyzes the received question using natural language processing techniques and generates an answer using a generative AI model (e.g., OpenAI API). For example, the server might generate the answer "This product is completely vegan."

[0510] 3. Submitting and displaying responses

[0511] The generated response is sent from the server to the smartphone and displayed on the user's screen. The user then reviews the response.

[0512] 4. Evaluation of the responses

[0513] Users rate whether their answer is appropriate to the question using a "yes / no" format. For example, if a user rates an answer as "yes," this rating is sent back to the server from their smartphone.

[0514] 5. Provision of prizes

[0515] The server provides rewards such as points and coupons based on user ratings. For example, if the rating is "yes," the user will be awarded "10 points."

[0516] Specific example

[0517] Suppose a user enters the question, "Is this product gluten-free?", and the server generates the answer, "This product is gluten-free." If the user rates this answer as "yes," the rating information is sent to the server, and the server provides the user with a digital coupon.

[0518] Example of a prompt

[0519] Question: Is this product vegan-friendly?

[0520] answer:

[0521] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0522] Step 1:

[0523] Input: The user enters the question on their smartphone.

[0524] Operation: An interface for entering a question appears on the smartphone. The user enters "Is this product vegan-friendly?".

[0525] Output: The entered question is captured by the application on the smartphone and prepared as a request to be sent to the server.

[0526] Step 2:

[0527] Input: User questions sent from a smartphone to the server.

[0528] Operation: A request containing a question arrives at the server. The server receives this question and parses its content using natural language processing. Specifically, it uses the OpenAI API to send a prompt for the question to an AI model and retrieves its answer.

[0529] Output: The generated response is returned to the server as "This product is completely vegan."

[0530] Step 3:

[0531] Input: The response returned by the AI ​​model.

[0532] Operation: The server prepares a request to send the generated response to the smartphone and displays the response to the user.

[0533] Output: The user's smartphone displays the response, "This product is completely vegan."

[0534] Step 4:

[0535] Input: User ratings for the displayed answers.

[0536] Operation: Users rate the displayed answers with "yes" or "no". A button is displayed on the rating interface, and the user presses the rating button.

[0537] Output: The evaluation result is sent from the smartphone to the server as "Yes".

[0538] Step 5:

[0539] Input: Evaluation results sent to the server.

[0540] Operation: The server receives the evaluation results and processes the provision of points or coupons to users who were evaluated as "yes". Specifically, it either adds points to the user's account or generates and sends a coupon to the user.

[0541] Output: The user will be awarded "10 points". Additionally, a digital coupon will be sent to the user's email address if necessary.

[0542] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0543] The system of this invention involves a user inputting a specialized question, to which a server generates an answer. Furthermore, the terminal displays the generated answer to the user, who then evaluates its appropriateness. Based on this evaluation, the server provides a prize. In addition, the invention incorporates an emotion engine that recognizes the user's emotions, and the system operates while taking the user's emotions into consideration.

[0544] Enter and submit your question

[0545] The user enters a specialized question using a dedicated terminal or computer. The terminal then sends this question to the server.

[0546] Question analysis and answer generation

[0547] The server analyzes the received question using natural language processing technology. Based on the analyzed question, the server uses a database and an AI engine to generate an appropriate answer.

[0548] Emotional analysis

[0549] The server uses an emotion engine to recognize the user's emotions based on the user's input and related data. For example, it analyzes emotions from positive or negative expressions contained in the input.

[0550] Submit and display of responses

[0551] The server sends the generated response and sentiment analysis results to the terminal. The terminal displays this response and sentiment analysis results to the user.

[0552] Evaluation of the answer

[0553] The user reviews the answer displayed on their device and evaluates whether the answer is "slightly off" from the question. The evaluation is in the format of "yes / no". This evaluation information is then sent back from the device to the server.

[0554] Prize distribution

[0555] The server processes the received evaluation information and sentiment analysis results. If the evaluation is "yes," it adjusts the type of prize based on the sentiment analysis results and provides the user with an appropriate prize. For example, if the user indicates negative emotions, it provides a specific prize that promotes positive emotions.

[0556] Specific example

[0557] Example 1: Question about the Earth's radius

[0558] The user enters the question, "What is the radius of the Earth?" into the device. The device sends this question to the server. The server analyzes the question and generates an answer, "The radius of the Earth is approximately 6,371 kilometers," and uses an emotion engine to analyze the user's emotions from the input. For example, if the question, "What is the radius of the Earth?", is entered in a calm tone, the server recognizes the user's emotions as neutral. The server sends the answer and the emotion analysis results to the device, which then displays them to the user.

[0559] The user reviews the response and, if they rate it as "slightly off," enters that rating into the device. The device sends the rating result to the server. Based on the rating result and sentiment analysis results, the server provides the user with a digital gift certificate; however, in this case, since the sentiment was neutral, a standard prize is provided.

[0560] This system can improve user satisfaction by considering both the accuracy of responses and user emotions. In particular, if a user expresses negative emotions, providing a special reward that addresses those emotions can help maintain user satisfaction.

[0561] The following describes the processing flow.

[0562] Step 1:

[0563] The user uses a terminal to enter a specialized question. For example, the user might enter the question, "What is the radius of the Earth?"

[0564] Step 2:

[0565] The terminal sends the user-entered questions to the server. Specifically, it sends the question data to the server as an HTTP request or WebSocket message.

[0566] Step 3:

[0567] The server receives questions sent from the terminal. It uses the receiving method to retrieve the question data.

[0568] Step 4:

[0569] The server analyzes received questions using natural language processing. This analysis utilizes techniques such as morphological analysis and contextual understanding.

[0570] Step 5:

[0571] The server generates appropriate answers based on the analyzed questions. It builds the answer data by referencing AI models and databases.

[0572] Step 6:

[0573] In addition to the generated responses, the server uses an emotion engine to analyze the user's emotions. It detects emotions such as positive, negative, or neutral based on the user's questions and context.

[0574] Step 7:

[0575] The server sends the generated response and sentiment analysis results to the terminal. The response data and sentiment data are sent as HTTP responses or WebSocket messages.

[0576] Step 8:

[0577] The terminal displays the user the responses and sentiment analysis results received from the server. Specifically, it displays the response data on the screen, as well as the sentiment analysis results.

[0578] Step 9:

[0579] Users review the displayed answers and rate their appropriateness. The rating is given as either "yes" or "no".

[0580] Step 10:

[0581] The user enters the evaluation result into the device. For example, if the result is "slightly off," they select "Yes."

[0582] Step 11:

[0583] The terminal sends the user's evaluation results to the server. The evaluation data is sent to the server as an HTTP request or WebSocket message.

[0584] Step 12:

[0585] The server receives the evaluation results sent from the terminal. It analyzes the evaluation data and verifies its contents.

[0586] Step 13:

[0587] If the evaluation is "yes," the server executes the process of providing a prize. At this time, the type of prize is adjusted based on the sentiment analysis results. For example, if the user has expressed negative emotions, a specific prize that promotes positive emotions will be provided.

[0588] Step 14:

[0589] The server logs information about the prizes provided, thereby maintaining a history of prize distribution. Through this process, users can receive answers to their expert questions, evaluate the appropriateness of those answers, and receive prizes if necessary.

[0590] As a concrete example, let's consider the case where a user enters the question, "What is the radius of the Earth?"

[0591] 1. The user enters "What is the radius of the Earth?" into the device.

[0592] 2. The device sends this question data to the server.

[0593] 3. The server receives the question data and analyzes it using natural language processing.

[0594] 4. The server generates the answer, "The Earth's radius is approximately 6,371 kilometers."

[0595] 5. The server uses an emotion engine and recognizes that the user's emotions are neutral because the input is in a calm tone.

[0596] 6. The server sends the response data and sentiment analysis results to the terminal.

[0597] 7. The device displays these to the user.

[0598] 8. The user reviews the answer and rates it as "slightly off."

[0599] 9. The user enters "Yes" as the evaluation result on the device.

[0600] 10. The device sends this evaluation result to the server.

[0601] 11. The server receives the evaluation result and, since it is "yes," decides to provide the prize.

[0602] 12. The server provides the user with an appropriate prize (e.g., a digital gift certificate) based on the user's rating and sentiment analysis results.

[0603] 13. The server logs information about the prizes provided.

[0604] In this way, we can create a system that evaluates the accuracy of the user's answers to their questions and provides prizes while also taking their emotions into consideration.

[0605] (Example 2)

[0606] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0607] Traditional question-answering systems struggle to provide accurate answers to user-generated questions and fail to consider user emotions and feedback, thus failing to improve user satisfaction. Furthermore, they lacked mechanisms to evaluate user satisfaction with answers and provide appropriate rewards based on that, making it difficult to maintain user engagement. New systems and methods are needed to address these challenges.

[0608] The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving a question entered by the user, means for analyzing the question and generating an answer, means for displaying the generated answer and sentiment analysis results, means for the user to evaluate whether the answer is "slightly off" from the question, and means for providing a prize based on the evaluation and sentiment analysis results. This not only provides accurate answers to the user's questions but also enables responses that take into account the user's emotions and feedback, thereby improving user satisfaction. Furthermore, by providing appropriate prizes based on the evaluation results, it is possible to increase user engagement.

[0609] A "user" refers to a person who uses the system to input questions and receive answers.

[0610] A "question" refers to information or a question that a user enters into a system.

[0611] "Means of receiving" refers to devices and software that send user questions and evaluations to a server and receive that information on the server.

[0612] "Means of analysis" refers to devices and software that use natural language processing techniques to understand received questions and generate appropriate answers.

[0613] "Answer" refers to the information or response text that the server generates in response to an analyzed question.

[0614] "Means of display" refers to devices or software that visually present generated responses and sentiment analysis results to the user.

[0615] "Means of evaluation" refers to devices or software that allow users to determine whether the displayed answers are appropriate and to input the results.

[0616] "Means of providing prizes" refers to devices or software that provide rewards or benefits to users based on user ratings and sentiment analysis results.

[0617] "Emotion analysis results" refer to data about the emotional state identified by the server based on the user's questions and feedback, after the server has analyzed the user's emotions.

[0618] "Natural language processing" refers to the technology that enables computers to understand, interpret, and manipulate human language.

[0619] A "server" refers to a computer system that receives and analyzes user inquiries, provides answers and sentiment analysis, and sends back the results.

[0620] A "terminal" refers to a device or equipment used by a user to input questions, display responses and sentiment analysis results from a server, and submit evaluations.

[0621] The present invention provides a system in which a user inputs a specialized question, and a server generates an appropriate answer to that question. Furthermore, the server has a function to analyze the user's emotions and provide the user with a prize based on that analysis. This system aims not only to improve user satisfaction but also to increase user engagement. Its embodiments are described in detail below.

[0622] Hardware and software to be used

[0623] User's device: An electronic device such as a personal computer or smartphone. Questions are entered, displayed, and evaluated through a dedicated application or web browser.

[0624] Server: A high-performance computer system. It analyzes questions and generates answers, analyzes sentiment, and processes evaluation results.

[0625] Natural language processing engine: Software for analyzing questions. For example, the Google Cloud Natural Language API as a natural language processing API.

[0626] Database: A data source for searching and generating answers to questions. For example, the Wikipedia API as an external API.

[0627] AI engine: An artificial intelligence model for generating answers. For example, GPT-4.

[0628] Emotion engine: Software used to analyze a user's emotions. For example, the Microsoft Azure Emotion API.

[0629] Specific operation of the system

[0630] 1. Enter and submit your question:

[0631] Users enter questions into a dedicated application or input form on a web browser using a PC or smartphone. For example, they might enter "What is the radius of the Earth?"

[0632] The terminal sends this entered question to the server over the internet, specifically using an HTTP POST request.

[0633] 2. Question analysis and answer generation:

[0634] The server analyzes the received question using natural language processing technology (Google Cloud Natural Language API). This analysis helps understand the intent and content of the question.

[0635] Based on the analysis results, the server uses a database (Wikipedia API) and an AI engine (GPT-4) to generate appropriate answers. For example, it might generate an answer such as, "The Earth's radius is approximately 6,371 kilometers."

[0636] 3. Analysis of emotions:

[0637] The server analyzes the user's input using an emotion engine (Microsoft Azure Emotion API) to determine the user's emotion. For example, if the question "What is the radius of the Earth?" is entered in a calm tone, the server will recognize the user's emotion as neutral.

[0638] 4. Submitting and viewing responses:

[0639] The server sends the generated response and sentiment analysis results to the terminal.

[0640] The device displays the received response and sentiment analysis results to the user. For example, it might display "The Earth's radius is approximately 6,371 kilometers" along with the sentiment analysis results.

[0641] 5. Rating of the answer:

[0642] The user reviews the answer displayed on their device and evaluates whether the answer is appropriate for the question. For example, they might answer "Yes" or "No" to the question "Is this answer appropriate?".

[0643] The terminal then sends the user's evaluation information back to the server. The evaluation data is sent using an HTTP POST request.

[0644] 6. Provision of prizes:

[0645] The server processes the received evaluation information and sentiment analysis results. If the evaluation is "yes," the type of prize is adjusted based on the sentiment analysis results.

[0646] The server then performs the process of providing the selected prize to the user. For example, it might send a digital gift certificate via email or provide a download link.

[0647] Specific example

[0648] The user enters the question "What is the radius of the Earth?" into the device. The device sends this question to the server. The server analyzes the question and generates the answer "The radius of the Earth is approximately 6,371 kilometers." Furthermore, it uses an emotion engine to recognize the user's emotion as neutral. The server sends the answer and the emotion analysis results to the device, which displays them to the user. The user confirms the answer and rates it as "yes." The device sends the rating result to the server. Based on the rating result and the emotion analysis results, the server provides the user with a digital gift certificate.

[0649] Example of a prompt:

[0650] Example prompt for generating a system description:

[0651] "This system works by having the user input a specialized question, and the server generates the answer. The server uses natural language processing technology to analyze the question and generates the answer using a database and an AI engine. Furthermore, it uses an emotion engine to recognize the user's emotions and provides an appropriate prize based on the results. The terminal displays the generated answer and the emotion analysis results to the user, who evaluates whether the answer is appropriate. The evaluation results are sent back to the server, which then provides the user with a prize based on that information."

[0652] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0653] Step 1:

[0654] The user enters the question using their device. Specifically, the user enters a question such as "What is the radius of the Earth?" in text format into an input form on a dedicated application or web browser. This input is temporarily stored on the device as data to be sent to the server.

[0655] Input: User's question text

[0656] Output: Question data for server transmission

[0657] Step 2:

[0658] The terminal generates an HTTP POST request to send the entered question data to the server. Specifically, it includes the question text in the HTTP request body and sends the request to a specific endpoint on the server.

[0659] Input: Question data entered by the user

[0660] Output: HTTP request sent to the server

[0661] Step 3:

[0662] The server extracts question data from the received HTTP request and analyzes the question using a natural language processing engine (Google Cloud Natural Language API). This analysis helps to understand the intent and content of the question.

[0663] Input: Question data sent from the device

[0664] Output: Analyzed question data

[0665] Step 4:

[0666] The server generates answers based on the analyzed question data, using a database (Wikipedia API) and an AI engine (GPT-4). For example, it might generate an answer such as, "The Earth's radius is approximately 6,371 kilometers." A pre-trained AI model is used in this process.

[0667] Input: Analyzed question data

[0668] Output: Generated response data

[0669] Step 5:

[0670] The server analyzes the user's input text using an emotion engine (Microsoft Azure Emotion API) to determine the user's emotional state. For example, it identifies emotional states such as positive, negative, or neutral.

[0671] Input: User's question text

[0672] Output: Sentiment analysis result data

[0673] Step 6:

[0674] The server generates an HTTP response to send the generated response data and sentiment analysis results to the terminal. The response includes the response and sentiment analysis results.

[0675] Input: Generated response data and sentiment analysis result data

[0676] Output: HTTP response sent to the terminal

[0677] Step 7:

[0678] The device analyzes the HTTP response received from the server and displays the response data and sentiment analysis results to the user. Specifically, the screen displays "The Earth's radius is approximately 6,371 kilometers," along with the sentiment analysis results.

[0679] Input: HTTP response sent from the server

[0680] Output: Responses and sentiment analysis results displayed to the user

[0681] Step 8:

[0682] The user reviews the answers displayed on their device and evaluates their appropriateness using a "yes / no" format. For example, they might answer "yes" to the question, "Is this answer appropriate?"

[0683] Input: Select user rating

[0684] Output: Evaluation data entered into the terminal

[0685] Step 9:

[0686] The device generates an HTTP POST request to send the user's rating data back to the server. The request includes the user's rating information.

[0687] Input: Evaluation data entered by the user.

[0688] Output: HTTP request sent to the server

[0689] Step 10:

[0690] The server processes the prize distribution based on the received evaluation information and sentiment analysis results. If the evaluation is "yes," it selects an appropriate prize based on the sentiment analysis results and provides it to the user. For example, it may send a digital gift certificate via email or provide a download link.

[0691] Input: User rating data and sentiment analysis results data

[0692] Output: Prizes provided to the user

[0693] (Application Example 2)

[0694] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0695] Traditional question-answering systems only provide appropriate answers to user-entered questions, lacking feedback and sentiment analysis to enhance user satisfaction. This creates a risk of inaccurate answers or responses that disregard user feelings. Furthermore, they fail to offer incentives that reflect user satisfaction.

[0696] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0697] In this invention, the server includes means for receiving a question entered by a user, means for analyzing the question and generating an answer, and means for displaying the generated answer and sentiment analysis results. This enables the provision of appropriate answers to the user's questions and the provision of incentives based on sentiment analysis.

[0698] A "user" is a party who uses this system to input questions and receive answers.

[0699] A "question" refers to the content of an inquiry entered by a user, seeking specialized information or knowledge.

[0700] "Means" refers to the technical components used to perform a specific function or task.

[0701] "Analysis" is the process of analyzing an input question using techniques such as natural language processing.

[0702] "Answer" refers to the information provided by the system in response to the analyzed question.

[0703] "Emotion analysis" is the process of recognizing and analyzing a user's emotions based on their input and other factors.

[0704] "Evaluation" is the act of a user judging the appropriateness of the answer they have provided and providing feedback to the system.

[0705] "Prizes" refers to rewards or incentives provided by the system based on user ratings and sentiment analysis results.

[0706] "Emotional analysis results" refer to information about the user's emotional state obtained based on emotional analysis.

[0707] "Generation" refers to the process by which the system creates new answers or sentiment analysis results based on the analysis results.

[0708] To realize this invention, it is necessary to use a cloud server, a user terminal, and related software.

[0709] Hardware configuration

[0710] Cloud Server: Responsible for data processing and storage. Utilizes cloud services such as AWS and Google Cloud Platform.

[0711] User terminal: A device used for entering questions, viewing answers, and entering ratings. Primarily a smartphone is used.

[0712] Software Configuration

[0713] 1. Natural Language Processing (NLP) Techniques:

[0714] We use TensorFlow and spaCy to analyze questions and generate appropriate answers.

[0715] 2. Emotion analysis engine:

[0716] Use the IBM Watson Emotion Analysis API to analyze user emotions.

[0717] 3. Database:

[0718] Use Firebase Firestore to store and manage question, answer, and rating data.

[0719] Program processing

[0720] The server receives questions entered by users and analyzes them using natural language processing technology. Based on the analyzed questions, the server utilizes a database and generative AI models to generate appropriate answers.

[0721] Next, an emotion analysis engine is used to analyze the user's emotions from their input. For example, the positive or negative expressions included in the input are used to recognize the user's emotional state.

[0722] The server sends the generated response and sentiment analysis results to the user's terminal. The user's terminal displays the response and sentiment analysis results to the user. The user reviews the displayed response and evaluates whether it is appropriate. The user's evaluation is in the form of "yes / no," and the evaluation information is sent back from the user's terminal to the server.

[0723] The server processes the received evaluation information and sentiment analysis results. If the evaluation is "yes," it adjusts the type of prize based on the sentiment analysis results. For example, if the user indicates negative emotions, it provides a specific prize that promotes positive emotions.

[0724] Specific example

[0725] Let's say a user launches a smartphone app and types, "How do I use an electronic payment app?" A cloud server receives the question and analyzes it using TensorFlow or spaCy. Based on the analysis, the server generates an answer, such as, "To use an electronic payment app, open the app, select a payment option, and enter the appropriate information."

[0726] Next, the server uses the IBM Watson Emotion Analysis API to analyze the user's input and recognizes that the user is feeling slightly anxious. This response and the emotion analysis result are sent to the user's terminal, where the user reviews the displayed content on the screen and evaluates whether it is appropriate. If the evaluation is "yes" and the emotion analysis result indicates slight anxiety, a reward (for example, a discount coupon usable on the next visit) is offered to alleviate that anxiety.

[0727] Example of a prompt

[0728] "Please enter your question about electronic payment apps. We will also perform sentiment analysis based on your answers. We will evaluate the accuracy of your answers and offer prizes accordingly."

[0729] In this way, by analyzing the user's emotional state and providing corresponding responses and incentives, it becomes possible to improve user satisfaction.

[0730] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0731] Step 1:

[0732] The user launches the app on their smartphone and enters a question. The entered information is sent from the user's device to the server. The questions entered here are specialized inquiries and are sent in text format.

[0733] Step 2:

[0734] The server analyzes the received question and generates an answer. In this process, the server uses natural language processing technologies such as TensorFlow and spaCy to analyze the question and generate an appropriate response. The generated answer is stored on the server in text format.

[0735] Step 3:

[0736] The server performs sentiment analysis based on the analyzed questions and generated answers. Using the IBM Watson Emotion Analysis API, it analyzes the user's emotions from the question input. The sentiment analysis results are output as specific emotional states such as positive, negative, or neutral.

[0737] Step 4:

[0738] The server sends the generated response and sentiment analysis results to the user's terminal. The user's terminal displays the received response and sentiment analysis results on its screen. The content displayed here shows the response to the question and the user's emotional state.

[0739] Step 5:

[0740] The user reviews the displayed answers and evaluates their appropriateness. The evaluation is entered into the user's terminal in the format of "yes / no". The evaluation results are then sent back to the server from the user's terminal.

[0741] Step 6:

[0742] The server processes the provision of prizes based on the received evaluation information and sentiment analysis results. If the evaluation is "yes" and the sentiment analysis result is negative, it determines and provides a prize (e.g., a discount coupon) to promote positive emotions in the user. The details of the prize provided are recorded in the database.

[0743] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0744] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0745] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0746] [Third Embodiment]

[0747] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0748] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0749] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0750] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0751] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0752] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0753] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0754] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0755] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0756] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0757] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0758] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0759] The system of this invention involves a user inputting a specialized question, to which a server generates an answer. Furthermore, the terminal displays the generated answer to the user, who then evaluates its appropriateness. Based on this evaluation, the server provides a prize.

[0760] Enter and submit your question

[0761] The user enters a specialized question using a dedicated terminal or computer. The terminal then sends this question to the server.

[0762] Question analysis and answer generation

[0763] The server analyzes the received question using natural language processing technology. Based on the analyzed question, the server uses a database and an AI engine to generate an appropriate answer.

[0764] Submit and display of responses

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

[0766] Evaluation of the answer

[0767] The user reviews the answer displayed on their device and evaluates whether the answer is "slightly off" from the question. The evaluation is in the format of "yes / no". This evaluation information is then sent back from the device to the server.

[0768] Prize distribution

[0769] The server processes the received evaluation information. If the evaluation is "yes," the server provides the user with an appropriate reward. For example, this could involve sending a gift code to the user's email address or mailing a physical reward.

[0770] Specific example

[0771] Example 1: Question about the Earth's radius

[0772] The user enters the question "What is the radius of the Earth?" into the device. The device sends this question to the server. The server analyzes the question, generates the answer "The radius of the Earth is approximately 6,371 kilometers," and sends it to the device. The device displays the generated answer. The user reviews the answer and, if they rate it as "slightly off," sends the rating information from the device to the server. The server receives this rating and provides the user with a prize (e.g., a digital gift certificate).

[0773] This system can improve user satisfaction compared to conventional question-answering systems, and can maintain user satisfaction by offering prizes even when the answers to specialized questions are not appropriate.

[0774] The following describes the processing flow.

[0775] Step 1:

[0776] The user uses a terminal to enter a specialized question. For example, the user might enter the question, "What is the radius of the Earth?"

[0777] Step 2:

[0778] The terminal sends the user-entered questions to the server. Specifically, it sends the question data to the server as an HTTP request or WebSocket message.

[0779] Step 3:

[0780] The server receives questions sent from the terminal. It uses the receiving method to retrieve the question data.

[0781] Step 4:

[0782] The server analyzes received questions using natural language processing. This analysis utilizes techniques such as morphological analysis and contextual understanding.

[0783] Step 5:

[0784] The server generates appropriate answers based on the analyzed questions. It builds the answer data by referencing AI models and databases.

[0785] Step 6:

[0786] The server sends the generated response to the terminal. The response data is sent as an HTTP response or WebSocket message.

[0787] Step 7:

[0788] The terminal displays the responses received from the server to the user. Specifically, it displays the response data on the screen for the user to see.

[0789] Step 8:

[0790] Users review the displayed answers and rate their appropriateness. The rating is given as either "yes" or "no".

[0791] Step 9:

[0792] The user enters the evaluation result into the device. For example, if the result is "slightly off," they select "Yes."

[0793] Step 10:

[0794] The terminal sends the user's evaluation results to the server. The evaluation data is sent to the server as an HTTP request or WebSocket message.

[0795] Step 11:

[0796] The server receives the evaluation results sent from the terminal. It analyzes the evaluation data and verifies its contents.

[0797] Step 12:

[0798] If the rating is "yes," the server will execute the process of providing a prize. For example, it might send a digital gift certificate to the user's email address.

[0799] Step 13:

[0800] The server logs information about the prizes provided, thereby maintaining a history of prize distribution.

[0801] Through this process, users can receive answers to their expert questions, evaluate the appropriateness of those answers, and receive prizes if necessary.

[0802] (Example 1)

[0803] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0804] Traditional question-answering systems often fail to provide appropriate responses when automatically generated answers do not meet user expectations. This leads to decreased user satisfaction. Furthermore, the lack of a standardized method for users to evaluate the quality of the provided answers makes system improvement difficult.

[0805] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0806] In this invention, the server includes means for receiving information entered by a user, means for analyzing the information and generating a response, means for displaying the generated response, means for the user to evaluate whether the response is "slightly off" from the information, and means for providing a reward based on the evaluation. This makes it possible to provide real-time feedback on the quality of the system's response based on the user's evaluation and improve user satisfaction.

[0807] A "user" is an individual or group that uses a system to input information and receive a response.

[0808] "Information" refers to the questions and data that users input into the system.

[0809] A "server" is a computer system that receives information, analyzes it, and generates a response.

[0810] A "response" is the answer or result that a server generates based on the information it has analyzed.

[0811] "Evaluation" is the user's judgment of the appropriateness of the generated response.

[0812] "Rewards" refer to incentives or prizes provided to users based on evaluation information.

[0813] A "generative AI model" is an artificial intelligence technology used to analyze information and generate responses.

[0814] This invention provides a system in which a user inputs a specialized question, a server generates an answer to that question, a terminal displays the generated answer to the user, and the user evaluates the appropriateness of the answer, thereby providing the user with a prize.

[0815] Enter and submit your question

[0816] Users enter questions using a dedicated terminal or computer. For example, users can use a form on a web browser or a mobile application. The terminal sends this question to the server as an HTTP request. Specific hardware for this purpose includes the user's PC, tablet, smartphone, etc. For example, a user might enter "What is the radius of the Earth?"

[0817] Question analysis and answer generation

[0818] The server receives HTTP requests sent from terminals and extracts the text data of the questions. This text data is then analyzed using natural language processing techniques. Specifically, the server uses a generative AI model (e.g., GPT-4). This model allows the server to analyze the received questions and generate appropriate answers. For example, in response to the question "What is the radius of the Earth?", it would generate the answer "The radius of the Earth is approximately 6,371 kilometers." The hardware used includes a dedicated server machine, and the software includes instances of the generative AI model.

[0819] Submit and display of responses

[0820] The server sends the generated response to the device. The generated response is sent in a standard format such as JSON. The device receives this response and displays it on the user's screen. For example, the response is displayed on the application screen of the user's smartphone.

[0821] Evaluation of the answer

[0822] The user reviews the displayed answer and evaluates its accuracy. This evaluation is done in the form of "yes / no". For example, if a user sees the answer "The Earth's radius is approximately 6,371 kilometers" and feels it is "slightly off," they select "yes." This evaluation information is then sent back to the server from the device.

[0823] Prize distribution

[0824] The server analyzes the evaluation information received from the user and provides appropriate rewards. If the evaluation is "yes," it can, for example, send a digital gift code to the user's email address or arrange for a physical product to be mailed to them.

[0825] Specific example

[0826] As a concrete example, consider a case where a user inputs the question, "What is the radius of the Earth?" The device sends this question to the server, which analyzes the question and uses a generative AI model (e.g., GPT-4) to generate the answer, "The radius of the Earth is approximately 6,371 kilometers." The answer is sent to the device and displayed to the user. If the user rates the answer as "slightly off," the rating information is sent from the device to the server, and the server provides an appropriate prize.

[0827] An example of a prompt for a generative AI model might be: "The user asked the question, 'What is the radius of the Earth?' Please generate the best possible answer to this question. The answer should be accurate and based on scientific data."

[0828] This system can improve user satisfaction compared to conventional question-answering systems. Furthermore, even if the answer to a specialized question is inappropriate, user satisfaction can be maintained by offering a prize.

[0829] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0830] Step 1: The user enters and submits the question.

[0831] Users enter questions using a dedicated terminal or computer. The input interface may include a form on a web browser or a mobile application. The question entered by the user (input) is sent to the terminal as text data. For example, if a user enters "What is the radius of the Earth?", the terminal sends this question to the server as an HTTP request in JSON format (output).

[0832] Step 2: The server receives and parses the question.

[0833] The server receives an HTTP request sent from the terminal (input). The received text data is passed to a natural language processing engine (e.g., the generative AI model GPT-4) to analyze the question. The server extracts keywords and contextual information from the question. This analysis process involves data processing to understand the context of the input question and generate the optimal response (data processing). For example, the keyword "radius of the Earth" is extracted (output).

[0834] Step 3: The server generates the answer.

[0835] The server uses a generative AI model based on the analyzed question to generate an appropriate answer. In this process, it retrieves relevant information from databases and the internet, integrates it, and generates the answer (data calculation). For example, it might generate the answer, "The Earth's radius is approximately 6,371 kilometers" (output). The generated answer is prepared as a JSON response.

[0836] Step 4: The server sends the response to the terminal.

[0837] The server sends the generated response to the terminal (input). The response sent to the terminal as an HTTP response must be in a format that the user can easily understand. Specifically, it is structured data in JSON format (output).

[0838] Step 5: The device displays the answer.

[0839] The terminal displays the response received from the server to the user (input). The user can visually confirm the response using the terminal's interface (e.g., a web page or application's text view) (output). For example, the terminal might display the text, "The Earth's radius is approximately 6,371 kilometers."

[0840] Step 6: Users rate the responses.

[0841] The user reviews the displayed response and evaluates its appropriateness (input). The evaluation is in a binary format of "yes / no". Based on the user's perception of the appropriateness of the response, they select one and send it to their device (output). For example, if the user feels it is "slightly off," they would select "yes."

[0842] Step 7: The device sends the evaluation to the server.

[0843] The terminal sends the user's rating to the server (input). The rating information is then sent back to the server as an HTTP request. For example, if "Yes" is selected, that information is sent to the server in a structured format (such as JSON) (output).

[0844] Step 8: The server provides rewards based on the evaluation information.

[0845] The server analyzes the received evaluation information and provides appropriate rewards (input). If the evaluation is "yes," the server arranges to send a digital gift code to the user via email or to mail a physical item (output). For example, a digital gift certificate may be sent to the user's email address.

[0846] This process allows users to obtain answers to their questions, evaluate the appropriateness of those answers, and receive appropriate rewards.

[0847] (Application Example 1)

[0848] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0849] Conventional question-answering systems suffer from a problem where inadequate answers to specialized questions lead to decreased user satisfaction and a lack of repeat business. This is particularly problematic in physical stores, where the inability of customers to receive immediate and accurate answers to their questions degrades the customer experience. Furthermore, incorrect answers can increase user distrust and potentially damage the store's reputation. This invention aims to solve these problems and improve user satisfaction.

[0850] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0851] In this invention, the server includes means for receiving a question entered by a user, means for analyzing the question and generating an answer, means for displaying the generated answer, means for the user to evaluate whether the answer is "slightly off" from the question, means for providing a prize based on the evaluation, means for providing the user with additional prompts to correct or supplement the generated answer, and means for providing points or coupons to the user based on the evaluation result. As a result, the user's satisfaction will increase by obtaining an accurate answer, and their willingness to reuse the service will increase by earning points or coupons according to their evaluation.

[0852] A "question" is text entered by a user to ask for specific information or knowledge they are seeking to resolve.

[0853] "Analysis" is the process of understanding an input question using natural language processing technology and extracting appropriate information.

[0854] An "answer" is the information or knowledge generated in response to an analyzed question and presented to the user.

[0855] "Display" refers to the act of showing the generated response to the user's device in a format that the user can see.

[0856] "Evaluation" is the act of a user determining whether the provided answer is appropriate to the question and providing that result as feedback.

[0857] "Prizes" refer to added value such as points, coupons, and gift certificates that are provided based on the user's evaluation results.

[0858] A "prompt statement" is an additional input statement provided to improve the accuracy of the generated response.

[0859] "Points" are numerical rewards awarded based on user ratings, and can be accumulated and exchanged for coupons or products.

[0860] A "coupon" is a voucher that allows a user to receive a monetary discount or benefit based on their evaluation results.

[0861] This invention is a system in which a user inputs a specialized question, a server analyzes it, generates an answer, and provides it to the user. Furthermore, the user evaluates the appropriateness of the answer, and a prize is offered based on that evaluation. This system is particularly intended for use in physical stores.

[0862] Hardware and software to be used

[0863] Hardware: Smartphones, servers

[0864] Software: Python, OpenAI API, Requests library (for HTTP communication), Flask (server-side framework)

[0865] System flow

[0866] 1. Enter and submit your question.

[0867] Users use their smartphones to enter specialized questions about products. These questions are sent from the smartphone to the server. For example, a user might enter the question, "Is this product vegan-friendly?"

[0868] 2. Question analysis and answer generation

[0869] The server analyzes the received question using natural language processing techniques and generates an answer using a generative AI model (e.g., OpenAI API). For example, the server might generate the answer "This product is completely vegan."

[0870] 3. Submitting and displaying responses

[0871] The generated response is sent from the server to the smartphone and displayed on the user's screen. The user then reviews the response.

[0872] 4. Evaluation of the responses

[0873] Users rate whether their answer is appropriate to the question using a "yes / no" format. For example, if a user rates an answer as "yes," this rating is sent back to the server from their smartphone.

[0874] 5. Provision of prizes

[0875] The server provides rewards such as points and coupons based on user ratings. For example, if the rating is "yes," the user will be awarded "10 points."

[0876] Specific example

[0877] Suppose a user enters the question, "Is this product gluten-free?", and the server generates the answer, "This product is gluten-free." If the user rates this answer as "yes," the rating information is sent to the server, and the server provides the user with a digital coupon.

[0878] Example of a prompt

[0879] Question: Is this product vegan-friendly?

[0880] answer:

[0881] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0882] Step 1:

[0883] Input: The user enters the question on their smartphone.

[0884] Operation: An interface for entering a question appears on the smartphone. The user enters "Is this product vegan-friendly?".

[0885] Output: The entered question is captured by the application on the smartphone and prepared as a request to be sent to the server.

[0886] Step 2:

[0887] Input: User questions sent from a smartphone to the server.

[0888] Operation: A request containing a question arrives at the server. The server receives this question and parses its content using natural language processing. Specifically, it uses the OpenAI API to send a prompt for the question to an AI model and retrieves its answer.

[0889] Output: The generated response is returned to the server as "This product is completely vegan."

[0890] Step 3:

[0891] Input: The response returned by the AI ​​model.

[0892] Operation: The server prepares a request to send the generated response to the smartphone and displays the response to the user.

[0893] Output: The user's smartphone displays the response, "This product is completely vegan."

[0894] Step 4:

[0895] Input: User ratings for the displayed answers.

[0896] Operation: Users rate the displayed answers with "yes" or "no". A button is displayed on the rating interface, and the user presses the rating button.

[0897] Output: The evaluation result is sent from the smartphone to the server as "Yes".

[0898] Step 5:

[0899] Input: Evaluation results sent to the server.

[0900] Operation: The server receives the evaluation results and processes the provision of points or coupons to users who were evaluated as "yes". Specifically, it either adds points to the user's account or generates and sends a coupon to the user.

[0901] Output: The user will be awarded "10 points". Additionally, a digital coupon will be sent to the user's email address if necessary.

[0902] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0903] The system of this invention involves a user inputting a specialized question, to which a server generates an answer. Furthermore, the terminal displays the generated answer to the user, who then evaluates its appropriateness. Based on this evaluation, the server provides a prize. In addition, the invention incorporates an emotion engine that recognizes the user's emotions, and the system operates while taking the user's emotions into consideration.

[0904] Enter and submit your question

[0905] The user enters a specialized question using a dedicated terminal or computer. The terminal then sends this question to the server.

[0906] Question analysis and answer generation

[0907] The server analyzes the received question using natural language processing technology. Based on the analyzed question, the server uses a database and an AI engine to generate an appropriate answer.

[0908] Emotional analysis

[0909] The server uses an emotion engine to recognize the user's emotions based on the user's input and related data. For example, it analyzes emotions from positive or negative expressions contained in the input.

[0910] Submit and display of responses

[0911] The server sends the generated response and sentiment analysis results to the terminal. The terminal displays this response and sentiment analysis results to the user.

[0912] Evaluation of the answer

[0913] The user reviews the answer displayed on their device and evaluates whether the answer is "slightly off" from the question. The evaluation is in the format of "yes / no". This evaluation information is then sent back from the device to the server.

[0914] Prize distribution

[0915] The server processes the received evaluation information and sentiment analysis results. If the evaluation is "yes," it adjusts the type of prize based on the sentiment analysis results and provides the user with an appropriate prize. For example, if the user indicates negative emotions, it provides a specific prize that promotes positive emotions.

[0916] Specific example

[0917] Example 1: Question about the Earth's radius

[0918] The user enters the question, "What is the radius of the Earth?" into the device. The device sends this question to the server. The server analyzes the question and generates an answer, "The radius of the Earth is approximately 6,371 kilometers," and uses an emotion engine to analyze the user's emotions from the input. For example, if the question, "What is the radius of the Earth?", is entered in a calm tone, the server recognizes the user's emotions as neutral. The server sends the answer and the emotion analysis results to the device, which then displays them to the user.

[0919] The user reviews the response and, if they rate it as "slightly off," enters that rating into the device. The device sends the rating result to the server. Based on the rating result and sentiment analysis results, the server provides the user with a digital gift certificate; however, in this case, since the sentiment was neutral, a standard prize is provided.

[0920] This system can improve user satisfaction by considering both the accuracy of responses and user emotions. In particular, if a user expresses negative emotions, providing a special reward that addresses those emotions can help maintain user satisfaction.

[0921] The following describes the processing flow.

[0922] Step 1:

[0923] The user uses a terminal to enter a specialized question. For example, the user might enter the question, "What is the radius of the Earth?"

[0924] Step 2:

[0925] The terminal sends the user-entered questions to the server. Specifically, it sends the question data to the server as an HTTP request or WebSocket message.

[0926] Step 3:

[0927] The server receives questions sent from the terminal. It uses the receiving method to retrieve the question data.

[0928] Step 4:

[0929] The server analyzes received questions using natural language processing. This analysis utilizes techniques such as morphological analysis and contextual understanding.

[0930] Step 5:

[0931] The server generates appropriate answers based on the analyzed questions. It builds the answer data by referencing AI models and databases.

[0932] Step 6:

[0933] In addition to the generated responses, the server uses an emotion engine to analyze the user's emotions. It detects emotions such as positive, negative, or neutral based on the user's questions and context.

[0934] Step 7:

[0935] The server sends the generated response and sentiment analysis results to the terminal. The response data and sentiment data are sent as HTTP responses or WebSocket messages.

[0936] Step 8:

[0937] The terminal displays the user the responses and sentiment analysis results received from the server. Specifically, it displays the response data on the screen, as well as the sentiment analysis results.

[0938] Step 9:

[0939] Users review the displayed answers and rate their appropriateness. The rating is given as either "yes" or "no".

[0940] Step 10:

[0941] The user enters the evaluation result into the device. For example, if the result is "slightly off," they select "Yes."

[0942] Step 11:

[0943] The terminal sends the user's evaluation results to the server. The evaluation data is sent to the server as an HTTP request or WebSocket message.

[0944] Step 12:

[0945] The server receives the evaluation results sent from the terminal. It analyzes the evaluation data and verifies its contents.

[0946] Step 13:

[0947] If the evaluation is "yes," the server executes the process of providing a prize. At this time, the type of prize is adjusted based on the sentiment analysis results. For example, if the user has expressed negative emotions, a specific prize that promotes positive emotions will be provided.

[0948] Step 14:

[0949] The server logs information about the prizes provided, thereby maintaining a history of prize distribution. Through this process, users can receive answers to their expert questions, evaluate the appropriateness of those answers, and receive prizes if necessary.

[0950] As a concrete example, let's consider the case where a user enters the question, "What is the radius of the Earth?"

[0951] 1. The user enters "What is the radius of the Earth?" into the device.

[0952] 2. The device sends this question data to the server.

[0953] 3. The server receives the question data and analyzes it using natural language processing.

[0954] 4. The server generates the answer, "The Earth's radius is approximately 6,371 kilometers."

[0955] 5. The server uses an emotion engine and recognizes that the user's emotions are neutral because the input is in a calm tone.

[0956] 6. The server sends the response data and sentiment analysis results to the terminal.

[0957] 7. The device displays these to the user.

[0958] 8. The user reviews the answer and rates it as "slightly off."

[0959] 9. The user enters "Yes" as the evaluation result on the device.

[0960] 10. The device sends this evaluation result to the server.

[0961] 11. The server receives the evaluation result and, since it is "yes," decides to provide the prize.

[0962] 12. The server provides the user with an appropriate prize (e.g., a digital gift certificate) based on the user's rating and sentiment analysis results.

[0963] 13. The server logs information about the prizes provided.

[0964] In this way, we can create a system that evaluates the accuracy of the user's answers to their questions and provides prizes while also taking their emotions into consideration.

[0965] (Example 2)

[0966] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0967] Traditional question-answering systems struggle to provide accurate answers to user-generated questions and fail to consider user emotions and feedback, thus failing to improve user satisfaction. Furthermore, they lacked mechanisms to evaluate user satisfaction with answers and provide appropriate rewards based on that, making it difficult to maintain user engagement. New systems and methods are needed to address these challenges.

[0968] The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving a question entered by the user, means for analyzing the question and generating an answer, means for displaying the generated answer and sentiment analysis results, means for the user to evaluate whether the answer is "slightly off" from the question, and means for providing a prize based on the evaluation and sentiment analysis results. This not only provides accurate answers to the user's questions but also enables responses that take into account the user's emotions and feedback, thereby improving user satisfaction. Furthermore, by providing appropriate prizes based on the evaluation results, it is possible to increase user engagement.

[0969] A "user" refers to a person who uses the system to input questions and receive answers.

[0970] A "question" refers to information or a question that a user enters into a system.

[0971] "Means of receiving" refers to devices and software that send user questions and evaluations to a server and receive that information on the server.

[0972] "Means of analysis" refers to devices and software that use natural language processing techniques to understand received questions and generate appropriate answers.

[0973] "Answer" refers to the information or response text that the server generates in response to an analyzed question.

[0974] "Means of display" refers to devices or software that visually present generated responses and sentiment analysis results to the user.

[0975] "Means of evaluation" refers to devices or software that allow users to determine whether the displayed answers are appropriate and to input the results.

[0976] "Means of providing prizes" refers to devices or software that provide rewards or benefits to users based on user ratings and sentiment analysis results.

[0977] "Emotion analysis results" refer to data about the emotional state identified by the server based on the user's questions and feedback, after the server has analyzed the user's emotions.

[0978] "Natural language processing" refers to the technology that enables computers to understand, interpret, and manipulate human language.

[0979] A "server" refers to a computer system that receives and analyzes user inquiries, provides answers and sentiment analysis, and sends back the results.

[0980] A "terminal" refers to a device or equipment used by a user to input questions, display responses and sentiment analysis results from a server, and submit evaluations.

[0981] The present invention provides a system in which a user inputs a specialized question, and a server generates an appropriate answer to that question. Furthermore, the server has a function to analyze the user's emotions and provide the user with a prize based on that analysis. This system aims not only to improve user satisfaction but also to increase user engagement. Its embodiments are described in detail below.

[0982] Hardware and software to be used

[0983] User's device: An electronic device such as a personal computer or smartphone. Questions are entered, displayed, and evaluated through a dedicated application or web browser.

[0984] Server: A high-performance computer system. It analyzes questions and generates answers, analyzes sentiment, and processes evaluation results.

[0985] Natural language processing engine: Software for analyzing questions. For example, the Google Cloud Natural Language API as a natural language processing API.

[0986] Database: A data source for searching and generating answers to questions. For example, the Wikipedia API as an external API.

[0987] AI engine: An artificial intelligence model for generating answers. For example, GPT-4.

[0988] Emotion engine: Software used to analyze a user's emotions. For example, the Microsoft Azure Emotion API.

[0989] Specific operation of the system

[0990] 1. Enter and submit your question:

[0991] Users enter questions into a dedicated application or input form on a web browser using a PC or smartphone. For example, they might enter "What is the radius of the Earth?"

[0992] The terminal sends this entered question to the server over the internet, specifically using an HTTP POST request.

[0993] 2. Question analysis and answer generation:

[0994] The server analyzes the received question using natural language processing technology (Google Cloud Natural Language API). This analysis helps understand the intent and content of the question.

[0995] Based on the analysis results, the server uses a database (Wikipedia API) and an AI engine (GPT-4) to generate appropriate answers. For example, it might generate an answer such as, "The Earth's radius is approximately 6,371 kilometers."

[0996] 3. Analysis of emotions:

[0997] The server analyzes the user's input using an emotion engine (Microsoft Azure Emotion API) to determine the user's emotion. For example, if the question "What is the radius of the Earth?" is entered in a calm tone, the server will recognize the user's emotion as neutral.

[0998] 4. Submitting and viewing responses:

[0999] The server sends the generated response and sentiment analysis results to the terminal.

[1000] The device displays the received response and sentiment analysis results to the user. For example, it might display "The Earth's radius is approximately 6,371 kilometers" along with the sentiment analysis results.

[1001] 5. Rating of the answer:

[1002] The user reviews the answer displayed on their device and evaluates whether the answer is appropriate for the question. For example, they might answer "Yes" or "No" to the question "Is this answer appropriate?".

[1003] The terminal then sends the user's evaluation information back to the server. The evaluation data is sent using an HTTP POST request.

[1004] 6. Provision of prizes:

[1005] The server processes the received evaluation information and sentiment analysis results. If the evaluation is "yes," the type of prize is adjusted based on the sentiment analysis results.

[1006] The server then performs the process of providing the selected prize to the user. For example, it might send a digital gift certificate via email or provide a download link.

[1007] Specific example

[1008] The user enters the question "What is the radius of the Earth?" into the device. The device sends this question to the server. The server analyzes the question and generates the answer "The radius of the Earth is approximately 6,371 kilometers." Furthermore, it uses an emotion engine to recognize the user's emotion as neutral. The server sends the answer and the emotion analysis results to the device, which displays them to the user. The user confirms the answer and rates it as "yes." The device sends the rating result to the server. Based on the rating result and the emotion analysis results, the server provides the user with a digital gift certificate.

[1009] Example of a prompt:

[1010] Example prompt for generating a system description:

[1011] "This system works by having the user input a specialized question, and the server generates the answer. The server uses natural language processing technology to analyze the question and generates the answer using a database and an AI engine. Furthermore, it uses an emotion engine to recognize the user's emotions and provides an appropriate prize based on the results. The terminal displays the generated answer and the emotion analysis results to the user, who evaluates whether the answer is appropriate. The evaluation results are sent back to the server, which then provides the user with a prize based on that information."

[1012] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1013] Step 1:

[1014] The user enters the question using their device. Specifically, the user enters a question such as "What is the radius of the Earth?" in text format into an input form on a dedicated application or web browser. This input is temporarily stored on the device as data to be sent to the server.

[1015] Input: User's question text

[1016] Output: Question data for server transmission

[1017] Step 2:

[1018] The terminal generates an HTTP POST request to send the entered question data to the server. Specifically, it includes the question text in the HTTP request body and sends the request to a specific endpoint on the server.

[1019] Input: Question data entered by the user

[1020] Output: HTTP request sent to the server

[1021] Step 3:

[1022] The server extracts question data from the received HTTP request and analyzes the question using a natural language processing engine (Google Cloud Natural Language API). This analysis helps to understand the intent and content of the question.

[1023] Input: Question data sent from the device

[1024] Output: Analyzed question data

[1025] Step 4:

[1026] The server generates answers based on the analyzed question data, using a database (Wikipedia API) and an AI engine (GPT-4). For example, it might generate an answer such as, "The Earth's radius is approximately 6,371 kilometers." A pre-trained AI model is used in this process.

[1027] Input: Analyzed question data

[1028] Output: Generated response data

[1029] Step 5:

[1030] The server analyzes the user's input text using an emotion engine (Microsoft Azure Emotion API) to determine the user's emotional state. For example, it identifies emotional states such as positive, negative, or neutral.

[1031] Input: User's question text

[1032] Output: Sentiment analysis result data

[1033] Step 6:

[1034] The server generates an HTTP response to send the generated response data and sentiment analysis results to the terminal. The response includes the response and sentiment analysis results.

[1035] Input: Generated response data and sentiment analysis result data

[1036] Output: HTTP response sent to the terminal

[1037] Step 7:

[1038] The device analyzes the HTTP response received from the server and displays the response data and sentiment analysis results to the user. Specifically, the screen displays "The Earth's radius is approximately 6,371 kilometers," along with the sentiment analysis results.

[1039] Input: HTTP response sent from the server

[1040] Output: Responses and sentiment analysis results displayed to the user

[1041] Step 8:

[1042] The user reviews the answers displayed on their device and evaluates their appropriateness using a "yes / no" format. For example, they might answer "yes" to the question, "Is this answer appropriate?"

[1043] Input: Select user rating

[1044] Output: Evaluation data entered into the terminal

[1045] Step 9:

[1046] The device generates an HTTP POST request to send the user's rating data back to the server. The request includes the user's rating information.

[1047] Input: Evaluation data entered by the user.

[1048] Output: HTTP request sent to the server

[1049] Step 10:

[1050] The server processes the prize distribution based on the received evaluation information and sentiment analysis results. If the evaluation is "yes," it selects an appropriate prize based on the sentiment analysis results and provides it to the user. For example, it may send a digital gift certificate via email or provide a download link.

[1051] Input: User rating data and sentiment analysis results data

[1052] Output: Prizes provided to the user

[1053] (Application Example 2)

[1054] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1055] Traditional question-answering systems only provide appropriate answers to user-entered questions, lacking feedback and sentiment analysis to enhance user satisfaction. This creates a risk of inaccurate answers or responses that disregard user feelings. Furthermore, they fail to offer incentives that reflect user satisfaction.

[1056] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1057] In this invention, the server includes means for receiving a question entered by a user, means for analyzing the question and generating an answer, and means for displaying the generated answer and sentiment analysis results. This enables the provision of appropriate answers to the user's questions and the provision of incentives based on sentiment analysis.

[1058] A "user" is a party who uses this system to input questions and receive answers.

[1059] A "question" refers to the content of an inquiry entered by a user, seeking specialized information or knowledge.

[1060] "Means" refers to the technical components used to perform a specific function or task.

[1061] "Analysis" is the process of analyzing an input question using techniques such as natural language processing.

[1062] "Answer" refers to the information provided by the system in response to the analyzed question.

[1063] "Emotion analysis" is the process of recognizing and analyzing a user's emotions based on their input and other factors.

[1064] "Evaluation" is the act of a user judging the appropriateness of the answer they have provided and providing feedback to the system.

[1065] "Prizes" refers to rewards or incentives provided by the system based on user ratings and sentiment analysis results.

[1066] "Emotional analysis results" refer to information about the user's emotional state obtained based on emotional analysis.

[1067] "Generation" refers to the process by which the system creates new answers or sentiment analysis results based on the analysis results.

[1068] To realize this invention, it is necessary to use a cloud server, a user terminal, and related software.

[1069] Hardware configuration

[1070] Cloud Server: Responsible for data processing and storage. Utilizes cloud services such as AWS and Google Cloud Platform.

[1071] User terminal: A device used for entering questions, viewing answers, and entering ratings. Primarily a smartphone is used.

[1072] Software Configuration

[1073] 1. Natural Language Processing (NLP) Techniques:

[1074] We use TensorFlow and spaCy to analyze questions and generate appropriate answers.

[1075] 2. Emotion analysis engine:

[1076] Use the IBM Watson Emotion Analysis API to analyze user emotions.

[1077] 3. Database:

[1078] Use Firebase Firestore to store and manage question, answer, and rating data.

[1079] Program processing

[1080] The server receives questions entered by users and analyzes them using natural language processing technology. Based on the analyzed questions, the server utilizes a database and generative AI models to generate appropriate answers.

[1081] Next, an emotion analysis engine is used to analyze the user's emotions from their input. For example, the positive or negative expressions included in the input are used to recognize the user's emotional state.

[1082] The server sends the generated response and sentiment analysis results to the user's terminal. The user's terminal displays the response and sentiment analysis results to the user. The user reviews the displayed response and evaluates whether it is appropriate. The user's evaluation is in the form of "yes / no," and the evaluation information is sent back from the user's terminal to the server.

[1083] The server processes the received evaluation information and sentiment analysis results. If the evaluation is "yes," it adjusts the type of prize based on the sentiment analysis results. For example, if the user indicates negative emotions, it provides a specific prize that promotes positive emotions.

[1084] Specific example

[1085] Let's say a user launches a smartphone app and types, "How do I use an electronic payment app?" A cloud server receives the question and analyzes it using TensorFlow or spaCy. Based on the analysis, the server generates an answer, such as, "To use an electronic payment app, open the app, select a payment option, and enter the appropriate information."

[1086] Next, the server uses the IBM Watson Emotion Analysis API to analyze the user's input and recognizes that the user is feeling slightly anxious. This response and the emotion analysis result are sent to the user's terminal, where the user reviews the displayed content on the screen and evaluates whether it is appropriate. If the evaluation is "yes" and the emotion analysis result indicates slight anxiety, a reward (for example, a discount coupon usable on the next visit) is offered to alleviate that anxiety.

[1087] Example of a prompt

[1088] "Please enter your question about electronic payment apps. We will also perform sentiment analysis based on your answers. We will evaluate the accuracy of your answers and offer prizes accordingly."

[1089] In this way, by analyzing the user's emotional state and providing corresponding responses and incentives, it becomes possible to improve user satisfaction.

[1090] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1091] Step 1:

[1092] The user launches the app on their smartphone and enters a question. The entered information is sent from the user's device to the server. The questions entered here are specialized inquiries and are sent in text format.

[1093] Step 2:

[1094] The server analyzes the received question and generates an answer. In this process, the server uses natural language processing technologies such as TensorFlow and spaCy to analyze the question and generate an appropriate response. The generated answer is stored on the server in text format.

[1095] Step 3:

[1096] The server performs sentiment analysis based on the analyzed questions and generated answers. Using the IBM Watson Emotion Analysis API, it analyzes the user's emotions from the question input. The sentiment analysis results are output as specific emotional states such as positive, negative, or neutral.

[1097] Step 4:

[1098] The server sends the generated response and sentiment analysis results to the user's terminal. The user's terminal displays the received response and sentiment analysis results on its screen. The content displayed here shows the response to the question and the user's emotional state.

[1099] Step 5:

[1100] The user reviews the displayed answers and evaluates their appropriateness. The evaluation is entered into the user's terminal in the format of "yes / no". The evaluation results are then sent back to the server from the user's terminal.

[1101] Step 6:

[1102] The server processes the provision of prizes based on the received evaluation information and sentiment analysis results. If the evaluation is "yes" and the sentiment analysis result is negative, it determines and provides a prize (e.g., a discount coupon) to promote positive emotions in the user. The details of the prize provided are recorded in the database.

[1103] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1104] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1105] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[1106] [Fourth Embodiment]

[1107] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1108] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1109] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1110] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[1111] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1113] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1114] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[1115] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1116] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1117] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1118] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1119] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1120] The system of this invention involves a user inputting a specialized question, to which a server generates an answer. Furthermore, the terminal displays the generated answer to the user, who then evaluates its appropriateness. Based on this evaluation, the server provides a prize.

[1121] Enter and submit your question

[1122] The user enters a specialized question using a dedicated terminal or computer. The terminal then sends this question to the server.

[1123] Question analysis and answer generation

[1124] The server analyzes the received question using natural language processing technology. Based on the analyzed question, the server uses a database and an AI engine to generate an appropriate answer.

[1125] Submit and display of responses

[1126] The server sends the generated response to the terminal. The terminal displays this response to the user.

[1127] Evaluation of the answer

[1128] The user reviews the answer displayed on their device and evaluates whether the answer is "slightly off" from the question. The evaluation is in the format of "yes / no". This evaluation information is then sent back from the device to the server.

[1129] Prize distribution

[1130] The server processes the received evaluation information. If the evaluation is "yes," the server provides the user with an appropriate reward. For example, this could involve sending a gift code to the user's email address or mailing a physical reward.

[1131] Specific example

[1132] Example 1: Question about the Earth's radius

[1133] The user enters the question "What is the radius of the Earth?" into the device. The device sends this question to the server. The server analyzes the question, generates the answer "The radius of the Earth is approximately 6,371 kilometers," and sends it to the device. The device displays the generated answer. The user reviews the answer and, if they rate it as "slightly off," sends the rating information from the device to the server. The server receives this rating and provides the user with a prize (e.g., a digital gift certificate).

[1134] This system can improve user satisfaction compared to conventional question-answering systems, and can maintain user satisfaction by offering prizes even when the answers to specialized questions are not appropriate.

[1135] The following describes the processing flow.

[1136] Step 1:

[1137] The user uses a terminal to enter a specialized question. For example, the user might enter the question, "What is the radius of the Earth?"

[1138] Step 2:

[1139] The terminal sends the user-entered questions to the server. Specifically, it sends the question data to the server as an HTTP request or WebSocket message.

[1140] Step 3:

[1141] The server receives questions sent from the terminal. It uses the receiving method to retrieve the question data.

[1142] Step 4:

[1143] The server analyzes received questions using natural language processing. This analysis utilizes techniques such as morphological analysis and contextual understanding.

[1144] Step 5:

[1145] The server generates appropriate answers based on the analyzed questions. It builds the answer data by referencing AI models and databases.

[1146] Step 6:

[1147] The server sends the generated response to the terminal. The response data is sent as an HTTP response or WebSocket message.

[1148] Step 7:

[1149] The terminal displays the responses received from the server to the user. Specifically, it displays the response data on the screen for the user to see.

[1150] Step 8:

[1151] Users review the displayed answers and rate their appropriateness. The rating is given as either "yes" or "no".

[1152] Step 9:

[1153] The user enters the evaluation result into the device. For example, if the result is "slightly off," they select "Yes."

[1154] Step 10:

[1155] The terminal sends the user's evaluation results to the server. The evaluation data is sent to the server as an HTTP request or WebSocket message.

[1156] Step 11:

[1157] The server receives the evaluation results sent from the terminal. It analyzes the evaluation data and verifies its contents.

[1158] Step 12:

[1159] If the rating is "yes," the server will execute the process of providing a prize. For example, it might send a digital gift certificate to the user's email address.

[1160] Step 13:

[1161] The server logs information about the prizes provided, thereby maintaining a history of prize distribution.

[1162] Through this process, users can receive answers to their expert questions, evaluate the appropriateness of those answers, and receive prizes if necessary.

[1163] (Example 1)

[1164] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1165] Traditional question-answering systems often fail to provide appropriate responses when automatically generated answers do not meet user expectations. This leads to decreased user satisfaction. Furthermore, the lack of a standardized method for users to evaluate the quality of the provided answers makes system improvement difficult.

[1166] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1167] In this invention, the server includes means for receiving information entered by a user, means for analyzing the information and generating a response, means for displaying the generated response, means for the user to evaluate whether the response is "slightly off" from the information, and means for providing a reward based on the evaluation. This makes it possible to provide real-time feedback on the quality of the system's response based on the user's evaluation and improve user satisfaction.

[1168] A "user" is an individual or group that uses a system to input information and receive a response.

[1169] "Information" refers to the questions and data that users input into the system.

[1170] A "server" is a computer system that receives information, analyzes it, and generates a response.

[1171] A "response" is the answer or result that a server generates based on the information it has analyzed.

[1172] "Evaluation" is the user's judgment of the appropriateness of the generated response.

[1173] "Rewards" refer to incentives or prizes provided to users based on evaluation information.

[1174] A "generative AI model" is an artificial intelligence technology used to analyze information and generate responses.

[1175] This invention provides a system in which a user inputs a specialized question, a server generates an answer to that question, a terminal displays the generated answer to the user, and the user evaluates the appropriateness of the answer, thereby providing the user with a prize.

[1176] Enter and submit your question

[1177] Users enter questions using a dedicated terminal or computer. For example, users can use a form on a web browser or a mobile application. The terminal sends this question to the server as an HTTP request. Specific hardware for this purpose includes the user's PC, tablet, smartphone, etc. For example, a user might enter "What is the radius of the Earth?"

[1178] Question analysis and answer generation

[1179] The server receives HTTP requests sent from terminals and extracts the text data of the questions. This text data is then analyzed using natural language processing techniques. Specifically, the server uses a generative AI model (e.g., GPT-4). This model allows the server to analyze the received questions and generate appropriate answers. For example, in response to the question "What is the radius of the Earth?", it would generate the answer "The radius of the Earth is approximately 6,371 kilometers." The hardware used includes a dedicated server machine, and the software includes instances of the generative AI model.

[1180] Submit and display of responses

[1181] The server sends the generated response to the device. The generated response is sent in a standard format such as JSON. The device receives this response and displays it on the user's screen. For example, the response is displayed on the application screen of the user's smartphone.

[1182] Evaluation of the answer

[1183] The user reviews the displayed answer and evaluates its accuracy. This evaluation is done in the form of "yes / no". For example, if a user sees the answer "The Earth's radius is approximately 6,371 kilometers" and feels it is "slightly off," they select "yes." This evaluation information is then sent back to the server from the device.

[1184] Prize distribution

[1185] The server analyzes the evaluation information received from the user and provides appropriate rewards. If the evaluation is "yes," it can, for example, send a digital gift code to the user's email address or arrange for a physical product to be mailed to them.

[1186] Specific example

[1187] As a concrete example, consider a case where a user inputs the question, "What is the radius of the Earth?" The device sends this question to the server, which analyzes the question and uses a generative AI model (e.g., GPT-4) to generate the answer, "The radius of the Earth is approximately 6,371 kilometers." The answer is sent to the device and displayed to the user. If the user rates the answer as "slightly off," the rating information is sent from the device to the server, and the server provides an appropriate prize.

[1188] An example of a prompt for a generative AI model might be: "The user asked the question, 'What is the radius of the Earth?' Please generate the best possible answer to this question. The answer should be accurate and based on scientific data."

[1189] This system can improve user satisfaction compared to conventional question-answering systems. Furthermore, even if the answer to a specialized question is inappropriate, user satisfaction can be maintained by offering a prize.

[1190] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1191] Step 1: The user enters and submits the question.

[1192] Users enter questions using a dedicated terminal or computer. The input interface may include a form on a web browser or a mobile application. The question entered by the user (input) is sent to the terminal as text data. For example, if a user enters "What is the radius of the Earth?", the terminal sends this question to the server as an HTTP request in JSON format (output).

[1193] Step 2: The server receives and parses the question.

[1194] The server receives an HTTP request sent from the terminal (input). The received text data is passed to a natural language processing engine (e.g., the generative AI model GPT-4) to analyze the question. The server extracts keywords and contextual information from the question. This analysis process involves data processing to understand the context of the input question and generate the optimal response (data processing). For example, the keyword "radius of the Earth" is extracted (output).

[1195] Step 3: The server generates the answer.

[1196] The server uses a generative AI model based on the analyzed question to generate an appropriate answer. In this process, it retrieves relevant information from databases and the internet, integrates it, and generates the answer (data calculation). For example, it might generate the answer, "The Earth's radius is approximately 6,371 kilometers" (output). The generated answer is prepared as a JSON response.

[1197] Step 4: The server sends the response to the terminal.

[1198] The server sends the generated response to the terminal (input). The response sent to the terminal as an HTTP response must be in a format that the user can easily understand. Specifically, it is structured data in JSON format (output).

[1199] Step 5: The device displays the answer.

[1200] The terminal displays the response received from the server to the user (input). The user can visually confirm the response using the terminal's interface (e.g., a web page or application's text view) (output). For example, the terminal might display the text, "The Earth's radius is approximately 6,371 kilometers."

[1201] Step 6: Users rate the responses.

[1202] The user reviews the displayed response and evaluates its appropriateness (input). The evaluation is in a binary format of "yes / no". Based on the user's perception of the appropriateness of the response, they select one and send it to their device (output). For example, if the user feels it is "slightly off," they would select "yes."

[1203] Step 7: The device sends the evaluation to the server.

[1204] The terminal sends the user's rating to the server (input). The rating information is then sent back to the server as an HTTP request. For example, if "Yes" is selected, that information is sent to the server in a structured format (such as JSON) (output).

[1205] Step 8: The server provides rewards based on the evaluation information.

[1206] The server analyzes the received evaluation information and provides appropriate rewards (input). If the evaluation is "yes," the server arranges to send a digital gift code to the user via email or to mail a physical item (output). For example, a digital gift certificate may be sent to the user's email address.

[1207] This process allows users to obtain answers to their questions, evaluate the appropriateness of those answers, and receive appropriate rewards.

[1208] (Application Example 1)

[1209] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1210] Conventional question-answering systems suffer from a problem where inadequate answers to specialized questions lead to decreased user satisfaction and a lack of repeat business. This is particularly problematic in physical stores, where the inability of customers to receive immediate and accurate answers to their questions degrades the customer experience. Furthermore, incorrect answers can increase user distrust and potentially damage the store's reputation. This invention aims to solve these problems and improve user satisfaction.

[1211] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1212] In this invention, the server includes means for receiving a question entered by a user, means for analyzing the question and generating an answer, means for displaying the generated answer, means for the user to evaluate whether the answer is "slightly off" from the question, means for providing a prize based on the evaluation, means for providing the user with additional prompts to correct or supplement the generated answer, and means for providing points or coupons to the user based on the evaluation result. As a result, the user's satisfaction will increase by obtaining an accurate answer, and their willingness to reuse the service will increase by earning points or coupons according to their evaluation.

[1213] A "question" is text entered by a user to ask for specific information or knowledge they are seeking to resolve.

[1214] "Analysis" is the process of understanding an input question using natural language processing technology and extracting appropriate information.

[1215] An "answer" is the information or knowledge generated in response to an analyzed question and presented to the user.

[1216] "Display" refers to the act of showing the generated response to the user's device in a format that the user can see.

[1217] "Evaluation" is the act of a user determining whether the provided answer is appropriate to the question and providing that result as feedback.

[1218] "Prizes" refer to added value such as points, coupons, and gift certificates that are provided based on the user's evaluation results.

[1219] A "prompt statement" is an additional input statement provided to improve the accuracy of the generated response.

[1220] "Points" are numerical rewards awarded based on user ratings, and can be accumulated and exchanged for coupons or products.

[1221] A "coupon" is a voucher that allows a user to receive a monetary discount or benefit based on their evaluation results.

[1222] This invention is a system in which a user inputs a specialized question, a server analyzes it, generates an answer, and provides it to the user. Furthermore, the user evaluates the appropriateness of the answer, and a prize is offered based on that evaluation. This system is particularly intended for use in physical stores.

[1223] Hardware and software to be used

[1224] Hardware: Smartphones, servers

[1225] Software: Python, OpenAI API, Requests library (for HTTP communication), Flask (server-side framework)

[1226] System flow

[1227] 1. Enter and submit your question.

[1228] Users use their smartphones to enter specialized questions about products. These questions are sent from the smartphone to the server. For example, a user might enter the question, "Is this product vegan-friendly?"

[1229] 2. Question analysis and answer generation

[1230] The server analyzes the received question using natural language processing techniques and generates an answer using a generative AI model (e.g., OpenAI API). For example, the server might generate the answer "This product is completely vegan."

[1231] 3. Submitting and displaying responses

[1232] The generated response is sent from the server to the smartphone and displayed on the user's screen. The user then reviews the response.

[1233] 4. Evaluation of the responses

[1234] Users rate whether their answer is appropriate to the question using a "yes / no" format. For example, if a user rates an answer as "yes," this rating is sent back to the server from their smartphone.

[1235] 5. Provision of prizes

[1236] The server provides rewards such as points and coupons based on user ratings. For example, if the rating is "yes," the user will be awarded "10 points."

[1237] Specific example

[1238] Suppose a user enters the question, "Is this product gluten-free?", and the server generates the answer, "This product is gluten-free." If the user rates this answer as "yes," the rating information is sent to the server, and the server provides the user with a digital coupon.

[1239] Example of a prompt

[1240] Question: Is this product vegan-friendly?

[1241] answer:

[1242] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1243] Step 1:

[1244] Input: The user enters the question on their smartphone.

[1245] Operation: An interface for entering a question appears on the smartphone. The user enters "Is this product vegan-friendly?".

[1246] Output: The entered question is captured by the application on the smartphone and prepared as a request to be sent to the server.

[1247] Step 2:

[1248] Input: User questions sent from a smartphone to the server.

[1249] Operation: A request containing a question arrives at the server. The server receives this question and parses its content using natural language processing. Specifically, it uses the OpenAI API to send a prompt for the question to an AI model and retrieves its answer.

[1250] Output: The generated response is returned to the server as "This product is completely vegan."

[1251] Step 3:

[1252] Input: The response returned by the AI ​​model.

[1253] Operation: The server prepares a request to send the generated response to the smartphone and displays the response to the user.

[1254] Output: The user's smartphone displays the response, "This product is completely vegan."

[1255] Step 4:

[1256] Input: User ratings for the displayed answers.

[1257] Operation: Users rate the displayed answers with "yes" or "no". A button is displayed on the rating interface, and the user presses the rating button.

[1258] Output: The evaluation result is sent from the smartphone to the server as "Yes".

[1259] Step 5:

[1260] Input: Evaluation results sent to the server.

[1261] Operation: The server receives the evaluation results and processes the provision of points or coupons to users who were evaluated as "yes". Specifically, it either adds points to the user's account or generates and sends a coupon to the user.

[1262] Output: The user will be awarded "10 points". Additionally, a digital coupon will be sent to the user's email address if necessary.

[1263] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1264] The system of this invention involves a user inputting a specialized question, to which a server generates an answer. Furthermore, the terminal displays the generated answer to the user, who then evaluates its appropriateness. Based on this evaluation, the server provides a prize. In addition, the invention incorporates an emotion engine that recognizes the user's emotions, and the system operates while taking the user's emotions into consideration.

[1265] Enter and submit your question

[1266] The user enters a specialized question using a dedicated terminal or computer. The terminal then sends this question to the server.

[1267] Question analysis and answer generation

[1268] The server analyzes the received question using natural language processing technology. Based on the analyzed question, the server uses a database and an AI engine to generate an appropriate answer.

[1269] Emotional analysis

[1270] The server uses an emotion engine to recognize the user's emotions based on the user's input and related data. For example, it analyzes emotions from positive or negative expressions contained in the input.

[1271] Submit and display of responses

[1272] The server sends the generated response and sentiment analysis results to the terminal. The terminal displays this response and sentiment analysis results to the user.

[1273] Evaluation of the answer

[1274] The user reviews the answer displayed on their device and evaluates whether the answer is "slightly off" from the question. The evaluation is in the format of "yes / no". This evaluation information is then sent back from the device to the server.

[1275] Prize distribution

[1276] The server processes the received evaluation information and sentiment analysis results. If the evaluation is "yes," it adjusts the type of prize based on the sentiment analysis results and provides the user with an appropriate prize. For example, if the user indicates negative emotions, it provides a specific prize that promotes positive emotions.

[1277] Specific example

[1278] Example 1: Question about the Earth's radius

[1279] The user enters the question, "What is the radius of the Earth?" into the device. The device sends this question to the server. The server analyzes the question and generates an answer, "The radius of the Earth is approximately 6,371 kilometers," and uses an emotion engine to analyze the user's emotions from the input. For example, if the question, "What is the radius of the Earth?", is entered in a calm tone, the server recognizes the user's emotions as neutral. The server sends the answer and the emotion analysis results to the device, which then displays them to the user.

[1280] The user reviews the response and, if they rate it as "slightly off," enters that rating into the device. The device sends the rating result to the server. Based on the rating result and sentiment analysis results, the server provides the user with a digital gift certificate; however, in this case, since the sentiment was neutral, a standard prize is provided.

[1281] This system can improve user satisfaction by considering both the accuracy of responses and user emotions. In particular, if a user expresses negative emotions, providing a special reward that addresses those emotions can help maintain user satisfaction.

[1282] The following describes the processing flow.

[1283] Step 1:

[1284] The user uses a terminal to enter a specialized question. For example, the user might enter the question, "What is the radius of the Earth?"

[1285] Step 2:

[1286] The terminal sends the user-entered questions to the server. Specifically, it sends the question data to the server as an HTTP request or WebSocket message.

[1287] Step 3:

[1288] The server receives questions sent from the terminal. It uses the receiving method to retrieve the question data.

[1289] Step 4:

[1290] The server analyzes received questions using natural language processing. This analysis utilizes techniques such as morphological analysis and contextual understanding.

[1291] Step 5:

[1292] The server generates appropriate answers based on the analyzed questions. It builds the answer data by referencing AI models and databases.

[1293] Step 6:

[1294] In addition to the generated responses, the server uses an emotion engine to analyze the user's emotions. It detects emotions such as positive, negative, or neutral based on the user's questions and context.

[1295] Step 7:

[1296] The server sends the generated response and sentiment analysis results to the terminal. The response data and sentiment data are sent as HTTP responses or WebSocket messages.

[1297] Step 8:

[1298] The terminal displays the user the responses and sentiment analysis results received from the server. Specifically, it displays the response data on the screen, as well as the sentiment analysis results.

[1299] Step 9:

[1300] Users review the displayed answers and rate their appropriateness. The rating is given as either "yes" or "no".

[1301] Step 10:

[1302] The user enters the evaluation result into the device. For example, if the result is "slightly off," they select "Yes."

[1303] Step 11:

[1304] The terminal sends the user's evaluation results to the server. The evaluation data is sent to the server as an HTTP request or WebSocket message.

[1305] Step 12:

[1306] The server receives the evaluation results sent from the terminal. It analyzes the evaluation data and verifies its contents.

[1307] Step 13:

[1308] If the evaluation is "yes," the server executes the process of providing a prize. At this time, the type of prize is adjusted based on the sentiment analysis results. For example, if the user has expressed negative emotions, a specific prize that promotes positive emotions will be provided.

[1309] Step 14:

[1310] The server logs information about the prizes provided, thereby maintaining a history of prize distribution. Through this process, users can receive answers to their expert questions, evaluate the appropriateness of those answers, and receive prizes if necessary.

[1311] As a concrete example, let's consider the case where a user enters the question, "What is the radius of the Earth?"

[1312] 1. The user enters "What is the radius of the Earth?" into the device.

[1313] 2. The device sends this question data to the server.

[1314] 3. The server receives the question data and analyzes it using natural language processing.

[1315] 4. The server generates the answer, "The Earth's radius is approximately 6,371 kilometers."

[1316] 5. The server uses an emotion engine and recognizes that the user's emotions are neutral because the input is in a calm tone.

[1317] 6. The server sends the response data and sentiment analysis results to the terminal.

[1318] 7. The device displays these to the user.

[1319] 8. The user reviews the answer and rates it as "slightly off."

[1320] 9. The user enters "Yes" as the evaluation result on the device.

[1321] 10. The device sends this evaluation result to the server.

[1322] 11. The server receives the evaluation result and, since it is "yes," decides to provide the prize.

[1323] 12. The server provides the user with an appropriate prize (e.g., a digital gift certificate) based on the user's rating and sentiment analysis results.

[1324] 13. The server logs information about the prizes provided.

[1325] In this way, we can create a system that evaluates the accuracy of the user's answers to their questions and provides prizes while also taking their emotions into consideration.

[1326] (Example 2)

[1327] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1328] Traditional question-answering systems struggle to provide accurate answers to user-generated questions and fail to consider user emotions and feedback, thus failing to improve user satisfaction. Furthermore, they lacked mechanisms to evaluate user satisfaction with answers and provide appropriate rewards based on that, making it difficult to maintain user engagement. New systems and methods are needed to address these challenges.

[1329] The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving a question entered by the user, means for analyzing the question and generating an answer, means for displaying the generated answer and sentiment analysis results, means for the user to evaluate whether the answer is "slightly off" from the question, and means for providing a prize based on the evaluation and sentiment analysis results. This not only provides accurate answers to the user's questions but also enables responses that take into account the user's emotions and feedback, thereby improving user satisfaction. Furthermore, by providing appropriate prizes based on the evaluation results, it is possible to increase user engagement.

[1330] A "user" refers to a person who uses the system to input questions and receive answers.

[1331] A "question" refers to information or a question that a user enters into a system.

[1332] "Means of receiving" refers to devices and software that send user questions and evaluations to a server and receive that information on the server.

[1333] "Means of analysis" refers to devices and software that use natural language processing techniques to understand received questions and generate appropriate answers.

[1334] "Answer" refers to the information or response text that the server generates in response to an analyzed question.

[1335] "Means of display" refers to devices or software that visually present generated responses and sentiment analysis results to the user.

[1336] "Means of evaluation" refers to devices or software that allow users to determine whether the displayed answers are appropriate and to input the results.

[1337] "Means of providing prizes" refers to devices or software that provide rewards or benefits to users based on user ratings and sentiment analysis results.

[1338] "Emotion analysis results" refer to data about the emotional state identified by the server based on the user's questions and feedback, after the server has analyzed the user's emotions.

[1339] "Natural language processing" refers to the technology that enables computers to understand, interpret, and manipulate human language.

[1340] A "server" refers to a computer system that receives and analyzes user inquiries, provides answers and sentiment analysis, and sends back the results.

[1341] A "terminal" refers to a device or equipment used by a user to input questions, display responses and sentiment analysis results from a server, and submit evaluations.

[1342] The present invention provides a system in which a user inputs a specialized question, and a server generates an appropriate answer to that question. Furthermore, the server has a function to analyze the user's emotions and provide the user with a prize based on that analysis. This system aims not only to improve user satisfaction but also to increase user engagement. Its embodiments are described in detail below.

[1343] Hardware and software to be used

[1344] User's device: An electronic device such as a personal computer or smartphone. Questions are entered, displayed, and evaluated through a dedicated application or web browser.

[1345] Server: A high-performance computer system. It analyzes questions and generates answers, analyzes sentiment, and processes evaluation results.

[1346] Natural language processing engine: Software for analyzing questions. For example, the Google Cloud Natural Language API as a natural language processing API.

[1347] Database: A data source for searching and generating answers to questions. For example, the Wikipedia API as an external API.

[1348] AI engine: An artificial intelligence model for generating answers. For example, GPT-4.

[1349] Emotion engine: Software used to analyze a user's emotions. For example, the Microsoft Azure Emotion API.

[1350] Specific operation of the system

[1351] 1. Enter and submit your question:

[1352] Users enter questions into a dedicated application or input form on a web browser using a PC or smartphone. For example, they might enter "What is the radius of the Earth?"

[1353] The terminal sends this entered question to the server over the internet, specifically using an HTTP POST request.

[1354] 2. Question analysis and answer generation:

[1355] The server analyzes the received question using natural language processing technology (Google Cloud Natural Language API). This analysis helps understand the intent and content of the question.

[1356] Based on the analysis results, the server uses a database (Wikipedia API) and an AI engine (GPT-4) to generate appropriate answers. For example, it might generate an answer such as, "The Earth's radius is approximately 6,371 kilometers."

[1357] 3. Analysis of emotions:

[1358] The server analyzes the user's input using an emotion engine (Microsoft Azure Emotion API) to determine the user's emotion. For example, if the question "What is the radius of the Earth?" is entered in a calm tone, the server will recognize the user's emotion as neutral.

[1359] 4. Submitting and viewing responses:

[1360] The server sends the generated response and sentiment analysis results to the terminal.

[1361] The device displays the received response and sentiment analysis results to the user. For example, it might display "The Earth's radius is approximately 6,371 kilometers" along with the sentiment analysis results.

[1362] 5. Rating of the answer:

[1363] The user reviews the answer displayed on their device and evaluates whether the answer is appropriate for the question. For example, they might answer "Yes" or "No" to the question "Is this answer appropriate?".

[1364] The terminal then sends the user's evaluation information back to the server. The evaluation data is sent using an HTTP POST request.

[1365] 6. Provision of prizes:

[1366] The server processes the received evaluation information and sentiment analysis results. If the evaluation is "yes," the type of prize is adjusted based on the sentiment analysis results.

[1367] The server then performs the process of providing the selected prize to the user. For example, it might send a digital gift certificate via email or provide a download link.

[1368] Specific example

[1369] The user enters the question "What is the radius of the Earth?" into the device. The device sends this question to the server. The server analyzes the question and generates the answer "The radius of the Earth is approximately 6,371 kilometers." Furthermore, it uses an emotion engine to recognize the user's emotion as neutral. The server sends the answer and the emotion analysis results to the device, which displays them to the user. The user confirms the answer and rates it as "yes." The device sends the rating result to the server. Based on the rating result and the emotion analysis results, the server provides the user with a digital gift certificate.

[1370] Example of a prompt:

[1371] Example prompt for generating a system description:

[1372] "This system works by having the user input a specialized question, and the server generates the answer. The server uses natural language processing technology to analyze the question and generates the answer using a database and an AI engine. Furthermore, it uses an emotion engine to recognize the user's emotions and provides an appropriate prize based on the results. The terminal displays the generated answer and the emotion analysis results to the user, who evaluates whether the answer is appropriate. The evaluation results are sent back to the server, which then provides the user with a prize based on that information."

[1373] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1374] Step 1:

[1375] The user enters the question using their device. Specifically, the user enters a question such as "What is the radius of the Earth?" in text format into an input form on a dedicated application or web browser. This input is temporarily stored on the device as data to be sent to the server.

[1376] Input: User's question text

[1377] Output: Question data for server transmission

[1378] Step 2:

[1379] The terminal generates an HTTP POST request to send the entered question data to the server. Specifically, it includes the question text in the HTTP request body and sends the request to a specific endpoint on the server.

[1380] Input: Question data entered by the user

[1381] Output: HTTP request sent to the server

[1382] Step 3:

[1383] The server extracts question data from the received HTTP request and analyzes the question using a natural language processing engine (Google Cloud Natural Language API). This analysis helps to understand the intent and content of the question.

[1384] Input: Question data sent from the device

[1385] Output: Analyzed question data

[1386] Step 4:

[1387] The server generates answers based on the analyzed question data, using a database (Wikipedia API) and an AI engine (GPT-4). For example, it might generate an answer such as, "The Earth's radius is approximately 6,371 kilometers." A pre-trained AI model is used in this process.

[1388] Input: Analyzed question data

[1389] Output: Generated response data

[1390] Step 5:

[1391] The server analyzes the user's input text using an emotion engine (Microsoft Azure Emotion API) to determine the user's emotional state. For example, it identifies emotional states such as positive, negative, or neutral.

[1392] Input: User's question text

[1393] Output: Sentiment analysis result data

[1394] Step 6:

[1395] The server generates an HTTP response to send the generated response data and sentiment analysis results to the terminal. The response includes the response and sentiment analysis results.

[1396] Input: Generated response data and sentiment analysis result data

[1397] Output: HTTP response sent to the terminal

[1398] Step 7:

[1399] The device analyzes the HTTP response received from the server and displays the response data and sentiment analysis results to the user. Specifically, the screen displays "The Earth's radius is approximately 6,371 kilometers," along with the sentiment analysis results.

[1400] Input: HTTP response sent from the server

[1401] Output: Responses and sentiment analysis results displayed to the user

[1402] Step 8:

[1403] The user reviews the answers displayed on their device and evaluates their appropriateness using a "yes / no" format. For example, they might answer "yes" to the question, "Is this answer appropriate?"

[1404] Input: Select user rating

[1405] Output: Evaluation data entered into the terminal

[1406] Step 9:

[1407] The device generates an HTTP POST request to send the user's rating data back to the server. The request includes the user's rating information.

[1408] Input: Evaluation data entered by the user.

[1409] Output: HTTP request sent to the server

[1410] Step 10:

[1411] The server processes the prize distribution based on the received evaluation information and sentiment analysis results. If the evaluation is "yes," it selects an appropriate prize based on the sentiment analysis results and provides it to the user. For example, it may send a digital gift certificate via email or provide a download link.

[1412] Input: User rating data and sentiment analysis results data

[1413] Output: Prizes provided to the user

[1414] (Application Example 2)

[1415] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1416] Traditional question-answering systems only provide appropriate answers to user-entered questions, lacking feedback and sentiment analysis to enhance user satisfaction. This creates a risk of inaccurate answers or responses that disregard user feelings. Furthermore, they fail to offer incentives that reflect user satisfaction.

[1417] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1418] In this invention, the server includes means for receiving a question entered by a user, means for analyzing the question and generating an answer, and means for displaying the generated answer and sentiment analysis results. This enables the provision of appropriate answers to the user's questions and the provision of incentives based on sentiment analysis.

[1419] A "user" is a party who uses this system to input questions and receive answers.

[1420] A "question" refers to the content of an inquiry entered by a user, seeking specialized information or knowledge.

[1421] "Means" refers to the technical components used to perform a specific function or task.

[1422] "Analysis" is the process of analyzing an input question using techniques such as natural language processing.

[1423] "Answer" refers to the information provided by the system in response to the analyzed question.

[1424] "Emotion analysis" is the process of recognizing and analyzing a user's emotions based on their input and other factors.

[1425] "Evaluation" is the act of a user judging the appropriateness of the answer they have provided and providing feedback to the system.

[1426] "Prizes" refers to rewards or incentives provided by the system based on user ratings and sentiment analysis results.

[1427] "Emotional analysis results" refer to information about the user's emotional state obtained based on emotional analysis.

[1428] "Generation" refers to the process by which the system creates new answers or sentiment analysis results based on the analysis results.

[1429] To realize this invention, it is necessary to use a cloud server, a user terminal, and related software.

[1430] Hardware configuration

[1431] Cloud Server: Responsible for data processing and storage. Utilizes cloud services such as AWS and Google Cloud Platform.

[1432] User terminal: A device used for entering questions, viewing answers, and entering ratings. Primarily a smartphone is used.

[1433] Software Configuration

[1434] 1. Natural Language Processing (NLP) Techniques:

[1435] We use TensorFlow and spaCy to analyze questions and generate appropriate answers.

[1436] 2. Emotion analysis engine:

[1437] Use the IBM Watson Emotion Analysis API to analyze user emotions.

[1438] 3. Database:

[1439] Use Firebase Firestore to store and manage question, answer, and rating data.

[1440] Program processing

[1441] The server receives questions entered by users and analyzes them using natural language processing technology. Based on the analyzed questions, the server utilizes a database and generative AI models to generate appropriate answers.

[1442] Next, an emotion analysis engine is used to analyze the user's emotions from their input. For example, the positive or negative expressions included in the input are used to recognize the user's emotional state.

[1443] The server sends the generated response and sentiment analysis results to the user's terminal. The user's terminal displays the response and sentiment analysis results to the user. The user reviews the displayed response and evaluates whether it is appropriate. The user's evaluation is in the form of "yes / no," and the evaluation information is sent back from the user's terminal to the server.

[1444] The server processes the received evaluation information and sentiment analysis results. If the evaluation is "yes," it adjusts the type of prize based on the sentiment analysis results. For example, if the user indicates negative emotions, it provides a specific prize that promotes positive emotions.

[1445] Specific example

[1446] Let's say a user launches a smartphone app and types, "How do I use an electronic payment app?" A cloud server receives the question and analyzes it using TensorFlow or spaCy. Based on the analysis, the server generates an answer, such as, "To use an electronic payment app, open the app, select a payment option, and enter the appropriate information."

[1447] Next, the server uses the IBM Watson Emotion Analysis API to analyze the user's input and recognizes that the user is feeling slightly anxious. This response and the emotion analysis result are sent to the user's terminal, where the user reviews the displayed content on the screen and evaluates whether it is appropriate. If the evaluation is "yes" and the emotion analysis result indicates slight anxiety, a reward (for example, a discount coupon usable on the next visit) is offered to alleviate that anxiety.

[1448] Example of a prompt

[1449] "Please enter your question about electronic payment apps. We will also perform sentiment analysis based on your answers. We will evaluate the accuracy of your answers and offer prizes accordingly."

[1450] In this way, by analyzing the user's emotional state and providing corresponding responses and incentives, it becomes possible to improve user satisfaction.

[1451] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1452] Step 1:

[1453] The user launches the app on their smartphone and enters a question. The entered information is sent from the user's device to the server. The questions entered here are specialized inquiries and are sent in text format.

[1454] Step 2:

[1455] The server analyzes the received question and generates an answer. In this process, the server uses natural language processing technologies such as TensorFlow and spaCy to analyze the question and generate an appropriate response. The generated answer is stored on the server in text format.

[1456] Step 3:

[1457] The server performs sentiment analysis based on the analyzed questions and generated answers. Using the IBM Watson Emotion Analysis API, it analyzes the user's emotions from the question input. The sentiment analysis results are output as specific emotional states such as positive, negative, or neutral.

[1458] Step 4:

[1459] The server sends the generated response and sentiment analysis results to the user's terminal. The user's terminal displays the received response and sentiment analysis results on its screen. The content displayed here shows the response to the question and the user's emotional state.

[1460] Step 5:

[1461] The user reviews the displayed answers and evaluates their appropriateness. The evaluation is entered into the user's terminal in the format of "yes / no". The evaluation results are then sent back to the server from the user's terminal.

[1462] Step 6:

[1463] The server processes the provision of prizes based on the received evaluation information and sentiment analysis results. If the evaluation is "yes" and the sentiment analysis result is negative, it determines and provides a prize (e.g., a discount coupon) to promote positive emotions in the user. The details of the prize provided are recorded in the database.

[1464] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1465] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1466] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[1467] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1468] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[1469] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[1470] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[1471] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[1472] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[1473] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[1474] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[1475] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[1476] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[1477] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1478] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[1479] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[1480] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[1481] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[1482] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[1483] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[1484] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[1485] The following is further disclosed regarding the embodiments described above.

[1486] (Claim 1)

[1487] A means of receiving questions entered by the user,

[1488] A means for analyzing the aforementioned questions and generating answers,

[1489] A means of displaying the generated answer,

[1490] A means for the user to evaluate whether the aforementioned answer is "slightly off" from the question,

[1491] A means of providing prizes based on the aforementioned evaluation,

[1492] A system that includes this.

[1493] (Claim 2)

[1494] The system according to claim 1, comprising means for using natural language processing to analyze a received question.

[1495] (Claim 3)

[1496] The system according to claim 1, comprising means for receiving evaluations entered by a user for the generated response.

[1497] "Example 1"

[1498] (Claim 1)

[1499] A means of receiving information entered by the user,

[1500] Means for analyzing the aforementioned information and generating a response,

[1501] Means for displaying the generated response,

[1502] A means for the user to evaluate whether the aforementioned response is "slightly off" from the information,

[1503] Means for providing rewards based on the aforementioned evaluation,

[1504] A system that includes this.

[1505] (Claim 2)

[1506] The system according to claim 1, comprising means of using a generative AI model to analyze received information.

[1507] (Claim 3)

[1508] The system according to claim 1, comprising means for receiving evaluations entered by a user for the generated response.

[1509] "Application Example 1"

[1510] (Claim 1)

[1511] A means of receiving questions entered by the user,

[1512] A means for analyzing the aforementioned questions and generating answers,

[1513] A means of displaying the generated answer,

[1514] A means for the user to evaluate whether the aforementioned answer is "slightly off" from the question,

[1515] A means of providing prizes based on the aforementioned evaluation,

[1516] A means of providing the user with additional prompts to correct or complete the generated answer,

[1517] A means of providing points or coupons to users based on evaluation results,

[1518] A system that includes this.

[1519] (Claim 2)

[1520] The system according to claim 1, comprising means for using natural language processing to analyze a received question.

[1521] (Claim 3)

[1522] The system according to claim 1, comprising means for receiving evaluations entered by a user for a generated response, and means for generating a response again based on a corrected prompt statement.

[1523] "Example 2 of combining an emotion engine"

[1524] (Claim 1)

[1525] A means of receiving questions entered by the user,

[1526] A means for analyzing the aforementioned questions and generating answers,

[1527] A means for displaying the generated responses and sentiment analysis results,

[1528] A means for the user to evaluate whether the aforementioned answer is "slightly off" from the question,

[1529] A means for providing prizes based on the aforementioned evaluation and sentiment analysis results,

[1530] A system that includes this.

[1531] (Claim 2)

[1532] The system according to claim 1, comprising means for using natural language processing to analyze received questions and an emotion engine for analyzing the user's emotions.

[1533] (Claim 3)

[1534] The system according to claim 1, comprising means for displaying the generated response and sentiment analysis results to the user and receiving the evaluation entered by the user.

[1535] "Application example 2 when combining with an emotional engine"

[1536] (Claim 1)

[1537] A means of receiving questions entered by the user,

[1538] A means for analyzing the aforementioned questions and generating answers,

[1539] A means of displaying the generated answer,

[1540] A means for the user to evaluate whether the aforementioned answer is "slightly off" from the question,

[1541] A means of providing prizes based on the aforementioned evaluation,

[1542] A means of analyzing user emotions,

[1543] means for displaying the emotion analysis results,

[1544] A means for adjusting the type of prize based on the aforementioned sentiment analysis results and evaluations,

[1545] A system that includes this.

[1546] (Claim 2)

[1547] The system according to claim 1, comprising means for using natural language processing to analyze a received question.

[1548] (Claim 3)

[1549] The system according to claim 1, comprising means for receiving evaluations entered by a user for the generated response. [Explanation of Symbols]

[1550] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of receiving questions entered by the user, A means for analyzing the aforementioned questions and generating answers, A means of displaying the generated answer, A means for the user to evaluate whether the aforementioned answer is "slightly off" from the question, A means of providing prizes based on the aforementioned evaluation, A system that includes this.

2. The system according to claim 1, comprising means for using natural language processing to analyze a received question.

3. The system according to claim 1, comprising means for receiving evaluations entered by a user for the generated response.

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