system

Abstract

Provide a system. A method for a user to input a question via an electronic device; means for transmitting the query as a format to a server; means for analyzing the question received by the server and transmitting the question to a natural language processing engine; means for the natural language processing engine to generate an answer to the question and return it to the server; means for reformatting and transmitting the response received by the server to the electronic device; The system includes means for the electronic device to display the answers on a user interface.

Description

[Technical Field]

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

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

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

[0004] In the past, the most common way for children to ask questions about things they didn't understand in their studies and receive appropriate answers was to use a private tutoring service. However, arranging a private tutor is time-consuming and costly, and it is often difficult to get a quick answer to a specific question. The present invention aims to solve these problems by providing a system that allows children to ask questions in real time while studying and receive an immediate answer. [Means for solving the problem]

[0005] The present invention provides a system including: means for a user to input a question via an electronic device; means for transmitting the question as a format to a server; means for the server to analyze the received question and transmit it to a natural language processing engine; means for the natural language processing engine to generate an answer to the question and transmit it back to the server; means for the server to reformat the received answer and transmit it to the electronic device; and means for the electronic device to display the answer on a user interface. The above-mentioned problems can be solved by further including means for converting the question into JSON format and means for the natural language processing engine to receive the question via an API request and generate an answer.

[0006] A "user" is someone who uses the system to enter a question and receive an answer.

[0007] An "electronic device" is a device used by a user to enter questions and view answers, including, for example, a smartphone, tablet, or computer.

[0008] A "question" is something that a user inputs via an electronic device, and is a problem or question that the user wants answered.

[0009] "Formatting" refers to putting data into a standard form, specifically converting questions into JSON format.

[0010] "Server" means a computer system that receives, analyzes, and processes queries submitted by users.

[0011] A "natural language processing engine" is an artificial intelligence (AI) technology that understands questions entered by users and generates answers to them.

[0012] An "API Request" is a request made to other software through an Application Program Interface, particularly one used to submit a question to a natural language processing engine.

[0013] An "answer" is an answer generated by a natural language processing engine and provided to a user via a server.

[0014] A "user interface" is a screen or operating means displayed on an electronic device, and is a means of expression through which a user can input questions and confirm answers. [Brief explanation of the drawings]

[0015] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0016] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0019] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0020] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0021] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0026] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0028] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0035] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0036] The system of the present invention allows users to input questions via electronic devices and provides answers to those questions in real time. The system begins operation when a user inputs and submits a question via a dedicated app or web browser.

[0037] First, a user opens a dedicated app or web browser on an electronic device such as a smartphone, tablet, or computer. The user enters a question in the question input field and presses the "Submit" button. For example, the user can enter "What is the answer to 1+1?"

[0038] Next, the device takes the question entered by the user, converts it to JSON format, and sends it to the server. The JSON format data looks like this:

[0039] json

[0040] {

[0041] "question": "What is the answer to 1+1?"

[0042] }

[0043] This JSON data is sent to the server as an HTTP POST request.

[0044] The server receives the HTTP POST request from the device, analyzes the data, and extracts the question body. The extracted question is converted into an API request to be sent to the natural language processing engine. For example, the following API request is made:

[0045] json

[0046] {

[0047] "prompt": "What is the answer to 1+1?",

[0048] "max_tokens": 10

[0049] }

[0050] The server sends this API request to the natural language processing engine.

[0051] The natural language processing engine analyzes the received question and generates the best answer, such as "The answer to 1 + 1 is 2," and the generated answer is sent back to the server in JSON format.

[0052] The server receives the response from the NLP engine, reformats it, and sends it back to the device. The reformatted JSON data looks like this:

[0053] json

[0054] {

[0055] "answer": "The answer to 1+1 is 2"

[0056] }

[0057] The server sends this answer to the terminal as an HTTP response.

[0058] Finally, the device receives the HTTP response from the server, extracts the answer data, and displays it on the user interface. The user can see the answer displayed on the device screen: "The answer to 1 + 1 is 2."

[0059] The system allows users to get answers to specific questions more quickly and efficiently than traditional tutoring services, automatically handling the entire process from submitting a question to displaying the answer, providing immediate feedback to users.

[0060] The processing flow will be explained below.

[0061] Step 1:

[0062] The user opens a dedicated app or web browser, enters "What is the answer to 1+1?" in the question input field, and presses the send button.

[0063] Step 2:

[0064] The terminal receives the question entered by the user and converts it into JSON format. The JSON data at this time looks like this:

[0065] json

[0066] {

[0067] "question": "What is the answer to 1+1?"

[0068] }

[0069] Step 3:

[0070] The device sends the JSON-encoded question to the server as an HTTP POST request, with the HTTP request header information also set appropriately.

[0071] Step 4:

[0072] The server receives an HTTP POST request from the device, extracts the JSON data from the request, and then extracts the question body.

[0073] Step 5:

[0074] The server prepares an appropriate API request to send the extracted question to the natural language processing engine, such as the following:

[0075] json

[0076] {

[0077] "prompt": "What is the answer to 1+1?",

[0078] "max_tokens": 10

[0079] }

[0080] Step 6:

[0081] The server sends this API request to the natural language processing engine, which makes the API request to a specific endpoint.

[0082] Step 7:

[0083] The natural language processing engine analyzes the received question and generates the most appropriate answer, for example, "The answer to 1 + 1 is 2."

[0084] Step 8:

[0085] The natural language processing engine returns the generated answer in JSON format to the server. The returned JSON data is as follows:

[0086] json

[0087] {

[0088] "response": "1+1 equals 2"

[0089] }

[0090] Step 9:

[0091] The server receives the response from the natural language processing engine, parses the received data, and reformats it if necessary.

[0092] Step 10:

[0093] The server will then send the reformatted response back to the device in JSON format, for example something like this:

[0094] json

[0095] {

[0096] "answer": "The answer to 1+1 is 2"

[0097] }

[0098] Step 11:

[0099] The terminal receives the HTTP response from the server, analyzes the received response data, and extracts the answer data.

[0100] Step 12:

[0101] The device displays the extracted answer on the user interface, and the user confirms the answer displayed on the screen: "The answer to 1 + 1 is 2."

[0102] This series of steps provides a quick and accurate answer to the user's question.

[0103] Example 1

[0104] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0105] Conventional information retrieval systems and question-answering systems have had issues with the speed and accuracy of the time it takes for a user to receive an answer after entering a question. In particular, in systems that provide answers in real time, the time it takes to analyze the question and generate an answer has been a problem. There has also been a demand for a method that allows users to easily enter questions and receive answers without using complex input formats.

[0106] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0107] In this invention, the server includes means for converting a question into JSON format and sending it to the server, means for the server to analyze the question received and send it to a natural language processing engine, and means for the natural language processing engine to generate an answer to the question and return it to the server. This allows users to easily input questions and receive quick and accurate answers.

[0108] A "user" is a person or entity that utilizes the system to enter questions and receive answers.

[0109] A "device" is an electronic device, such as a smartphone, tablet, or computer, that a user uses to enter a question.

[0110] "Means for converting a question into JSON format and sending it to a server" refers to means capable of converting a question entered by a user into JavaScript (registered trademark) Object Notation format and executing a process to send it to a server via the Internet.

[0111] A "server" is a central processing unit responsible for receiving, parsing and sending queries submitted by users to a natural language processing engine.

[0112] A "means for analyzing a question" is a means capable of understanding the content of a question entered by a user and converting the data into an appropriate format.

[0113] A "natural language processing engine" is an engine that utilizes artificial intelligence techniques used to generate responses to questions.

[0114] An "API Request" is a request to send or receive data via an Application Programming Interface.

[0115] "Means for reformatting and transmitting a response to a terminal" refers to means capable of executing a process for reformatting a response obtained from a natural language processing engine and transmitting the response to a user's device.

[0116] A "user interface" is a screen or interactive environment with which a user directly interacts and sees input and output.

[0117] The system of the present invention allows a user to input a question via a device and provides an answer to the question in real time. The following components and steps are used to implement the invention.

[0118] First, a user uses a device such as a smartphone, tablet, or computer. They open a dedicated app or web browser and enter a question into the question input field. For example, they might enter, "What is the answer to 1+1?" At this point, they press the "Send" button.

[0119] The terminal takes the question entered by the user and converts it into JavaScript Object Notation (JSON) format. The converted JSON data is then sent to the server via the Internet as an HTTP POST request. For example, the converted JSON data might look like this:

[0120] json

[0121] {

[0122] "question": "What is the answer to 1+1?"

[0123] }

[0124] The server receives the HTTP POST request sent from the device and parses the JSON formatted data. It extracts the question body and converts it into an API request. This API request is sent to a natural language processing engine (e.g., a generative AI model). An example of an API request is as follows:

[0125] json

[0126] {

[0127] "prompt": "What is the answer to 1+1?",

[0128] "max_tokens": 10

[0129] }

[0130] The natural language processing engine analyzes the received API request and generates the best answer, such as "The answer to 1+1 is 2," and sends the answer back to the server in JSON format. The generated JSON data looks like this:

[0131] json

[0132] {

[0133] "answer": "The answer to 1+1 is 2"

[0134] }

[0135] When the server receives the response from the natural language processing engine, it reformats it and sends the reformatted data to the device as an HTTP response.

[0136] Finally, the device receives the HTTP response from the server and extracts the answer data in JSON format. The user interface displays "The answer to 1+1 is 2." This allows the user to check the generated answer on the device screen.

[0137] Examples of prompt sentences include the following:

[0138] "What's the answer to 3 x 4?"

[0139] "What is the capital of Japan?"

[0140] This system allows users to obtain answers to their questions quickly and accurately, and is expected to be particularly useful in the fields of education and information retrieval.

[0141] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0142] Step 1:

[0143] The user opens a dedicated app or web browser on the device, enters a question in the question input field on the user interface, and presses the "Submit" button. For example, they enter "What is the answer to 1+1?" The input is in text format.

[0144] Step 2:

[0145] The terminal takes the user's input and converts the text data into JSON format. The input data is "What is the answer to 1+1?" and the converted JSON format is as follows:

[0146] json

[0147] {

[0148] "question": "What is the answer to 1+1?"

[0149] }

[0150] This JSON data is sent to the server as an HTTP POST request.

[0151] Step 3:

[0152] The server receives an HTTP POST request. The received data is in JSON format, and the server parses it. It extracts the content of the "question" field from the input JSON data and then converts it to generate an API request to the natural language processing engine. The specific format of the API request is as follows:

[0153] json

[0154] {

[0155] "prompt": "What is the answer to 1+1?",

[0156] "max_tokens": 10

[0157] }

[0158] Step 4:

[0159] The server sends the generated API request to the natural language processing engine. After the API request is sent, the natural language processing engine receives the request and begins analyzing it. This analysis process generates the best answer to the input question, "What is the answer to 1+1?"

[0160] Step 5:

[0161] The natural language processing engine generates the best answer. The generated answer is "The answer to 1+1 is 2." This answer is returned to the server in JSON format. The format of the returned data is as follows:

[0162] json

[0163] {

[0164] "answer": "The answer to 1+1 is 2"

[0165] }

[0166] Step 6:

[0167] The server receives the response data returned from the natural language processing engine. It reformats the received data and sends it to the device. The reformatted data looks like this:

[0168] json

[0169] {

[0170] "answer": "The answer to 1+1 is 2"

[0171] }

[0172] The server sends this data to the terminal as an HTTP response.

[0173] Step 7:

[0174] The device analyzes the HTTP response received from the server and displays it on the user interface. Specifically, the text "The answer to 1 + 1 is 2" is displayed. The user can check the answer on the device screen.

[0175] This allows users to smoothly experience the entire process from input to display of the answer.

[0176] (Application example 1)

[0177] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0178] On modern online shopping sites, users have limited means to instantly resolve questions about products and services. This can lead to a poor user experience and a risk of losing purchasing motivation. Furthermore, slow response times in FAQ sections and customer support can lead to users abandoning the site. To solve these issues, a system is needed that allows users to ask questions in real time and receive immediate answers.

[0179] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0180] In this invention, the server includes means for a user to input a question via an electronic device, means for formatting the question and sending it to the server, means for the server to analyze the received question and send it to a natural language processing engine, means for the natural language processing engine to generate an answer to the question and send it back to the server, means for the server to reformat the answer received and send it to the electronic device, means for the electronic device to display the answer on a user interface, and chatbot software embedded in an online shopping site where users can ask questions about products and services in real time, thereby enabling users to quickly resolve their questions and smoothly progress through the purchasing process.

[0181] An "electronic device" is a hardware device used by a user to input or receive information, including, for example, a smartphone, tablet, or computer.

[0182] The "means for inputting a question" is a function that provides an interface for a user to input a question in text format and send it to the system.

[0183] A "server" is a central processing unit that receives, analyzes, processes data over a network, and transmits the results to other devices.

[0184] A "natural language processing engine" is software or an algorithm that analyzes human-written language data, understands its content, and generates appropriate answers.

[0185] A "prompt sentence" is a phrase that explicitly specifies a user's question and is input to a natural language processing engine.

[0186] "Chatbot software" is a program that automatically interacts with users and generates answers to their questions.

[0187] A "format" is a procedure or convention for converting and organizing questions and answers into a particular form.

[0188] "Real-time" refers to the fact that the time between when a user enters a question and when they receive a response is very short, with the response being almost instantaneous.

[0189] "User interface" refers to the screen or display that contains the visual and operational elements that allow a user to interact with a system.

[0190] An "online shopping site" is a website that offers products and services over the Internet and allows users to purchase them online.

[0191] An embodiment of the present invention is a system for enabling a user to ask a question about a product or service in real time and for providing an answer to the question immediately. The system of the present invention includes the following means.

[0192] First, a user opens a dedicated app or web browser on an electronic device such as a smartphone, tablet, or computer. The user enters a question in the question input field and presses the "Submit" button. For example, the user can enter "What is the refund policy for this product?"

[0193] The device receives the question entered by the user, converts it into JSON format, and sends it to the server. The JSON format data looks like this:

[0194] json

[0195] {

[0196] "question": "What is the refund policy for this item?"

[0197] }

[0198] This JSON data is sent to the server as an HTTP POST request.

[0199] The server parses the incoming HTTP POST request, extracts the question body, and converts it into an API request to send to the natural language processing engine. The API request looks like this:

[0200] json

[0201] {

[0202] "prompt": "What is the refund policy for this item?",

[0203] "max_tokens": 10

[0204] }

[0205] The server sends this API request to the natural language processing engine.

[0206] A natural language processing engine (such as OpenAI's GPT-3) analyzes the received question and generates the optimal answer, such as "This product's refund policy is a full refund within 30 days," and the generated answer is sent back to the server in JSON format.

[0207] The server receives the response from the natural language processing engine, reformats it in a format that is easy for the user to understand, and sends it back to the device. The reformatted JSON data looks like this:

[0208] json

[0209] {

[0210] "answer": "This product's refund policy is a full refund within 30 days"

[0211] }

[0212] The server sends this answer to the terminal as an HTTP response.

[0213] Finally, the device receives the HTTP response from the server, extracts the answer data, and displays it on the user interface. The user can see the answer displayed on the device screen: "The refund policy for this product is a full refund within 30 days."

[0214] The system of the present invention features the incorporation of chatbot software into an online shopping site, allowing users to quickly resolve their questions and smoothly progress through the purchasing process. An example of a specific prompt sentence is "What is the refund policy for this product?"

[0215] The hardware used includes smartphones, tablets, and computers, and the software used includes React and Vue.js for the front end, Python, Flask, and Django for the back end, HTTP and REST for API communication, and OpenAI GPT-3 as a natural language processing engine. These systems improve the user experience and enable quick and efficient resolution of queries.

[0216] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0217] Step 1:

[0218] A user opens a dedicated app or web browser on a smartphone, tablet, or computer, enters a question, and presses the "Send" button. The input is the text data of the question. For example, the user might enter, "What is the refund policy for this product?" The output is the text question data sent from the device to the server.

[0219] Step 2:

[0220] The terminal converts the question data entered by the user into JSON format. The input is a text question, and the data processing involves converting this text into the following JSON format:

[0221] json

[0222] {

[0223] "question": "What is the refund policy for this item?"

[0224] }

[0225] The output is the question data in JSON format.

[0226] Step 3:

[0227] The terminal sends JSON-formatted question data to the server as an HTTP POST request. The input is JSON-formatted question data, and the data processing involves creating an HTTP request. The output is the HTTP request sent to the server.

[0228] Step 4:

[0229] The server parses the received HTTP POST request and extracts the question body. The input is the HTTP request, and the data operation is the parsing of JSON data. The output is the extracted question body. For example, the extracted sentence is "What is the refund policy for this product?"

[0230] Step 5:

[0231] The server converts the extracted question text into an API request to send to the natural language processing engine. The input is the question text, and the data processing involves converting it into the API request format as follows:

[0232] json

[0233] {

[0234] "prompt": "What is the refund policy for this item?",

[0235] "max_tokens": 10

[0236] }

[0237] The output is data in the form of an API request.

[0238] Step 6:

[0239] The server sends data in the form of an API request to the natural language processing engine. Generative AI models used include OpenAI GPT-3. The input is the API request data, and data processing involves sending an HTTP request. The output is the response data returned by the natural language processing engine.

[0240] Step 7:

[0241] The natural language processing engine generates an answer based on the question it receives. The input is a question in the form of an API request, and the data calculation is the generation of an answer by the NLP engine. For example, if the input question is "What is the refund policy for this product?", the generated answer will be "The refund policy for this product is a full refund within 30 days." The output is the answer data in JSON format.

[0242] Step 8:

[0243] The server receives the response data returned by the natural language processing engine, reformats it, and sends it to the terminal. The input is the response data in JSON format, and the data processing is to convert it into the final JSON format as shown below:

[0244] json

[0245] {

[0246] "answer": "This product's refund policy is a full refund within 30 days"

[0247] }

[0248] The output is the reformatted JSON response data.

[0249] Step 9:

[0250] The terminal analyzes the JSON formatted response data received from the server and displays it on the user interface. The input is the JSON formatted response data, and the data operation is data binding to the UI component. The output is the response displayed on the user interface. The user can confirm the response displayed on the screen of their electronic device, which states, "The refund policy for this product is a full refund within 30 days."

[0251] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0252] The system of the present invention allows a user to input a question via an electronic device and not only provides an answer to the question in real time, but also recognizes the user's emotions and provides more appropriate feedback.

[0253] The user opens a dedicated app or web browser on an electronic device such as a smartphone, tablet, or computer. The user enters "What is the answer to 1+1?" in the question input field and presses the send button. The emotion engine analyzes the user's emotions from this input and connects that emotional information to the system.

[0254] The device converts the question entered by the user into JSON format and emotional information, and sends it to the server. The JSON format data looks like this:

[0255] json

[0256] {

[0257] "question": "What is the answer to 1+1?",

[0258] "emotion": "neutral"

[0259] }

[0260] This JSON data and emotion information is sent to the server as an HTTP POST request.

[0261] The server receives the HTTP POST request from the device, analyzes the data, and extracts the question body and sentiment information. The extracted question and sentiment information are converted into an API request to be sent to the natural language processing engine. For example, make the following API request:

[0262] json

[0263] {

[0264] "prompt": "What is the answer to 1+1?",

[0265] "emotion": "neutral",

[0266] "max_tokens": 10

[0267] }

[0268] The server sends this API request to the natural language processing engine.

[0269] The natural language processing engine analyzes the received question and sentiment information to generate the optimal answer, such as "The answer to 1 + 1 is 2," and returns the answer in JSON format to the server.

[0270] The server receives the response from the NLP engine, reformats it, and sends it back to the device. The reformatted JSON data looks like this:

[0271] json

[0272] {

[0273] "answer": "The answer to 1+1 is 2"

[0274] }

[0275] The server sends this response data to the terminal as an HTTP response.

[0276] The device receives the HTTP response from the server, extracts the answer data and emotional feedback, and displays it on the user interface. The user can then respond based on the feedback. Along with the answer "The answer to 1 + 1 is 2," emotional feedback such as "You look happy!" is also displayed.

[0277] This system not only provides answers to specific questions more quickly and efficiently than traditional tutoring services, but also provides personalized feedback based on the user's emotions. The system automatically handles the entire process from submitting a question to displaying an answer based on the user's emotions, providing users with a more personalized service faster.

[0278] The processing flow will be explained below.

[0279] Step 1:

[0280] The user opens a dedicated app or web browser, enters "What is the answer to 1+1?" in the question input field, and presses the send button.

[0281] Step 2:

[0282] The terminal acquires the question entered by the user and sends the question content to the emotion engine.

[0283] Step 3:

[0284] The emotion engine analyzes the question and recognizes the user's emotion. In this case, the emotion engine determines the emotion as "neutral."

[0285] Step 4:

[0286] The device converts the acquired question and the recognition result from the emotion engine into JSON format. The JSON data at this time looks like this:

[0287] json

[0288] {

[0289] "question": "What is the answer to 1+1?",

[0290] "emotion": "neutral"

[0291] }

[0292] Step 5:

[0293] The device sends JSON format data to the server as an HTTP POST request.

[0294] Step 6:

[0295] The server receives the HTTP POST request from the device, analyzes the data, and extracts the question text and sentiment information.

[0296] Step 7:

[0297] The server converts the extracted question and sentiment information into an API request to send to the natural language processing engine. An example of an API request is as follows:

[0298] json

[0299] {

[0300] "prompt": "What is the answer to 1+1?",

[0301] "emotion": "neutral",

[0302] "max_tokens": 10

[0303] }

[0304] Step 8:

[0305] The server sends this API request to the natural language processing engine.

[0306] Step 9:

[0307] The natural language processing engine analyzes the received question and sentiment information to generate the most appropriate answer, such as "The answer to 1 + 1 is 2."

[0308] Step 10:

[0309] The natural language processing engine returns the generated answer in JSON format to the server. The returned JSON data is as follows:

[0310] json

[0311] {

[0312] "response": "1+1 equals 2"

[0313] }

[0314] Step 11:

[0315] The server receives the response from the natural language processing engine, reformats it if necessary, and sends the reformatted data to the device in the following JSON format:

[0316] json

[0317] {

[0318] "answer": "The answer to 1+1 is 2",

[0319] "emotion_feedback": "Thank you!"

[0320] }

[0321] Step 12:

[0322] The device receives the HTTP response from the server and extracts the response data and emotional feedback.

[0323] Step 13:

[0324] The device displays the extracted answer and emotional feedback on the user interface. For example, the answer "The answer to 1+1 is 2" is displayed along with emotional feedback such as "Thank you!"

[0325] This series of steps allows users to not only get a quick and appropriate answer to their question, but also receive feedback based on their emotions.

[0326] Example 2

[0327] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0328] Conventional question-answering systems only provide answers to questions entered by the user, and are therefore unable to provide feedback that takes into account the user's emotional state. This has resulted in a lack of individualized responses and personalized services for users. Furthermore, there has been no system that can analyze a user's emotions and provide optimal answers based on those emotions, making it difficult to improve the user experience.

[0329] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for a user to input a question and emotion information, a means for converting the question and emotion information into JSON format, and a means for transmitting the question and emotion information to a natural language processing engine and generating an answer. This makes it possible to provide an optimal answer based on the user's emotion.

[0330] "User" refers to a person who uses the system to enter questions and receive answers.

[0331] "Electronic device" refers to a device, such as a smartphone, tablet, or computer, that a user uses to enter a question.

[0332] A "question" refers to a question that a user asks the system.

[0333] "Emotion analysis engine" refers to software or algorithms that analyze a user's emotional state based on questions entered by the user.

[0334] "Emotion information" refers to data that represents the emotional state analyzed by the emotion analysis engine from the user's question.

[0335] "Format" refers to a specific data format for sending questions and emotion information to the server.

[0336] "Server" refers to a computer system that analyzes data received from a terminal and communicates with a natural language processing engine to generate an answer.

[0337] A "natural language processing engine" refers to software or algorithms that generate appropriate answers based on input questions and emotional information.

[0338] "Answer" refers to information generated by a natural language processing engine in response to a user's question.

[0339] "Reformatting" refers to the process of converting the answers received from the natural language processing engine into a format that is easy for the user to understand.

[0340] "User interface" refers to the graphical display area and operation portion through which a user interacts with a system.

[0341] "Emotional feedback" refers to additional feedback or comments provided based on the user's emotional information.

[0342] "JSON format" refers to a lightweight data exchange format for expressing data in text format.

[0343] "API Request" means a communication request to send a question and sentiment information to a natural language processing engine and receive a response.

[0344] The present invention provides a system that allows a user to input a question using an electronic device and receives feedback based on the answer and emotions to the question. Specifically, a user uses an electronic device such as a smartphone, tablet, or computer and opens a dedicated application or web browser. When the user inputs a question and presses the send button, the system operates in the following steps.

[0345] First, the device receives a question entered by the user. At this time, the question is analyzed in real time by a sentiment analysis engine to obtain the user's emotional information. This sentiment analysis uses a general algorithm for identifying emotions from voice or text. For example, the sentiment analysis APIs of IBM Watson (registered trademark) and Microsoft (registered trademark) Azure (registered trademark) can be considered.

[0346] The device then converts the question and emotion information into JSON format data, which makes it easier to transmit and analyze the data, and sends the generated JSON data to the server as an HTTP POST request.

[0347] The server analyzes the HTTP POST request received from the device and extracts the question and sentiment information separately. Based on this extracted data, the server sends an API request to a natural language processing engine. The natural language processing engine uses advanced text analysis algorithms such as OpenAI GPT-3 and Google® BERT. This engine analyzes the user's question and sentiment information and generates the optimal answer.

[0348] The generated answer is sent back to the server, which then reformats it and sends it to the device. The reformatted answer data is displayed to the user on a user interface. The user is provided with the answer to their question as well as sentiment-based feedback, allowing them to get a more personalized answer quickly.

[0349] Specific examples

[0350] If a user types "What is the answer to 1+1?", the sentiment analysis engine will recognize the user's sentiment as "neutral." It will then generate JSON data containing the following:

[0351] json

[0352] {

[0353] "question": "What is the answer to 1+1?",

[0354] "emotion": "neutral"

[0355] }

[0356] This data is sent to the server, which extracts the question "What is the answer to 1+1?" and the emotion "neutral." The following prompt is sent to the natural language processing engine:

[0357] "What is the answer to 1 + 1?" (Neutral emotion, maximum tokens: 10)

[0358] The natural language processing engine responds with "1 + 1 equals 2," which is then reformatted and sent to the device, where it displays the specific answer to the question along with feedback such as "You look happy!" This allows users to get answers to their questions quickly and efficiently, while also receiving personalized feedback based on their emotions.

[0359] Unlike conventional question-answering systems, this system provides personalized feedback based on the user's emotions, improving the user experience.

[0360] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0361] Step 1: User enters question

[0362] A user opens a dedicated application or web browser on a smartphone, tablet, or computer, enters a question in the question input field, for example, "What is the answer to 1+1?", and presses the submit button. This action sends the entered question to the system.

[0363] Input: A question typed by the user (e.g., "What is the answer to 1+1?")

[0364] Output: Question data entered by the user

[0365] Step 2: The device analyzes emotions

[0366] The device sends the question received from the user to a sentiment analysis engine, which analyzes the user's sentiment. This analysis uses an algorithm that extracts sentiment from text, and emotional information such as "neutral" is obtained.

[0367] Input: User question data

[0368] Output: User question data and sentiment information (e.g., "neutral")

[0369] Step 3: The device converts the question and emotion information into JSON format.

[0370] The device converts the user's question and the analyzed emotion information into JSON format. Specifically, it generates the following JSON data:

[0371] json

[0372] {

[0373] "question": "What is the answer to 1+1?",

[0374] "emotion": "neutral"

[0375] }

[0376] Input: User question data and emotion information

[0377] Output: JSON format data

[0378] Step 4: The device sends the data to the server

[0379] The device sends the generated JSON data as an HTTP POST request to the server, which then uses a network communication library to send the data.

[0380] Input: JSON format data

[0381] Output: HTTP POST request sent to the server

[0382] Step 5: The server receives and parses the data

[0383] The server analyzes the HTTP POST request received from the device, extracts JSON data from the request body, and then extracts the question and sentiment information from this data separately and passes them on to the next stage of processing.

[0384] Input: HTTP POST request

[0385] Output: Extracted question and sentiment information

[0386] Step 6: The server sends a request to the natural language processing engine

[0387] The server converts the extracted question and sentiment information into an API request to be sent to the natural language processing engine, for example in the following format:

[0388] Prompt: "What is the answer to 1 + 1?" (Emotion: Neutral, Max Tokens: 10)

[0389] Input: Extracted question and sentiment information

[0390] Output: API request to the natural language processing engine

[0391] Step 7: The natural language processing engine generates the answer

[0392] The natural language processing engine analyzes the received question and emotional information to generate the optimal answer, such as "The answer to 1 + 1 is 2."

[0393] Input: API request

[0394] Output: Generated response data

[0395] Step 8: Server Reformats Response Data

[0396] The server reformats the response data received from the natural language processing engine and converts it into a format that is easy for the user to understand, for example, into a JSON format like this:

[0397] json

[0398] {

[0399] "answer": "The answer to 1+1 is 2"

[0400] }

[0401] Input: Generated response data

[0402] Output: Reformatted response data

[0403] Step 9: The server sends the reformatted data to the device

[0404] The server then sends the reformatted response data to the terminal as an HTTP response. The data is then sent to the terminal via network communication.

[0405] Input: Reformatted response data

[0406] Output: Sending HTTP response to the terminal

[0407] Step 10: Your device displays your answer and emotional feedback

[0408] The device analyzes the HTTP response received from the server and displays the answer and emotional feedback on the user interface. For example, the answer "The answer to 1 + 1 is 2" and the emotional feedback "You look happy!" are displayed.

[0409] Input: HTTP response from the server

[0410] Output: Display of answer and emotional feedback to the user

[0411] (Application example 2)

[0412] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0413] In conventional systems, even if a user inputs a question, they are unable to receive emotional feedback and are simply provided with a mechanical answer. This means that personalized feedback based on the user's emotions is not provided, which can lead to a decrease in work efficiency and employee satisfaction, especially in stressful environments such as factories.

[0414] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for analyzing a received question and analyzing the user's emotion via an emotion analysis engine, means for transmitting the question and the analyzed emotion information to a natural language processing engine, and means for the natural language processing engine to generate an answer to the question and feedback based on the emotion and return it to the server. This makes it possible to provide personalized feedback based on the user's emotion in real time.

[0415] 1. "Means for users to input questions via electronic devices" refers to devices that provide an interface for users to input questions in natural language using electronic devices such as smartphones, tablets, and computers.

[0416] 2. "Means for transmitting the question to the server in a format" refers to the protocol and hardware that converts the input question into an appropriate data format (e.g., JSON format) and transmits it to the server via a network.

[0417] 3. "Sentiment analysis engine" refers to algorithms and software that analyze and extract user emotions from input text data.

[0418] 4. A "natural language processing engine" is an algorithm or software that analyzes text data, performs natural language processing, and generates answers to questions.

[0419] 5. "Means for reformatting the response received by the server and transmitting it to the electronic device" refers to protocols and hardware that receive the response returned from the natural language processing engine, convert it into an appropriate display format, and transmit it to the electronic device.

[0420] 6. "Emotion-based feedback" refers to additional comments or information provided based on an analysis of a user's emotions to generate a personalized response to the user.

[0421] 7. "User interface" means the screen and operating environment through which a user can input and confirm questions via an electronic device.

[0422] 8. "API Request" is a protocol for sending specific data to another software or service and receiving the processing results.

[0423] A system for implementing the present invention allows a user to input a question via an electronic device and receive a real-time answer. The system uses a sentiment analysis engine to analyze the user's sentiment, and a natural language processing engine to provide personalized feedback based on the sentiment.

[0424] The overall approach of the system is as follows: First, the user inputs a question using a smartphone, tablet, smart glasses, or computer. The input question is converted into an appropriate data format, such as JSON, and sent to the server. At this stage, the user's emotional information is also sent along with the question data.

[0425] The server analyzes the received question and emotion data and executes the user's emotion through an emotion analysis engine. As a result, the question and emotion information are obtained. The server then sends this question and emotion information to a natural language processing engine. The natural language processing engine generates an answer to the question and also generates feedback based on the user's emotion. If the user is feeling a certain emotion, this feedback provides a response appropriate to that emotion.

[0426] The generated answers and emotional feedback are then sent back to the server, which then reformats and sends the data to the user's electronic device, where the user can finally view the answers to the questions and the emotional feedback.

[0427] For example, if a factory worker uses smart glasses to ask, "Please tell me how to install this part," their emotions will also be analyzed. If the system detects that the worker is nervous, it will display feedback such as, "Here's how to install this part. Please relax and continue working." In this way, personalized feedback is provided according to the user's emotions.

[0428] An example of a prompt for the generative AI model is as follows:

[0429] "Please tell me how to install this part" is entered.

[0430] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0431] Step 1:

[0432] A user inputs a question via an electronic device. The electronic device receives the input as text data and analyzes the user's emotions using a sentiment analysis engine. This analysis provides the question entered by the user and emotional information when entering the question.

[0433] input:

[0434] A text question entered by the user (e.g., "How do I install this part?")

[0435] The user's emotional state

[0436] output:

[0437] Question text

[0438] Emotional information (e.g., "I'm nervous")

[0439] Step 2:

[0440] The device formats the question and emotion information into JSON format and sends it to the server using an HTTP POST request.

[0441] input:

[0442] Question text (e.g., "How do I install this part?")

[0443] Emotional information (e.g., "I'm nervous")

[0444] output:

[0445] JSON format data

[0446] json

[0447] {

[0448] "question": "How do I install this part?",

[0449] "emotion": "nervous"

[0450] }

[0451] Step 3:

[0452] The server analyzes the received question and sentiment information and sends it to the natural language processing engine. The data sent to the natural language processing engine is in the form of an API request.

[0453] input:

[0454] Question and sentiment data in JSON format

[0455] output:

[0456] API requests to the natural language processing engine

[0457] {

[0458] "prompt": "How do I install this part?",

[0459] "emotion": "nervous",

[0460] "max_tokens": 50

[0461] }

[0462] Step 4:

[0463] A natural language processing engine parses the API request and generates answers and sentiment feedback for the question, which is then sent back to the server in JSON format.

[0464] input:

[0465] API requests to the natural language processing engine

[0466] output:

[0467] Feedback based on questions and sentiment

[0468] json

[0469] {

[0470] "answer": "Here's how to install this part.",

[0471] "emotion_feedback": "Just relax and keep working"

[0472] }

[0473] Step 5:

[0474] The server then reformats the received answers and feedback and sends them to the electronic device, sending the data as an HTTP response.

[0475] input:

[0476] Answers and feedback from natural language processing engines

[0477] output:

[0478] Formatted answers and feedback

[0479] json

[0480] {

[0481] "answer": "Here's how to install this part.",

[0482] "emotion_feedback": "Just relax and keep working"

[0483] }

[0484] Step 6:

[0485] The device receives the HTTP response from the server and displays the answer and feedback on the user interface, allowing the user to review the displayed information and take action based on it.

[0486] input:

[0487] Formatted answers and feedback

[0488] output:

[0489] Answers and feedback displayed on the user interface

[0490] Answer: Here's how to install this part:

[0491] Feedback: Relax and keep working

[0492] These steps provide real-time, emotion-based, personalized feedback to user-entered questions.

[0493] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0494] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0495] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0496] [Second embodiment]

[0497] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0498] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0499] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0500] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0501] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0502] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0503] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0504] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0505] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0506] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0507] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0508] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0509] The system of the present invention allows users to input questions via electronic devices and provides answers to those questions in real time. The system begins operation when a user inputs and submits a question via a dedicated app or web browser.

[0510] First, a user opens a dedicated app or web browser on an electronic device such as a smartphone, tablet, or computer. The user enters a question in the question input field and presses the "Submit" button. For example, the user can enter "What is the answer to 1+1?"

[0511] Next, the device takes the question entered by the user, converts it to JSON format, and sends it to the server. The JSON format data looks like this:

[0512] json

[0513] {

[0514] "question": "What is the answer to 1+1?"

[0515] }

[0516] This JSON data is sent to the server as an HTTP POST request.

[0517] The server receives the HTTP POST request from the device, analyzes the data, and extracts the question body. The extracted question is converted into an API request to be sent to the natural language processing engine. For example, the following API request is made:

[0518] json

[0519] {

[0520] "prompt": "What is the answer to 1+1?",

[0521] "max_tokens": 10

[0522] }

[0523] The server sends this API request to the natural language processing engine.

[0524] The natural language processing engine analyzes the received question and generates the best answer, such as "The answer to 1 + 1 is 2," and the generated answer is sent back to the server in JSON format.

[0525] The server receives the response from the NLP engine, reformats it, and sends it back to the device. The reformatted JSON data looks like this:

[0526] json

[0527] {

[0528] "answer": "The answer to 1+1 is 2"

[0529] }

[0530] The server sends this answer to the terminal as an HTTP response.

[0531] Finally, the device receives the HTTP response from the server, extracts the answer data, and displays it on the user interface. The user can see the answer displayed on the device screen: "The answer to 1 + 1 is 2."

[0532] The system allows users to get answers to specific questions more quickly and efficiently than traditional tutoring services, automatically handling the entire process from submitting a question to displaying the answer, providing immediate feedback to users.

[0533] The processing flow will be explained below.

[0534] Step 1:

[0535] The user opens a dedicated app or web browser, enters "What is the answer to 1+1?" in the question input field, and presses the send button.

[0536] Step 2:

[0537] The terminal receives the question entered by the user and converts it into JSON format. The JSON data at this time looks like this:

[0538] json

[0539] {

[0540] "question": "What is the answer to 1+1?"

[0541] }

[0542] Step 3:

[0543] The device sends the JSON-encoded question to the server as an HTTP POST request, with the HTTP request header information also set appropriately.

[0544] Step 4:

[0545] The server receives an HTTP POST request from the device, extracts the JSON data from the request, and then extracts the question body.

[0546] Step 5:

[0547] The server prepares an appropriate API request to send the extracted question to the natural language processing engine, such as the following:

[0548] json

[0549] {

[0550] "prompt": "What is the answer to 1+1?",

[0551] "max_tokens": 10

[0552] }

[0553] Step 6:

[0554] The server sends this API request to the natural language processing engine, which makes the API request to a specific endpoint.

[0555] Step 7:

[0556] The natural language processing engine analyzes the received question and generates the most appropriate answer, for example, "The answer to 1 + 1 is 2."

[0557] Step 8:

[0558] The natural language processing engine returns the generated answer in JSON format to the server. The returned JSON data is as follows:

[0559] json

[0560] {

[0561] "response": "1+1 equals 2"

[0562] }

[0563] Step 9:

[0564] The server receives the response from the natural language processing engine, parses the received data, and reformats it if necessary.

[0565] Step 10:

[0566] The server will then send the reformatted response back to the device in JSON format, for example something like this:

[0567] json

[0568] {

[0569] "answer": "The answer to 1+1 is 2"

[0570] }

[0571] Step 11:

[0572] The terminal receives the HTTP response from the server, analyzes the received response data, and extracts the answer data.

[0573] Step 12:

[0574] The device displays the extracted answer on the user interface, and the user confirms the answer displayed on the screen: "The answer to 1 + 1 is 2."

[0575] This series of steps provides a quick and accurate answer to the user's question.

[0576] Example 1

[0577] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0578] Conventional information retrieval systems and question-answering systems have had issues with the speed and accuracy of the time it takes for a user to receive an answer after entering a question. In particular, in systems that provide answers in real time, the time it takes to analyze the question and generate an answer has been a problem. There has also been a demand for a method that allows users to easily enter questions and receive answers without using complex input formats.

[0579] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0580] In this invention, the server includes means for converting a question into JSON format and sending it to the server, means for the server to analyze the question received and send it to a natural language processing engine, and means for the natural language processing engine to generate an answer to the question and return it to the server. This allows users to easily input questions and receive quick and accurate answers.

[0581] A "user" is a person or entity that utilizes the system to enter questions and receive answers.

[0582] A "device" is an electronic device, such as a smartphone, tablet, or computer, that a user uses to enter a question.

[0583] "Means for converting a question into JSON format and sending it to a server" refers to means capable of converting a question entered by a user into JavaScript Object Notation format and transmitting it to a server via the Internet.

[0584] A "server" is a central processing unit responsible for receiving, parsing and sending queries submitted by users to a natural language processing engine.

[0585] A "means for analyzing a question" is a means capable of understanding the content of a question entered by a user and converting the data into an appropriate format.

[0586] A "natural language processing engine" is an engine that utilizes artificial intelligence techniques used to generate responses to questions.

[0587] An "API Request" is a request to send or receive data via an Application Programming Interface.

[0588] "Means for reformatting and transmitting a response to a terminal" refers to means capable of executing a process for reformatting a response obtained from a natural language processing engine and transmitting the response to a user's device.

[0589] A "user interface" is a screen or interactive environment with which a user directly interacts and sees input and output.

[0590] The system of the present invention allows a user to input a question via a device and provides an answer to the question in real time. The following components and steps are used to implement the invention.

[0591] First, a user uses a device such as a smartphone, tablet, or computer. They open a dedicated app or web browser and enter a question into the question input field. For example, they might enter, "What is the answer to 1+1?" At this point, they press the "Send" button.

[0592] The terminal takes the question entered by the user and converts it into JavaScript Object Notation (JSON) format. The converted JSON data is then sent to the server via the Internet as an HTTP POST request. For example, the converted JSON data might look like this:

[0593] json

[0594] {

[0595] "question": "What is the answer to 1+1?"

[0596] }

[0597] The server receives the HTTP POST request sent from the device and parses the JSON formatted data. It extracts the question body and converts it into an API request. This API request is sent to a natural language processing engine (e.g., a generative AI model). An example of an API request is as follows:

[0598] json

[0599] {

[0600] "prompt": "What is the answer to 1+1?",

[0601] "max_tokens": 10

[0602] }

[0603] The natural language processing engine analyzes the received API request and generates the best answer, such as "The answer to 1+1 is 2," and sends the answer back to the server in JSON format. The generated JSON data looks like this:

[0604] json

[0605] {

[0606] "answer": "The answer to 1+1 is 2"

[0607] }

[0608] When the server receives the response from the natural language processing engine, it reformats it and sends the reformatted data to the device as an HTTP response.

[0609] Finally, the device receives the HTTP response from the server and extracts the answer data in JSON format. The user interface displays "The answer to 1+1 is 2." This allows the user to check the generated answer on the device screen.

[0610] Examples of prompt sentences include the following:

[0611] "What's the answer to 3 x 4?"

[0612] "What is the capital of Japan?"

[0613] This system allows users to obtain answers to their questions quickly and accurately, and is expected to be particularly useful in the fields of education and information retrieval.

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

[0615] Step 1:

[0616] The user opens a dedicated app or web browser on the device, enters a question in the question input field on the user interface, and presses the "Submit" button. For example, they enter "What is the answer to 1+1?" The input is in text format.

[0617] Step 2:

[0618] The terminal takes the user's input and converts the text data into JSON format. The input data is "What is the answer to 1+1?" and the converted JSON format is as follows:

[0619] json

[0620] {

[0621] "question": "What is the answer to 1+1?"

[0622] }

[0623] This JSON data is sent to the server as an HTTP POST request.

[0624] Step 3:

[0625] The server receives an HTTP POST request. The received data is in JSON format, and the server parses it. It extracts the content of the "question" field from the input JSON data and then converts it to generate an API request to the natural language processing engine. The specific format of the API request is as follows:

[0626] json

[0627] {

[0628] "prompt": "What is the answer to 1+1?",

[0629] "max_tokens": 10

[0630] }

[0631] Step 4:

[0632] The server sends the generated API request to the natural language processing engine. After the API request is sent, the natural language processing engine receives the request and begins analyzing it. This analysis process generates the best answer to the input question, "What is the answer to 1+1?"

[0633] Step 5:

[0634] The natural language processing engine generates the best answer. The generated answer is "The answer to 1+1 is 2." This answer is returned to the server in JSON format. The format of the returned data is as follows:

[0635] json

[0636] {

[0637] "answer": "The answer to 1+1 is 2"

[0638] }

[0639] Step 6:

[0640] The server receives the response data returned from the natural language processing engine. It reformats the received data and sends it to the device. The reformatted data looks like this:

[0641] json

[0642] {

[0643] "answer": "The answer to 1+1 is 2"

[0644] }

[0645] The server sends this data to the terminal as an HTTP response.

[0646] Step 7:

[0647] The device analyzes the HTTP response received from the server and displays it on the user interface. Specifically, the text "The answer to 1 + 1 is 2" is displayed. The user can check the answer on the device screen.

[0648] This allows users to smoothly experience the entire process from input to display of the answer.

[0649] (Application example 1)

[0650] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0651] On modern online shopping sites, users have limited means to instantly resolve questions about products and services. This can lead to a poor user experience and a risk of losing purchasing motivation. Furthermore, slow response times in FAQ sections and customer support can lead to users abandoning the site. To solve these issues, a system is needed that allows users to ask questions in real time and receive immediate answers.

[0652] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0653] In this invention, the server includes means for a user to input a question via an electronic device, means for formatting the question and sending it to the server, means for the server to analyze the received question and send it to a natural language processing engine, means for the natural language processing engine to generate an answer to the question and send it back to the server, means for the server to reformat the answer received and send it to the electronic device, means for the electronic device to display the answer on a user interface, and chatbot software embedded in an online shopping site where users can ask questions about products and services in real time, thereby enabling users to quickly resolve their questions and smoothly progress through the purchasing process.

[0654] An "electronic device" is a hardware device used by a user to input or receive information, including, for example, a smartphone, tablet, or computer.

[0655] The "means for inputting a question" is a function that provides an interface for a user to input a question in text format and send it to the system.

[0656] A "server" is a central processing unit that receives, analyzes, processes data over a network, and transmits the results to other devices.

[0657] A "natural language processing engine" is software or an algorithm that analyzes human-written language data, understands its content, and generates appropriate answers.

[0658] A "prompt sentence" is a phrase that explicitly specifies a user's question and is input to a natural language processing engine.

[0659] "Chatbot software" is a program that automatically interacts with users and generates answers to their questions.

[0660] A "format" is a procedure or convention for converting and organizing questions and answers into a particular form.

[0661] "Real-time" refers to the fact that the time between when a user enters a question and when they receive a response is very short, with the response being almost instantaneous.

[0662] "User interface" refers to the screen or display that contains the visual and operational elements that allow a user to interact with a system.

[0663] An "online shopping site" is a website that offers products and services over the Internet and allows users to purchase them online.

[0664] An embodiment of the present invention is a system for enabling a user to ask a question about a product or service in real time and for providing an answer to the question immediately. The system of the present invention includes the following means.

[0665] First, a user opens a dedicated app or web browser on an electronic device such as a smartphone, tablet, or computer. The user enters a question in the question input field and presses the "Submit" button. For example, the user can enter "What is the refund policy for this product?"

[0666] The device receives the question entered by the user, converts it into JSON format, and sends it to the server. The JSON format data looks like this:

[0667] json

[0668] {

[0669] "question": "What is the refund policy for this item?"

[0670] }

[0671] This JSON data is sent to the server as an HTTP POST request.

[0672] The server parses the incoming HTTP POST request, extracts the question body, and converts it into an API request to send to the natural language processing engine. The API request looks like this:

[0673] json

[0674] {

[0675] "prompt": "What is the refund policy for this item?",

[0676] "max_tokens": 10

[0677] }

[0678] The server sends this API request to the natural language processing engine.

[0679] A natural language processing engine (such as OpenAI's GPT-3) analyzes the received question and generates the optimal answer, such as "The refund policy for this product is a full refund within 30 days," and the generated answer is sent back to the server in JSON format.

[0680] The server receives the response from the natural language processing engine, reformats it in a format that is easy for the user to understand, and sends it back to the device. The reformatted JSON data looks like this:

[0681] json

[0682] {

[0683] "answer": "This product's refund policy is a full refund within 30 days"

[0684] }

[0685] The server sends this answer to the terminal as an HTTP response.

[0686] Finally, the device receives the HTTP response from the server, extracts the answer data, and displays it on the user interface. The user can see the answer displayed on the device screen: "The refund policy for this product is a full refund within 30 days."

[0687] The system of the present invention features the incorporation of chatbot software into an online shopping site, allowing users to quickly resolve their questions and smoothly progress through the purchasing process. An example of a specific prompt sentence is "What is the refund policy for this product?"

[0688] The hardware used includes smartphones, tablets, and computers, and the software used includes React and Vue.js for the front end, Python, Flask, and Django for the back end, HTTP and REST for API communication, and OpenAI GPT-3 as a natural language processing engine. These systems improve the user experience and enable quick and efficient resolution of queries.

[0689] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0690] Step 1:

[0691] A user opens a dedicated app or web browser on a smartphone, tablet, or computer, enters a question, and presses the "Send" button. The input is the text data of the question. For example, the user might enter, "What is the refund policy for this product?" The output is the text question data sent from the device to the server.

[0692] Step 2:

[0693] The terminal converts the question data entered by the user into JSON format. The input is a text question, and the data processing involves converting this text into the following JSON format:

[0694] json

[0695] {

[0696] "question": "What is the refund policy for this item?"

[0697] }

[0698] The output is the question data in JSON format.

[0699] Step 3:

[0700] The terminal sends JSON-formatted question data to the server as an HTTP POST request. The input is JSON-formatted question data, and the data processing involves creating an HTTP request. The output is the HTTP request sent to the server.

[0701] Step 4:

[0702] The server parses the received HTTP POST request and extracts the question body. The input is the HTTP request, and the data operation is the parsing of JSON data. The output is the extracted question body. For example, the extracted sentence is "What is the refund policy for this product?"

[0703] Step 5:

[0704] The server converts the extracted question text into an API request to send to the natural language processing engine. The input is the question text, and the data processing involves converting it into the API request format as follows:

[0705] json

[0706] {

[0707] "prompt": "What is the refund policy for this item?",

[0708] "max_tokens": 10

[0709] }

[0710] The output is data in the form of an API request.

[0711] Step 6:

[0712] The server sends data in the form of an API request to the natural language processing engine. Generative AI models used include OpenAI GPT-3. The input is the API request data, and data processing involves sending an HTTP request. The output is the response data returned by the natural language processing engine.

[0713] Step 7:

[0714] The natural language processing engine generates an answer based on the question it receives. The input is a question in the form of an API request, and the data calculation is the generation of an answer by the NLP engine. For example, if the input question is "What is the refund policy for this product?", the generated answer will be "The refund policy for this product is a full refund within 30 days." The output is the answer data in JSON format.

[0715] Step 8:

[0716] The server receives the response data returned by the natural language processing engine, reformats it, and sends it to the terminal. The input is the response data in JSON format, and the data processing is to convert it into the final JSON format as shown below:

[0717] json

[0718] {

[0719] "answer": "This product's refund policy is a full refund within 30 days"

[0720] }

[0721] The output is the reformatted JSON response data.

[0722] Step 9:

[0723] The terminal analyzes the JSON formatted response data received from the server and displays it on the user interface. The input is the JSON formatted response data, and the data operation is data binding to the UI component. The output is the response displayed on the user interface. The user can confirm the response displayed on the screen of their electronic device, which states, "The refund policy for this product is a full refund within 30 days."

[0724] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0725] The system of the present invention allows a user to input a question via an electronic device and not only provides an answer to the question in real time, but also recognizes the user's emotions and provides more appropriate feedback.

[0726] The user opens a dedicated app or web browser on an electronic device such as a smartphone, tablet, or computer. The user enters "What is the answer to 1+1?" in the question input field and presses the send button. The emotion engine analyzes the user's emotions from this input and connects that emotional information to the system.

[0727] The device converts the question entered by the user into JSON format and emotional information, and sends it to the server. The JSON format data looks like this:

[0728] json

[0729] {

[0730] "question": "What is the answer to 1+1?",

[0731] "emotion": "neutral"

[0732] }

[0733] This JSON data and emotion information is sent to the server as an HTTP POST request.

[0734] The server receives the HTTP POST request from the device, analyzes the data, and extracts the question body and sentiment information. The extracted question and sentiment information are converted into an API request to be sent to the natural language processing engine. For example, make the following API request:

[0735] json

[0736] {

[0737] "prompt": "What is the answer to 1+1?",

[0738] "emotion": "neutral",

[0739] "max_tokens": 10

[0740] }

[0741] The server sends this API request to the natural language processing engine.

[0742] The natural language processing engine analyzes the received question and sentiment information to generate the optimal answer, such as "The answer to 1 + 1 is 2," and returns the answer in JSON format to the server.

[0743] The server receives the response from the NLP engine, reformats it, and sends it back to the device. The reformatted JSON data looks like this:

[0744] json

[0745] {

[0746] "answer": "The answer to 1+1 is 2"

[0747] }

[0748] The server sends this response data to the terminal as an HTTP response.

[0749] The device receives the HTTP response from the server, extracts the answer data and emotional feedback, and displays it on the user interface. The user can then respond based on the feedback. Along with the answer "The answer to 1 + 1 is 2," emotional feedback such as "You look happy!" is also displayed.

[0750] This system not only provides answers to specific questions more quickly and efficiently than traditional tutoring services, but also provides personalized feedback based on the user's emotions. The system automatically handles the entire process from submitting a question to displaying an answer based on the user's emotions, providing users with a more personalized service faster.

[0751] The processing flow will be explained below.

[0752] Step 1:

[0753] The user opens a dedicated app or web browser, enters "What is the answer to 1+1?" in the question input field, and presses the send button.

[0754] Step 2:

[0755] The terminal acquires the question entered by the user and sends the question content to the emotion engine.

[0756] Step 3:

[0757] The emotion engine analyzes the question and recognizes the user's emotion. In this case, the emotion engine determines the emotion as "neutral."

[0758] Step 4:

[0759] The device converts the acquired question and the recognition result from the emotion engine into JSON format. The JSON data at this time looks like this:

[0760] json

[0761] {

[0762] "question": "What is the answer to 1+1?",

[0763] "emotion": "neutral"

[0764] }

[0765] Step 5:

[0766] The device sends JSON format data to the server as an HTTP POST request.

[0767] Step 6:

[0768] The server receives the HTTP POST request from the device, analyzes the data, and extracts the question text and sentiment information.

[0769] Step 7:

[0770] The server converts the extracted question and sentiment information into an API request to send to the natural language processing engine. An example of an API request is as follows:

[0771] json

[0772] {

[0773] "prompt": "What is the answer to 1+1?",

[0774] "emotion": "neutral",

[0775] "max_tokens": 10

[0776] }

[0777] Step 8:

[0778] The server sends this API request to the natural language processing engine.

[0779] Step 9:

[0780] The natural language processing engine analyzes the received question and sentiment information to generate the most appropriate answer, such as "The answer to 1 + 1 is 2."

[0781] Step 10:

[0782] The natural language processing engine returns the generated answer in JSON format to the server. The returned JSON data is as follows:

[0783] json

[0784] {

[0785] "response": "1+1 equals 2"

[0786] }

[0787] Step 11:

[0788] The server receives the response from the natural language processing engine, reformats it if necessary, and sends the reformatted data to the device in the following JSON format:

[0789] json

[0790] {

[0791] "answer": "The answer to 1+1 is 2",

[0792] "emotion_feedback": "Thank you!"

[0793] }

[0794] Step 12:

[0795] The device receives the HTTP response from the server and extracts the response data and emotional feedback.

[0796] Step 13:

[0797] The device displays the extracted answer and emotional feedback on the user interface. For example, the answer "The answer to 1+1 is 2" is displayed along with emotional feedback such as "Thank you!"

[0798] This series of steps allows users to not only get a quick and appropriate answer to their question, but also receive feedback based on their emotions.

[0799] Example 2

[0800] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0801] Conventional question-answering systems only provide answers to questions entered by the user, and are therefore unable to provide feedback that takes into account the user's emotional state. This has resulted in a lack of individualized responses and personalized services for users. Furthermore, there has been no system that can analyze a user's emotions and provide optimal answers based on those emotions, making it difficult to improve the user experience.

[0802] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for a user to input a question and emotion information, a means for converting the question and emotion information into JSON format, and a means for transmitting the question and emotion information to a natural language processing engine and generating an answer. This makes it possible to provide an optimal answer based on the user's emotion.

[0803] "User" refers to a person who uses the system to enter questions and receive answers.

[0804] "Electronic device" refers to a device, such as a smartphone, tablet, or computer, that a user uses to enter a question.

[0805] A "question" refers to a question that a user asks the system.

[0806] "Emotion analysis engine" refers to software or algorithms that analyze a user's emotional state based on questions entered by the user.

[0807] "Emotion information" refers to data that represents the emotional state analyzed by the emotion analysis engine from the user's question.

[0808] "Format" refers to a specific data format for sending questions and emotion information to the server.

[0809] "Server" refers to a computer system that analyzes data received from a terminal and communicates with a natural language processing engine to generate an answer.

[0810] A "natural language processing engine" refers to software or algorithms that generate appropriate answers based on input questions and emotional information.

[0811] "Answer" refers to information generated by a natural language processing engine in response to a user's question.

[0812] "Reformatting" refers to the process of converting the answers received from the natural language processing engine into a format that is easy for the user to understand.

[0813] "User interface" refers to the graphical display area and operation portion through which a user interacts with a system.

[0814] "Emotional feedback" refers to additional feedback or comments provided based on the user's emotional information.

[0815] "JSON format" refers to a lightweight data exchange format for expressing data in text format.

[0816] "API Request" means a communication request to send a question and sentiment information to a natural language processing engine and receive a response.

[0817] The present invention provides a system that allows a user to input a question using an electronic device and receives feedback based on the answer and emotions to the question. Specifically, a user uses an electronic device such as a smartphone, tablet, or computer and opens a dedicated application or web browser. When the user inputs a question and presses the send button, the system operates in the following steps.

[0818] First, the device receives a question entered by the user. At this time, the question is analyzed in real time by a sentiment analysis engine to obtain the user's emotional information. This sentiment analysis uses a general algorithm for identifying emotions from voice or text. For example, the sentiment analysis API of IBM Watson or Microsoft Azure can be considered.

[0819] The device then converts the question and emotion information into JSON format data, which makes it easier to transmit and analyze the data, and sends the generated JSON data to the server as an HTTP POST request.

[0820] The server analyzes the HTTP POST request received from the device and extracts the question and sentiment information separately. Based on this extracted data, the server sends an API request to a natural language processing engine. The natural language processing engine uses advanced text analysis algorithms such as OpenAI GPT-3 and Google BERT. This engine analyzes the user's question and sentiment information and generates the optimal answer.

[0821] The generated answer is sent back to the server, which then reformats it and sends it to the device. The reformatted answer data is displayed to the user on a user interface. The user is provided with the answer to their question as well as sentiment-based feedback, allowing them to get a more personalized answer quickly.

[0822] Specific examples

[0823] If a user types "What is the answer to 1+1?", the sentiment analysis engine will recognize the user's sentiment as "neutral." It will then generate JSON data containing the following:

[0824] json

[0825] {

[0826] "question": "What is the answer to 1+1?",

[0827] "emotion": "neutral"

[0828] }

[0829] This data is sent to the server, which extracts the question "What is the answer to 1+1?" and the emotion "neutral." The following prompt is sent to the natural language processing engine:

[0830] "What is the answer to 1 + 1?" (Neutral emotion, maximum tokens: 10)

[0831] The natural language processing engine responds with "1 + 1 equals 2," which is then reformatted and sent to the device, where it displays the specific answer to the question along with feedback such as "You look happy!" This allows users to get answers to their questions quickly and efficiently, while also receiving personalized feedback based on their emotions.

[0832] Unlike conventional question-answering systems, this system provides personalized feedback based on the user's emotions, improving the user experience.

[0833] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0834] Step 1: User enters question

[0835] A user opens a dedicated application or web browser on a smartphone, tablet, or computer, enters a question in the question input field, for example, "What is the answer to 1+1?", and presses the submit button. This action sends the entered question to the system.

[0836] Input: A question typed by the user (e.g., "What is the answer to 1+1?")

[0837] Output: Question data entered by the user

[0838] Step 2: The device analyzes emotions

[0839] The device sends the question received from the user to a sentiment analysis engine, which analyzes the user's sentiment. This analysis uses an algorithm that extracts sentiment from text, and emotional information such as "neutral" is obtained.

[0840] Input: User question data

[0841] Output: User question data and sentiment information (e.g., "neutral")

[0842] Step 3: The device converts the question and emotion information into JSON format.

[0843] The device converts the user's question and the analyzed emotion information into JSON format. Specifically, it generates the following JSON data:

[0844] json

[0845] {

[0846] "question": "What is the answer to 1+1?",

[0847] "emotion": "neutral"

[0848] }

[0849] Input: User question data and emotion information

[0850] Output: JSON format data

[0851] Step 4: The device sends the data to the server

[0852] The device sends the generated JSON data as an HTTP POST request to the server, which then uses a network communication library to send the data.

[0853] Input: JSON format data

[0854] Output: HTTP POST request sent to the server

[0855] Step 5: The server receives and parses the data

[0856] The server analyzes the HTTP POST request received from the device, extracts JSON data from the request body, and then extracts the question and sentiment information from this data separately and passes them on to the next stage of processing.

[0857] Input: HTTP POST request

[0858] Output: Extracted question and sentiment information

[0859] Step 6: The server sends a request to the natural language processing engine

[0860] The server converts the extracted question and sentiment information into an API request to be sent to the natural language processing engine, for example in the following format:

[0861] Prompt: "What is the answer to 1 + 1?" (Emotion: Neutral, Max Tokens: 10)

[0862] Input: Extracted question and sentiment information

[0863] Output: API request to the natural language processing engine

[0864] Step 7: The natural language processing engine generates the answer

[0865] The natural language processing engine analyzes the received question and emotional information to generate the optimal answer, such as "The answer to 1 + 1 is 2."

[0866] Input: API request

[0867] Output: Generated response data

[0868] Step 8: Server Reformats Response Data

[0869] The server reformats the response data received from the natural language processing engine and converts it into a format that is easy for the user to understand, for example, into a JSON format like this:

[0870] json

[0871] {

[0872] "answer": "The answer to 1+1 is 2"

[0873] }

[0874] Input: Generated response data

[0875] Output: Reformatted response data

[0876] Step 9: The server sends the reformatted data to the device

[0877] The server then sends the reformatted response data to the terminal as an HTTP response. The data is then sent to the terminal via network communication.

[0878] Input: Reformatted response data

[0879] Output: Sending HTTP response to the terminal

[0880] Step 10: Your device displays your answer and emotional feedback

[0881] The device analyzes the HTTP response received from the server and displays the answer and emotional feedback on the user interface. For example, the answer "The answer to 1 + 1 is 2" and the emotional feedback "You look happy!" are displayed.

[0882] Input: HTTP response from the server

[0883] Output: Display of answer and emotional feedback to the user

[0884] (Application example 2)

[0885] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0886] In conventional systems, even if a user inputs a question, they are unable to receive emotional feedback and are simply provided with a mechanical answer. This means that personalized feedback based on the user's emotions is not provided, which can lead to a decrease in work efficiency and employee satisfaction, especially in stressful environments such as factories.

[0887] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for analyzing a received question and analyzing the user's emotion via an emotion analysis engine, means for transmitting the question and the analyzed emotion information to a natural language processing engine, and means for the natural language processing engine to generate an answer to the question and feedback based on the emotion and return it to the server. This makes it possible to provide personalized feedback based on the user's emotion in real time.

[0888] 1. "Means for users to input questions via electronic devices" refers to devices that provide an interface for users to input questions in natural language using electronic devices such as smartphones, tablets, and computers.

[0889] 2. "Means for transmitting the question to the server in a format" refers to the protocol and hardware that converts the input question into an appropriate data format (e.g., JSON format) and transmits it to the server via a network.

[0890] 3. "Sentiment analysis engine" refers to algorithms and software that analyze and extract user emotions from input text data.

[0891] 4. A "natural language processing engine" is an algorithm or software that analyzes text data, performs natural language processing, and generates answers to questions.

[0892] 5. "Means for reformatting the response received by the server and transmitting it to the electronic device" refers to protocols and hardware that receive the response returned from the natural language processing engine, convert it into an appropriate display format, and transmit it to the electronic device.

[0893] 6. "Emotion-based feedback" refers to additional comments or information provided based on an analysis of a user's emotions to generate a personalized response to the user.

[0894] 7. "User interface" means the screen and operating environment through which a user can input and confirm questions via an electronic device.

[0895] 8. "API Request" is a protocol for sending specific data to another software or service and receiving the processing results.

[0896] A system for implementing the present invention allows a user to input a question via an electronic device and receive a real-time answer. The system uses a sentiment analysis engine to analyze the user's sentiment, and a natural language processing engine to provide personalized feedback based on the sentiment.

[0897] The overall approach of the system is as follows: First, the user inputs a question using a smartphone, tablet, smart glasses, or computer. The input question is converted into an appropriate data format, such as JSON, and sent to the server. At this stage, the user's emotional information is also sent along with the question data.

[0898] The server analyzes the received question and emotion data and executes the user's emotion through an emotion analysis engine. As a result, the question and emotion information are obtained. The server then sends this question and emotion information to a natural language processing engine. The natural language processing engine generates an answer to the question and also generates feedback based on the user's emotion. If the user is feeling a certain emotion, this feedback provides a response appropriate to that emotion.

[0899] The generated answers and emotional feedback are then sent back to the server, which then reformats and sends the data to the user's electronic device, where the user can finally view the answers to the questions and the emotional feedback.

[0900] For example, if a factory worker uses smart glasses to ask, "Please tell me how to install this part," their emotions will also be analyzed. If the system detects that the worker is nervous, it will display feedback such as, "Here's how to install this part. Please relax and continue working." In this way, personalized feedback is provided according to the user's emotions.

[0901] An example of a prompt for the generative AI model is as follows:

[0902] "Please tell me how to install this part" is entered.

[0903] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0904] Step 1:

[0905] A user inputs a question via an electronic device. The electronic device receives the input as text data and analyzes the user's emotions using a sentiment analysis engine. This analysis provides the question entered by the user and emotional information when entering the question.

[0906] input:

[0907] A text question entered by the user (e.g., "How do I install this part?")

[0908] The user's emotional state

[0909] output:

[0910] Question text

[0911] Emotional information (e.g., "I'm nervous")

[0912] Step 2:

[0913] The device formats the question and emotion information into JSON format and sends it to the server using an HTTP POST request.

[0914] input:

[0915] Question text (e.g., "How do I install this part?")

[0916] Emotional information (e.g., "I'm nervous")

[0917] output:

[0918] JSON format data

[0919] json

[0920] {

[0921] "question": "How do I install this part?",

[0922] "emotion": "nervous"

[0923] }

[0924] Step 3:

[0925] The server analyzes the received question and sentiment information and sends it to the natural language processing engine. The data sent to the natural language processing engine is in the form of an API request.

[0926] input:

[0927] Question and sentiment data in JSON format

[0928] output:

[0929] API requests to the natural language processing engine

[0930] {

[0931] "prompt": "How do I install this part?",

[0932] "emotion": "nervous",

[0933] "max_tokens": 50

[0934] }

[0935] Step 4:

[0936] A natural language processing engine parses the API request and generates answers and sentiment feedback for the question, which is then sent back to the server in JSON format.

[0937] input:

[0938] API requests to the natural language processing engine

[0939] output:

[0940] Feedback based on questions and sentiment

[0941] json

[0942] {

[0943] "answer": "Here's how to install this part.",

[0944] "emotion_feedback": "Just relax and keep working"

[0945] }

[0946] Step 5:

[0947] The server then reformats the received answers and feedback and sends them to the electronic device, sending the data as an HTTP response.

[0948] input:

[0949] Answers and feedback from natural language processing engines

[0950] output:

[0951] Formatted answers and feedback

[0952] json

[0953] {

[0954] "answer": "Here's how to install this part.",

[0955] "emotion_feedback": "Just relax and keep working"

[0956] }

[0957] Step 6:

[0958] The device receives the HTTP response from the server and displays the answer and feedback on the user interface, allowing the user to review the displayed information and take action based on it.

[0959] input:

[0960] Formatted answers and feedback

[0961] output:

[0962] Answers and feedback displayed on the user interface

[0963] Answer: Here's how to install this part:

[0964] Feedback: Relax and keep working

[0965] These steps provide real-time, emotion-based, personalized feedback to user-entered questions.

[0966] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0967] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0968] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0969] [Third embodiment]

[0970] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0971] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0972] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0973] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0974] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0975] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0976] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0977] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0978] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0979] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0980] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0981] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0982] The system of the present invention allows users to input questions via electronic devices and provides answers to those questions in real time. The system begins operation when a user inputs and submits a question via a dedicated app or web browser.

[0983] First, a user opens a dedicated app or web browser on an electronic device such as a smartphone, tablet, or computer. The user enters a question in the question input field and presses the "Submit" button. For example, the user can enter "What is the answer to 1+1?"

[0984] Next, the device takes the question entered by the user, converts it to JSON format, and sends it to the server. The JSON format data looks like this:

[0985] json

[0986] {

[0987] "question": "What is the answer to 1+1?"

[0988] }

[0989] This JSON data is sent to the server as an HTTP POST request.

[0990] The server receives the HTTP POST request from the device, analyzes the data, and extracts the question body. The extracted question is converted into an API request to be sent to the natural language processing engine. For example, the following API request is made:

[0991] json

[0992] {

[0993] "prompt": "What is the answer to 1+1?",

[0994] "max_tokens": 10

[0995] }

[0996] The server sends this API request to the natural language processing engine.

[0997] The natural language processing engine analyzes the received question and generates the best answer, such as "The answer to 1 + 1 is 2," and the generated answer is sent back to the server in JSON format.

[0998] The server receives the response from the NLP engine, reformats it, and sends it back to the device. The reformatted JSON data looks like this:

[0999] json

[1000] {

[1001] "answer": "The answer to 1+1 is 2"

[1002] }

[1003] The server sends this answer to the terminal as an HTTP response.

[1004] Finally, the device receives the HTTP response from the server, extracts the answer data, and displays it on the user interface. The user can see the answer displayed on the device screen: "The answer to 1 + 1 is 2."

[1005] The system allows users to get answers to specific questions more quickly and efficiently than traditional tutoring services, automatically handling the entire process from submitting a question to displaying the answer, providing immediate feedback to users.

[1006] The processing flow will be explained below.

[1007] Step 1:

[1008] The user opens a dedicated app or web browser, enters "What is the answer to 1+1?" in the question input field, and presses the send button.

[1009] Step 2:

[1010] The terminal receives the question entered by the user and converts it into JSON format. The JSON data at this time looks like this:

[1011] json

[1012] {

[1013] "question": "What is the answer to 1+1?"

[1014] }

[1015] Step 3:

[1016] The device sends the JSON-encoded question to the server as an HTTP POST request, with the HTTP request header information also set appropriately.

[1017] Step 4:

[1018] The server receives an HTTP POST request from the device, extracts the JSON data from the request, and then extracts the question body.

[1019] Step 5:

[1020] The server prepares an appropriate API request to send the extracted question to the natural language processing engine, such as the following:

[1021] json

[1022] {

[1023] "prompt": "What is the answer to 1+1?",

[1024] "max_tokens": 10

[1025] }

[1026] Step 6:

[1027] The server sends this API request to the natural language processing engine, which makes the API request to a specific endpoint.

[1028] Step 7:

[1029] The natural language processing engine analyzes the received question and generates the most appropriate answer, for example, "The answer to 1 + 1 is 2."

[1030] Step 8:

[1031] The natural language processing engine returns the generated answer in JSON format to the server. The returned JSON data is as follows:

[1032] json

[1033] {

[1034] "response": "1+1 equals 2"

[1035] }

[1036] Step 9:

[1037] The server receives the response from the natural language processing engine, parses the received data, and reformats it if necessary.

[1038] Step 10:

[1039] The server will then send the reformatted response back to the device in JSON format, for example something like this:

[1040] json

[1041] {

[1042] "answer": "The answer to 1+1 is 2"

[1043] }

[1044] Step 11:

[1045] The terminal receives the HTTP response from the server, analyzes the received response data, and extracts the answer data.

[1046] Step 12:

[1047] The device displays the extracted answer on the user interface, and the user confirms the answer displayed on the screen: "The answer to 1 + 1 is 2."

[1048] This series of steps provides a quick and accurate answer to the user's question.

[1049] Example 1

[1050] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1051] Conventional information retrieval systems and question-answering systems have had issues with the speed and accuracy of the time it takes for a user to receive an answer after entering a question. In particular, in systems that provide answers in real time, the time it takes to analyze the question and generate an answer has been a problem. There has also been a demand for a method that allows users to easily enter questions and receive answers without using complex input formats.

[1052] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1053] In this invention, the server includes means for converting a question into JSON format and sending it to the server, means for the server to analyze the question received and send it to a natural language processing engine, and means for the natural language processing engine to generate an answer to the question and return it to the server. This allows users to easily input questions and receive quick and accurate answers.

[1054] A "user" is a person or entity that utilizes the system to enter questions and receive answers.

[1055] A "device" is an electronic device, such as a smartphone, tablet, or computer, that a user uses to enter a question.

[1056] "Means for converting a question into JSON format and sending it to a server" refers to means capable of converting a question entered by a user into JavaScript Object Notation format and transmitting it to a server via the Internet.

[1057] A "server" is a central processing unit responsible for receiving, parsing and sending queries submitted by users to a natural language processing engine.

[1058] A "means for analyzing a question" is a means capable of understanding the content of a question entered by a user and converting the data into an appropriate format.

[1059] A "natural language processing engine" is an engine that utilizes artificial intelligence techniques used to generate responses to questions.

[1060] An "API Request" is a request to send or receive data via an Application Programming Interface.

[1061] "Means for reformatting and transmitting a response to a terminal" refers to means capable of executing a process for reformatting a response obtained from a natural language processing engine and transmitting the response to a user's device.

[1062] A "user interface" is a screen or interactive environment with which a user directly interacts and sees input and output.

[1063] The system of the present invention allows a user to input a question via a device and provides an answer to the question in real time. The following components and steps are used to implement the invention.

[1064] First, a user uses a device such as a smartphone, tablet, or computer. They open a dedicated app or web browser and enter a question into the question input field. For example, they might enter, "What is the answer to 1+1?" At this point, they press the "Send" button.

[1065] The terminal takes the question entered by the user and converts it into JavaScript Object Notation (JSON) format. The converted JSON data is then sent to the server via the Internet as an HTTP POST request. For example, the converted JSON data might look like this:

[1066] json

[1067] {

[1068] "question": "What is the answer to 1+1?"

[1069] }

[1070] The server receives the HTTP POST request sent from the device and parses the JSON formatted data. It extracts the question body and converts it into an API request. This API request is sent to a natural language processing engine (e.g., a generative AI model). An example of an API request is as follows:

[1071] json

[1072] {

[1073] "prompt": "What is the answer to 1+1?",

[1074] "max_tokens": 10

[1075] }

[1076] The natural language processing engine analyzes the received API request and generates the best answer, such as "The answer to 1+1 is 2," and sends the answer back to the server in JSON format. The generated JSON data looks like this:

[1077] json

[1078] {

[1079] "answer": "The answer to 1+1 is 2"

[1080] }

[1081] When the server receives the response from the natural language processing engine, it reformats it and sends the reformatted data to the device as an HTTP response.

[1082] Finally, the device receives the HTTP response from the server and extracts the answer data in JSON format. The user interface displays "The answer to 1+1 is 2." This allows the user to check the generated answer on the device screen.

[1083] Examples of prompt sentences include the following:

[1084] "What's the answer to 3 x 4?"

[1085] "What is the capital of Japan?"

[1086] This system allows users to obtain answers to their questions quickly and accurately, and is expected to be particularly useful in the fields of education and information retrieval.

[1087] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1088] Step 1:

[1089] The user opens a dedicated app or web browser on the device, enters a question in the question input field on the user interface, and presses the "Submit" button. For example, they enter "What is the answer to 1+1?" The input is in text format.

[1090] Step 2:

[1091] The terminal takes the user's input and converts the text data into JSON format. The input data is "What is the answer to 1+1?" and the converted JSON format is as follows:

[1092] json

[1093] {

[1094] "question": "What is the answer to 1+1?"

[1095] }

[1096] This JSON data is sent to the server as an HTTP POST request.

[1097] Step 3:

[1098] The server receives an HTTP POST request. The received data is in JSON format, and the server parses it. It extracts the content of the "question" field from the input JSON data and then converts it to generate an API request to the natural language processing engine. The specific format of the API request is as follows:

[1099] json

[1100] {

[1101] "prompt": "What is the answer to 1+1?",

[1102] "max_tokens": 10

[1103] }

[1104] Step 4:

[1105] The server sends the generated API request to the natural language processing engine. After the API request is sent, the natural language processing engine receives the request and begins analyzing it. This analysis process generates the best answer to the input question, "What is the answer to 1+1?"

[1106] Step 5:

[1107] The natural language processing engine generates the best answer. The generated answer is "The answer to 1+1 is 2." This answer is returned to the server in JSON format. The format of the returned data is as follows:

[1108] json

[1109] {

[1110] "answer": "The answer to 1+1 is 2"

[1111] }

[1112] Step 6:

[1113] The server receives the response data returned from the natural language processing engine. It reformats the received data and sends it to the device. The reformatted data looks like this:

[1114] json

[1115] {

[1116] "answer": "The answer to 1+1 is 2"

[1117] }

[1118] The server sends this data to the terminal as an HTTP response.

[1119] Step 7:

[1120] The device analyzes the HTTP response received from the server and displays it on the user interface. Specifically, the text "The answer to 1 + 1 is 2" is displayed. The user can check the answer on the device screen.

[1121] This allows users to smoothly experience the entire process from input to display of the answer.

[1122] (Application example 1)

[1123] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1124] On modern online shopping sites, users have limited means to instantly resolve questions about products and services. This can lead to a poor user experience and a risk of losing purchasing motivation. Furthermore, slow response times in FAQ sections and customer support can lead to users abandoning the site. To solve these issues, a system is needed that allows users to ask questions in real time and receive immediate answers.

[1125] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1126] In this invention, the server includes means for a user to input a question via an electronic device, means for formatting the question and sending it to the server, means for the server to analyze the received question and send it to a natural language processing engine, means for the natural language processing engine to generate an answer to the question and send it back to the server, means for the server to reformat the answer received and send it to the electronic device, means for the electronic device to display the answer on a user interface, and chatbot software embedded in an online shopping site where users can ask questions about products and services in real time, thereby enabling users to quickly resolve their questions and smoothly progress through the purchasing process.

[1127] An "electronic device" is a hardware device used by a user to input or receive information, including, for example, a smartphone, tablet, or computer.

[1128] The "means for inputting a question" is a function that provides an interface for a user to input a question in text format and send it to the system.

[1129] A "server" is a central processing unit that receives, analyzes, processes data over a network, and transmits the results to other devices.

[1130] A "natural language processing engine" is software or an algorithm that analyzes human-written language data, understands its content, and generates appropriate answers.

[1131] A "prompt sentence" is a phrase that explicitly specifies a user's question and is input to a natural language processing engine.

[1132] "Chatbot software" is a program that automatically interacts with users and generates answers to their questions.

[1133] A "format" is a procedure or convention for converting and organizing questions and answers into a particular form.

[1134] "Real-time" refers to the fact that the time between when a user enters a question and when they receive a response is very short, with the response being almost instantaneous.

[1135] "User interface" refers to the screen or display that contains the visual and operational elements that allow a user to interact with a system.

[1136] An "online shopping site" is a website that offers products and services over the Internet and allows users to purchase them online.

[1137] An embodiment of the present invention is a system for enabling a user to ask a question about a product or service in real time and for providing an answer to the question immediately. The system of the present invention includes the following means.

[1138] First, a user opens a dedicated app or web browser on an electronic device such as a smartphone, tablet, or computer. The user enters a question in the question input field and presses the "Submit" button. For example, the user can enter "What is the refund policy for this product?"

[1139] The device receives the question entered by the user, converts it into JSON format, and sends it to the server. The JSON format data looks like this:

[1140] json

[1141] {

[1142] "question": "What is the refund policy for this item?"

[1143] }

[1144] This JSON data is sent to the server as an HTTP POST request.

[1145] The server parses the incoming HTTP POST request, extracts the question body, and converts it into an API request to send to the natural language processing engine. The API request looks like this:

[1146] json

[1147] {

[1148] "prompt": "What is the refund policy for this item?",

[1149] "max_tokens": 10

[1150] }

[1151] The server sends this API request to the natural language processing engine.

[1152] A natural language processing engine (such as OpenAI's GPT-3) analyzes the received question and generates the optimal answer, such as "The refund policy for this product is a full refund within 30 days," and the generated answer is sent back to the server in JSON format.

[1153] The server receives the response from the natural language processing engine, reformats it in a format that is easy for the user to understand, and sends it back to the device. The reformatted JSON data looks like this:

[1154] json

[1155] {

[1156] "answer": "This product's refund policy is a full refund within 30 days"

[1157] }

[1158] The server sends this answer to the terminal as an HTTP response.

[1159] Finally, the device receives the HTTP response from the server, extracts the answer data, and displays it on the user interface. The user can see the answer displayed on the device screen: "The refund policy for this product is a full refund within 30 days."

[1160] The system of the present invention features the incorporation of chatbot software into an online shopping site, allowing users to quickly resolve their questions and smoothly progress through the purchasing process. An example of a specific prompt sentence is "What is the refund policy for this product?"

[1161] The hardware used includes smartphones, tablets, and computers, and the software used includes React and Vue.js for the front end, Python, Flask, and Django for the back end, HTTP and REST for API communication, and OpenAI GPT-3 as a natural language processing engine. These systems improve the user experience and enable quick and efficient resolution of queries.

[1162] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1163] Step 1:

[1164] A user opens a dedicated app or web browser on a smartphone, tablet, or computer, enters a question, and presses the "Send" button. The input is the text data of the question. For example, the user might enter, "What is the refund policy for this product?" The output is the text question data sent from the device to the server.

[1165] Step 2:

[1166] The terminal converts the question data entered by the user into JSON format. The input is a text question, and the data processing involves converting this text into the following JSON format:

[1167] json

[1168] {

[1169] "question": "What is the refund policy for this item?"

[1170] }

[1171] The output is the question data in JSON format.

[1172] Step 3:

[1173] The terminal sends JSON-formatted question data to the server as an HTTP POST request. The input is JSON-formatted question data, and the data processing involves creating an HTTP request. The output is the HTTP request sent to the server.

[1174] Step 4:

[1175] The server parses the received HTTP POST request and extracts the question body. The input is the HTTP request, and the data operation is the parsing of JSON data. The output is the extracted question body. For example, the extracted sentence is "What is the refund policy for this product?"

[1176] Step 5:

[1177] The server converts the extracted question text into an API request to send to the natural language processing engine. The input is the question text, and the data processing involves converting it into the API request format as follows:

[1178] json

[1179] {

[1180] "prompt": "What is the refund policy for this item?",

[1181] "max_tokens": 10

[1182] }

[1183] The output is data in the form of an API request.

[1184] Step 6:

[1185] The server sends data in the form of an API request to the natural language processing engine. Generative AI models used include OpenAI GPT-3. The input is the API request data, and data processing involves sending an HTTP request. The output is the response data returned by the natural language processing engine.

[1186] Step 7:

[1187] The natural language processing engine generates an answer based on the question it receives. The input is a question in the form of an API request, and the data calculation is the generation of an answer by the NLP engine. For example, if the input question is "What is the refund policy for this product?", the generated answer will be "The refund policy for this product is a full refund within 30 days." The output is the answer data in JSON format.

[1188] Step 8:

[1189] The server receives the response data returned by the natural language processing engine, reformats it, and sends it to the terminal. The input is the response data in JSON format, and the data processing is to convert it into the final JSON format as shown below:

[1190] json

[1191] {

[1192] "answer": "This product's refund policy is a full refund within 30 days"

[1193] }

[1194] The output is the reformatted JSON response data.

[1195] Step 9:

[1196] The terminal analyzes the JSON formatted response data received from the server and displays it on the user interface. The input is the JSON formatted response data, and the data operation is data binding to the UI component. The output is the response displayed on the user interface. The user can confirm the response displayed on the screen of their electronic device, which states, "The refund policy for this product is a full refund within 30 days."

[1197] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1198] The system of the present invention allows a user to input a question via an electronic device and not only provides an answer to the question in real time, but also recognizes the user's emotions and provides more appropriate feedback.

[1199] The user opens a dedicated app or web browser on an electronic device such as a smartphone, tablet, or computer. The user enters "What is the answer to 1+1?" in the question input field and presses the send button. The emotion engine analyzes the user's emotions from this input and connects that emotional information to the system.

[1200] The device converts the question entered by the user into JSON format and emotional information, and sends it to the server. The JSON format data looks like this:

[1201] json

[1202] {

[1203] "question": "What is the answer to 1+1?",

[1204] "emotion": "neutral"

[1205] }

[1206] This JSON data and emotion information is sent to the server as an HTTP POST request.

[1207] The server receives the HTTP POST request from the device, analyzes the data, and extracts the question body and sentiment information. The extracted question and sentiment information are converted into an API request to be sent to the natural language processing engine. For example, make the following API request:

[1208] json

[1209] {

[1210] "prompt": "What is the answer to 1+1?",

[1211] "emotion": "neutral",

[1212] "max_tokens": 10

[1213] }

[1214] The server sends this API request to the natural language processing engine.

[1215] The natural language processing engine analyzes the received question and sentiment information to generate the optimal answer, such as "The answer to 1 + 1 is 2," and returns the answer in JSON format to the server.

[1216] The server receives the response from the NLP engine, reformats it, and sends it back to the device. The reformatted JSON data looks like this:

[1217] json

[1218] {

[1219] "answer": "The answer to 1+1 is 2"

[1220] }

[1221] The server sends this response data to the terminal as an HTTP response.

[1222] The device receives the HTTP response from the server, extracts the answer data and emotional feedback, and displays it on the user interface. The user can then respond based on the feedback. Along with the answer "The answer to 1 + 1 is 2," emotional feedback such as "You look happy!" is also displayed.

[1223] This system not only provides answers to specific questions more quickly and efficiently than traditional tutoring services, but also provides personalized feedback based on the user's emotions. The system automatically handles the entire process from submitting a question to displaying an answer based on the user's emotions, providing users with a more personalized service faster.

[1224] The processing flow will be explained below.

[1225] Step 1:

[1226] The user opens a dedicated app or web browser, enters "What is the answer to 1+1?" in the question input field, and presses the send button.

[1227] Step 2:

[1228] The terminal acquires the question entered by the user and sends the question content to the emotion engine.

[1229] Step 3:

[1230] The emotion engine analyzes the question and recognizes the user's emotion. In this case, the emotion engine determines the emotion as "neutral."

[1231] Step 4:

[1232] The device converts the acquired question and the recognition result from the emotion engine into JSON format. The JSON data at this time looks like this:

[1233] json

[1234] {

[1235] "question": "What is the answer to 1+1?",

[1236] "emotion": "neutral"

[1237] }

[1238] Step 5:

[1239] The device sends JSON format data to the server as an HTTP POST request.

[1240] Step 6:

[1241] The server receives the HTTP POST request from the device, analyzes the data, and extracts the question text and sentiment information.

[1242] Step 7:

[1243] The server converts the extracted question and sentiment information into an API request to send to the natural language processing engine. An example of an API request is as follows:

[1244] json

[1245] {

[1246] "prompt": "What is the answer to 1+1?",

[1247] "emotion": "neutral",

[1248] "max_tokens": 10

[1249] }

[1250] Step 8:

[1251] The server sends this API request to the natural language processing engine.

[1252] Step 9:

[1253] The natural language processing engine analyzes the received question and sentiment information to generate the most appropriate answer, such as "The answer to 1 + 1 is 2."

[1254] Step 10:

[1255] The natural language processing engine returns the generated answer in JSON format to the server. The returned JSON data is as follows:

[1256] json

[1257] {

[1258] "response": "1+1 equals 2"

[1259] }

[1260] Step 11:

[1261] The server receives the response from the natural language processing engine, reformats it if necessary, and sends the reformatted data to the device in the following JSON format:

[1262] json

[1263] {

[1264] "answer": "The answer to 1+1 is 2",

[1265] "emotion_feedback": "Thank you!"

[1266] }

[1267] Step 12:

[1268] The device receives the HTTP response from the server and extracts the response data and emotional feedback.

[1269] Step 13:

[1270] The device displays the extracted answer and emotional feedback on the user interface. For example, the answer "The answer to 1+1 is 2" is displayed along with emotional feedback such as "Thank you!"

[1271] This series of steps allows users to not only get a quick and appropriate answer to their question, but also receive feedback based on their emotions.

[1272] Example 2

[1273] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1274] Conventional question-answering systems only provide answers to questions entered by the user, and are therefore unable to provide feedback that takes into account the user's emotional state. This has resulted in a lack of individualized responses and personalized services for users. Furthermore, there has been no system that can analyze a user's emotions and provide optimal answers based on those emotions, making it difficult to improve the user experience.

[1275] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for a user to input a question and emotion information, a means for converting the question and emotion information into JSON format, and a means for transmitting the question and emotion information to a natural language processing engine and generating an answer. This makes it possible to provide an optimal answer based on the user's emotion.

[1276] "User" refers to a person who uses the system to enter questions and receive answers.

[1277] "Electronic device" refers to a device, such as a smartphone, tablet, or computer, that a user uses to enter a question.

[1278] A "question" refers to a question that a user asks the system.

[1279] "Emotion analysis engine" refers to software or algorithms that analyze a user's emotional state based on questions entered by the user.

[1280] "Emotion information" refers to data that represents the emotional state analyzed by the emotion analysis engine from the user's question.

[1281] "Format" refers to a specific data format for sending questions and emotion information to the server.

[1282] "Server" refers to a computer system that analyzes data received from a terminal and communicates with a natural language processing engine to generate an answer.

[1283] A "natural language processing engine" refers to software or algorithms that generate appropriate answers based on input questions and emotional information.

[1284] "Answer" refers to information generated by a natural language processing engine in response to a user's question.

[1285] "Reformatting" refers to the process of converting the answers received from the natural language processing engine into a format that is easy for the user to understand.

[1286] "User interface" refers to the graphical display area and operation portion through which a user interacts with a system.

[1287] "Emotional feedback" refers to additional feedback or comments provided based on the user's emotional information.

[1288] "JSON format" refers to a lightweight data exchange format for expressing data in text format.

[1289] "API Request" means a communication request to send a question and sentiment information to a natural language processing engine and receive a response.

[1290] The present invention provides a system that allows a user to input a question using an electronic device and receives feedback based on the answer and emotions to the question. Specifically, a user uses an electronic device such as a smartphone, tablet, or computer and opens a dedicated application or web browser. When the user inputs a question and presses the send button, the system operates in the following steps.

[1291] First, the device receives a question entered by the user. At this time, the question is analyzed in real time by a sentiment analysis engine to obtain the user's emotional information. This sentiment analysis uses a general algorithm for identifying emotions from voice or text. For example, the sentiment analysis API of IBM Watson or Microsoft Azure can be considered.

[1292] The device then converts the question and emotion information into JSON format data, which makes it easier to transmit and analyze the data, and sends the generated JSON data to the server as an HTTP POST request.

[1293] The server analyzes the HTTP POST request received from the device and extracts the question and sentiment information separately. Based on this extracted data, the server sends an API request to a natural language processing engine. The natural language processing engine uses advanced text analysis algorithms such as OpenAI GPT-3 and Google BERT. This engine analyzes the user's question and sentiment information and generates the optimal answer.

[1294] The generated answer is sent back to the server, which then reformats it and sends it to the device. The reformatted answer data is displayed to the user on a user interface. The user is provided with the answer to their question as well as sentiment-based feedback, allowing them to get a more personalized answer quickly.

[1295] Specific examples

[1296] If a user types "What is the answer to 1+1?", the sentiment analysis engine will recognize the user's sentiment as "neutral." It will then generate JSON data containing the following:

[1297] json

[1298] {

[1299] "question": "What is the answer to 1+1?",

[1300] "emotion": "neutral"

[1301] }

[1302] This data is sent to the server, which extracts the question "What is the answer to 1+1?" and the emotion "neutral." The following prompt is sent to the natural language processing engine:

[1303] "What is the answer to 1 + 1?" (Neutral emotion, maximum tokens: 10)

[1304] The natural language processing engine responds with "1 + 1 equals 2," which is then reformatted and sent to the device, where it displays the specific answer to the question along with feedback such as "You look happy!" This allows users to get answers to their questions quickly and efficiently, while also receiving personalized feedback based on their emotions.

[1305] Unlike conventional question-answering systems, this system provides personalized feedback based on the user's emotions, improving the user experience.

[1306] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1307] Step 1: User enters question

[1308] A user opens a dedicated application or web browser on a smartphone, tablet, or computer, enters a question in the question input field, for example, "What is the answer to 1+1?", and presses the submit button. This action sends the entered question to the system.

[1309] Input: A question typed by the user (e.g., "What is the answer to 1+1?")

[1310] Output: Question data entered by the user

[1311] Step 2: The device analyzes emotions

[1312] The device sends the question received from the user to a sentiment analysis engine, which analyzes the user's sentiment. This analysis uses an algorithm that extracts sentiment from text, and emotional information such as "neutral" is obtained.

[1313] Input: User question data

[1314] Output: User question data and sentiment information (e.g., "neutral")

[1315] Step 3: The device converts the question and emotion information into JSON format.

[1316] The device converts the user's question and the analyzed emotion information into JSON format. Specifically, it generates the following JSON data:

[1317] json

[1318] {

[1319] "question": "What is the answer to 1+1?",

[1320] "emotion": "neutral"

[1321] }

[1322] Input: User question data and emotion information

[1323] Output: JSON format data

[1324] Step 4: The device sends the data to the server

[1325] The device sends the generated JSON data as an HTTP POST request to the server, which then uses a network communication library to send the data.

[1326] Input: JSON format data

[1327] Output: HTTP POST request sent to the server

[1328] Step 5: The server receives and parses the data

[1329] The server analyzes the HTTP POST request received from the device, extracts JSON data from the request body, and then extracts the question and sentiment information from this data separately and passes them on to the next stage of processing.

[1330] Input: HTTP POST request

[1331] Output: Extracted question and sentiment information

[1332] Step 6: The server sends a request to the natural language processing engine

[1333] The server converts the extracted question and sentiment information into an API request to be sent to the natural language processing engine, for example in the following format:

[1334] Prompt: "What is the answer to 1 + 1?" (Emotion: Neutral, Max Tokens: 10)

[1335] Input: Extracted question and sentiment information

[1336] Output: API request to the natural language processing engine

[1337] Step 7: The natural language processing engine generates the answer

[1338] The natural language processing engine analyzes the received question and emotional information to generate the optimal answer, such as "The answer to 1 + 1 is 2."

[1339] Input: API request

[1340] Output: Generated response data

[1341] Step 8: Server Reformats Response Data

[1342] The server reformats the response data received from the natural language processing engine and converts it into a format that is easy for the user to understand, for example, into a JSON format like this:

[1343] json

[1344] {

[1345] "answer": "The answer to 1+1 is 2"

[1346] }

[1347] Input: Generated response data

[1348] Output: Reformatted response data

[1349] Step 9: The server sends the reformatted data to the device

[1350] The server then sends the reformatted response data to the terminal as an HTTP response. The data is then sent to the terminal via network communication.

[1351] Input: Reformatted response data

[1352] Output: Sending HTTP response to the terminal

[1353] Step 10: Your device displays your answer and emotional feedback

[1354] The device analyzes the HTTP response received from the server and displays the answer and emotional feedback on the user interface. For example, the answer "The answer to 1 + 1 is 2" and the emotional feedback "You look happy!" are displayed.

[1355] Input: HTTP response from the server

[1356] Output: Display of answer and emotional feedback to the user

[1357] (Application example 2)

[1358] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1359] In conventional systems, even if a user inputs a question, they are unable to receive emotional feedback and are simply provided with a mechanical answer. This means that personalized feedback based on the user's emotions is not provided, which can lead to a decrease in work efficiency and employee satisfaction, especially in stressful environments such as factories.

[1360] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for analyzing a received question and analyzing the user's emotion via an emotion analysis engine, means for transmitting the question and the analyzed emotion information to a natural language processing engine, and means for the natural language processing engine to generate an answer to the question and feedback based on the emotion and return it to the server. This makes it possible to provide personalized feedback based on the user's emotion in real time.

[1361] 1. "Means for users to input questions via electronic devices" refers to devices that provide an interface for users to input questions in natural language using electronic devices such as smartphones, tablets, and computers.

[1362] 2. "Means for transmitting the question to the server in a format" refers to the protocol and hardware that converts the input question into an appropriate data format (e.g., JSON format) and transmits it to the server via a network.

[1363] 3. "Sentiment analysis engine" refers to algorithms and software that analyze and extract user emotions from input text data.

[1364] 4. A "natural language processing engine" is an algorithm or software that analyzes text data, performs natural language processing, and generates answers to questions.

[1365] 5. "Means for reformatting the response received by the server and transmitting it to the electronic device" refers to protocols and hardware that receive the response returned from the natural language processing engine, convert it into an appropriate display format, and transmit it to the electronic device.

[1366] 6. "Emotion-based feedback" refers to additional comments or information provided based on an analysis of a user's emotions to generate a personalized response to the user.

[1367] 7. "User interface" means the screen and operating environment through which a user can input and confirm questions via an electronic device.

[1368] 8. "API Request" is a protocol for sending specific data to another software or service and receiving the processing results.

[1369] A system for implementing the present invention allows a user to input a question via an electronic device and receive a real-time answer. The system uses a sentiment analysis engine to analyze the user's sentiment, and a natural language processing engine to provide personalized feedback based on the sentiment.

[1370] The overall approach of the system is as follows: First, the user inputs a question using a smartphone, tablet, smart glasses, or computer. The input question is converted into an appropriate data format, such as JSON, and sent to the server. At this stage, the user's emotional information is also sent along with the question data.

[1371] The server analyzes the received question and emotion data and executes the user's emotion through an emotion analysis engine. As a result, the question and emotion information are obtained. The server then sends this question and emotion information to a natural language processing engine. The natural language processing engine generates an answer to the question and also generates feedback based on the user's emotion. If the user is feeling a certain emotion, this feedback provides a response appropriate to that emotion.

[1372] The generated answers and emotional feedback are then sent back to the server, which then reformats and sends the data to the user's electronic device, where the user can finally view the answers to the questions and the emotional feedback.

[1373] For example, if a factory worker uses smart glasses to ask, "Please tell me how to install this part," their emotions will also be analyzed. If the system detects that the worker is nervous, it will display feedback such as, "Here's how to install this part. Please relax and continue working." In this way, personalized feedback is provided according to the user's emotions.

[1374] An example of a prompt for the generative AI model is as follows:

[1375] "Please tell me how to install this part" is entered.

[1376] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1377] Step 1:

[1378] A user inputs a question via an electronic device. The electronic device receives the input as text data and analyzes the user's emotions using a sentiment analysis engine. This analysis provides the question entered by the user and emotional information when entering the question.

[1379] input:

[1380] A text question entered by the user (e.g., "How do I install this part?")

[1381] The user's emotional state

[1382] output:

[1383] Question text

[1384] Emotional information (e.g., "I'm nervous")

[1385] Step 2:

[1386] The device formats the question and emotion information into JSON format and sends it to the server using an HTTP POST request.

[1387] input:

[1388] Question text (e.g., "How do I install this part?")

[1389] Emotional information (e.g., "I'm nervous")

[1390] output:

[1391] JSON format data

[1392] json

[1393] {

[1394] "question": "How do I install this part?",

[1395] "emotion": "nervous"

[1396] }

[1397] Step 3:

[1398] The server analyzes the received question and sentiment information and sends it to the natural language processing engine. The data sent to the natural language processing engine is in the form of an API request.

[1399] input:

[1400] Question and sentiment data in JSON format

[1401] output:

[1402] API requests to the natural language processing engine

[1403] {

[1404] "prompt": "How do I install this part?",

[1405] "emotion": "nervous",

[1406] "max_tokens": 50

[1407] }

[1408] Step 4:

[1409] A natural language processing engine parses the API request and generates answers and sentiment feedback for the question, which is then sent back to the server in JSON format.

[1410] input:

[1411] API requests to the natural language processing engine

[1412] output:

[1413] Feedback based on questions and sentiment

[1414] json

[1415] {

[1416] "answer": "Here's how to install this part.",

[1417] "emotion_feedback": "Just relax and keep working"

[1418] }

[1419] Step 5:

[1420] The server then reformats the received answers and feedback and sends them to the electronic device, sending the data as an HTTP response.

[1421] input:

[1422] Answers and feedback from natural language processing engines

[1423] output:

[1424] Formatted answers and feedback

[1425] json

[1426] {

[1427] "answer": "Here's how to install this part.",

[1428] "emotion_feedback": "Just relax and keep working"

[1429] }

[1430] Step 6:

[1431] The device receives the HTTP response from the server and displays the answer and feedback on the user interface, allowing the user to review the displayed information and take action based on it.

[1432] input:

[1433] Formatted answers and feedback

[1434] output:

[1435] Answers and feedback displayed on the user interface

[1436] Answer: Here's how to install this part:

[1437] Feedback: Relax and keep working

[1438] These steps provide real-time, emotion-based, personalized feedback to user-entered questions.

[1439] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1440] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1441] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1442] [Fourth embodiment]

[1443] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1444] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1445] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1446] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1447] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1448] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1449] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1450] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1451] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1452] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1453] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1454] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1455] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1456] The system of the present invention allows users to input questions via electronic devices and provides answers to those questions in real time. The system begins operation when a user inputs and submits a question via a dedicated app or web browser.

[1457] First, a user opens a dedicated app or web browser on an electronic device such as a smartphone, tablet, or computer. The user enters a question in the question input field and presses the "Submit" button. For example, the user can enter "What is the answer to 1+1?"

[1458] Next, the device takes the question entered by the user, converts it to JSON format, and sends it to the server. The JSON format data looks like this:

[1459] json

[1460] {

[1461] "question": "What is the answer to 1+1?"

[1462] }

[1463] This JSON data is sent to the server as an HTTP POST request.

[1464] The server receives the HTTP POST request from the device, analyzes the data, and extracts the question body. The extracted question is converted into an API request to be sent to the natural language processing engine. For example, the following API request is made:

[1465] json

[1466] {

[1467] "prompt": "What is the answer to 1+1?",

[1468] "max_tokens": 10

[1469] }

[1470] The server sends this API request to the natural language processing engine.

[1471] The natural language processing engine analyzes the received question and generates the best answer, such as "The answer to 1 + 1 is 2," and the generated answer is sent back to the server in JSON format.

[1472] The server receives the response from the NLP engine, reformats it, and sends it back to the device. The reformatted JSON data looks like this:

[1473] json

[1474] {

[1475] "answer": "The answer to 1+1 is 2"

[1476] }

[1477] The server sends this answer to the terminal as an HTTP response.

[1478] Finally, the device receives the HTTP response from the server, extracts the answer data, and displays it on the user interface. The user can see the answer displayed on the device screen: "The answer to 1 + 1 is 2."

[1479] The system allows users to get answers to specific questions more quickly and efficiently than traditional tutoring services, automatically handling the entire process from submitting a question to displaying the answer, providing immediate feedback to users.

[1480] The processing flow will be explained below.

[1481] Step 1:

[1482] The user opens a dedicated app or web browser, enters "What is the answer to 1+1?" in the question input field, and presses the send button.

[1483] Step 2:

[1484] The terminal receives the question entered by the user and converts it into JSON format. The JSON data at this time looks like this:

[1485] json

[1486] {

[1487] "question": "What is the answer to 1+1?"

[1488] }

[1489] Step 3:

[1490] The device sends the JSON-encoded question to the server as an HTTP POST request, with the HTTP request header information also set appropriately.

[1491] Step 4:

[1492] The server receives an HTTP POST request from the device, extracts the JSON data from the request, and then extracts the question body.

[1493] Step 5:

[1494] The server prepares an appropriate API request to send the extracted question to the natural language processing engine, such as the following:

[1495] json

[1496] {

[1497] "prompt": "What is the answer to 1+1?",

[1498] "max_tokens": 10

[1499] }

[1500] Step 6:

[1501] The server sends this API request to the natural language processing engine, which makes the API request to a specific endpoint.

[1502] Step 7:

[1503] The natural language processing engine analyzes the received question and generates the most appropriate answer, for example, "The answer to 1 + 1 is 2."

[1504] Step 8:

[1505] The natural language processing engine returns the generated answer in JSON format to the server. The returned JSON data is as follows:

[1506] json

[1507] {

[1508] "response": "1+1 equals 2"

[1509] }

[1510] Step 9:

[1511] The server receives the response from the natural language processing engine, parses the received data, and reformats it if necessary.

[1512] Step 10:

[1513] The server will then send the reformatted response back to the device in JSON format, for example something like this:

[1514] json

[1515] {

[1516] "answer": "The answer to 1+1 is 2"

[1517] }

[1518] Step 11:

[1519] The terminal receives the HTTP response from the server, analyzes the received response data, and extracts the answer data.

[1520] Step 12:

[1521] The device displays the extracted answer on the user interface, and the user confirms the answer displayed on the screen: "The answer to 1 + 1 is 2."

[1522] This series of steps provides a quick and accurate answer to the user's question.

[1523] Example 1

[1524] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1525] Conventional information retrieval systems and question-answering systems have had issues with the speed and accuracy of the time it takes for a user to receive an answer after entering a question. In particular, in systems that provide answers in real time, the time it takes to analyze the question and generate an answer has been a problem. There has also been a demand for a method that allows users to easily enter questions and receive answers without using complex input formats.

[1526] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1527] In this invention, the server includes means for converting a question into JSON format and sending it to the server, means for the server to analyze the question received and send it to a natural language processing engine, and means for the natural language processing engine to generate an answer to the question and return it to the server. This allows users to easily input questions and receive quick and accurate answers.

[1528] A "user" is a person or entity that utilizes the system to enter questions and receive answers.

[1529] A "device" is an electronic device, such as a smartphone, tablet, or computer, that a user uses to enter a question.

[1530] "Means for converting a question into JSON format and sending it to a server" refers to means capable of converting a question entered by a user into JavaScript Object Notation format and transmitting it to a server via the Internet.

[1531] A "server" is a central processing unit responsible for receiving, parsing and sending queries submitted by users to a natural language processing engine.

[1532] A "means for analyzing a question" is a means capable of understanding the content of a question entered by a user and converting the data into an appropriate format.

[1533] A "natural language processing engine" is an engine that utilizes artificial intelligence techniques used to generate responses to questions.

[1534] An "API Request" is a request to send or receive data via an Application Programming Interface.

[1535] "Means for reformatting and transmitting a response to a terminal" refers to means capable of executing a process for reformatting a response obtained from a natural language processing engine and transmitting the response to a user's device.

[1536] A "user interface" is a screen or interactive environment with which a user directly interacts and sees input and output.

[1537] The system of the present invention allows a user to input a question via a device and provides an answer to the question in real time. The following components and steps are used to implement the invention.

[1538] First, a user uses a device such as a smartphone, tablet, or computer. They open a dedicated app or web browser and enter a question into the question input field. For example, they might enter, "What is the answer to 1+1?" At this point, they press the "Send" button.

[1539] The terminal takes the question entered by the user and converts it into JavaScript Object Notation (JSON) format. The converted JSON data is then sent to the server via the Internet as an HTTP POST request. For example, the converted JSON data might look like this:

[1540] json

[1541] {

[1542] "question": "What is the answer to 1+1?"

[1543] }

[1544] The server receives the HTTP POST request sent from the device and parses the JSON formatted data. It extracts the question body and converts it into an API request. This API request is sent to a natural language processing engine (e.g., a generative AI model). An example of an API request is as follows:

[1545] json

[1546] {

[1547] "prompt": "What is the answer to 1+1?",

[1548] "max_tokens": 10

[1549] }

[1550] The natural language processing engine analyzes the received API request and generates the best answer, such as "The answer to 1+1 is 2," and sends the answer back to the server in JSON format. The generated JSON data looks like this:

[1551] json

[1552] {

[1553] "answer": "The answer to 1+1 is 2"

[1554] }

[1555] When the server receives the response from the natural language processing engine, it reformats it and sends the reformatted data to the device as an HTTP response.

[1556] Finally, the device receives the HTTP response from the server and extracts the answer data in JSON format. The user interface displays "The answer to 1+1 is 2." This allows the user to check the generated answer on the device screen.

[1557] Examples of prompt sentences include the following:

[1558] "What's the answer to 3 x 4?"

[1559] "What is the capital of Japan?"

[1560] This system allows users to obtain answers to their questions quickly and accurately, and is expected to be particularly useful in the fields of education and information retrieval.

[1561] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1562] Step 1:

[1563] The user opens a dedicated app or web browser on the device, enters a question in the question input field on the user interface, and presses the "Submit" button. For example, they enter "What is the answer to 1+1?" The input is in text format.

[1564] Step 2:

[1565] The terminal takes the user's input and converts the text data into JSON format. The input data is "What is the answer to 1+1?" and the converted JSON format is as follows:

[1566] json

[1567] {

[1568] "question": "What is the answer to 1+1?"

[1569] }

[1570] This JSON data is sent to the server as an HTTP POST request.

[1571] Step 3:

[1572] The server receives an HTTP POST request. The received data is in JSON format, and the server parses it. It extracts the content of the "question" field from the input JSON data and then converts it to generate an API request to the natural language processing engine. The specific format of the API request is as follows:

[1573] json

[1574] {

[1575] "prompt": "What is the answer to 1+1?",

[1576] "max_tokens": 10

[1577] }

[1578] Step 4:

[1579] The server sends the generated API request to the natural language processing engine. After the API request is sent, the natural language processing engine receives the request and begins analyzing it. This analysis process generates the best answer to the input question, "What is the answer to 1+1?"

[1580] Step 5:

[1581] The natural language processing engine generates the best answer. The generated answer is "The answer to 1+1 is 2." This answer is returned to the server in JSON format. The format of the returned data is as follows:

[1582] json

[1583] {

[1584] "answer": "The answer to 1+1 is 2"

[1585] }

[1586] Step 6:

[1587] The server receives the response data returned from the natural language processing engine. It reformats the received data and sends it to the device. The reformatted data looks like this:

[1588] json

[1589] {

[1590] "answer": "The answer to 1+1 is 2"

[1591] }

[1592] The server sends this data to the terminal as an HTTP response.

[1593] Step 7:

[1594] The device analyzes the HTTP response received from the server and displays it on the user interface. Specifically, the text "The answer to 1 + 1 is 2" is displayed. The user can check the answer on the device screen.

[1595] This allows users to smoothly experience the entire process from input to display of the answer.

[1596] (Application example 1)

[1597] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1598] On modern online shopping sites, users have limited means to instantly resolve questions about products and services. This can lead to a poor user experience and a risk of losing purchasing motivation. Furthermore, slow response times in FAQ sections and customer support can lead to users abandoning the site. To solve these issues, a system is needed that allows users to ask questions in real time and receive immediate answers.

[1599] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1600] In this invention, the server includes means for a user to input a question via an electronic device, means for formatting the question and sending it to the server, means for the server to analyze the received question and send it to a natural language processing engine, means for the natural language processing engine to generate an answer to the question and send it back to the server, means for the server to reformat the answer received and send it to the electronic device, means for the electronic device to display the answer on a user interface, and chatbot software embedded in an online shopping site where users can ask questions about products and services in real time, thereby enabling users to quickly resolve their questions and smoothly progress through the purchasing process.

[1601] An "electronic device" is a hardware device used by a user to input or receive information, including, for example, a smartphone, tablet, or computer.

[1602] The "means for inputting a question" is a function that provides an interface for a user to input a question in text format and send it to the system.

[1603] A "server" is a central processing unit that receives, analyzes, processes data over a network, and transmits the results to other devices.

[1604] A "natural language processing engine" is software or an algorithm that analyzes human-written language data, understands its content, and generates appropriate answers.

[1605] A "prompt sentence" is a phrase that explicitly specifies a user's question and is input to a natural language processing engine.

[1606] "Chatbot software" is a program that automatically interacts with users and generates answers to their questions.

[1607] A "format" is a procedure or convention for converting and organizing questions and answers into a particular form.

[1608] "Real-time" refers to the fact that the time between when a user enters a question and when they receive a response is very short, with the response being almost instantaneous.

[1609] "User interface" refers to the screen or display that contains the visual and operational elements that allow a user to interact with a system.

[1610] An "online shopping site" is a website that offers products and services over the Internet and allows users to purchase them online.

[1611] An embodiment of the present invention is a system for enabling a user to ask a question about a product or service in real time and for providing an answer to the question immediately. The system of the present invention includes the following means.

[1612] First, a user opens a dedicated app or web browser on an electronic device such as a smartphone, tablet, or computer. The user enters a question in the question input field and presses the "Submit" button. For example, the user can enter "What is the refund policy for this product?"

[1613] The device receives the question entered by the user, converts it into JSON format, and sends it to the server. The JSON format data looks like this:

[1614] json

[1615] {

[1616] "question": "What is the refund policy for this item?"

[1617] }

[1618] This JSON data is sent to the server as an HTTP POST request.

[1619] The server parses the incoming HTTP POST request, extracts the question body, and converts it into an API request to send to the natural language processing engine. The API request looks like this:

[1620] json

[1621] {

[1622] "prompt": "What is the refund policy for this item?",

[1623] "max_tokens": 10

[1624] }

[1625] The server sends this API request to the natural language processing engine.

[1626] A natural language processing engine (such as OpenAI's GPT-3) analyzes the received question and generates the optimal answer, such as "The refund policy for this product is a full refund within 30 days," and the generated answer is sent back to the server in JSON format.

[1627] The server receives the response from the natural language processing engine, reformats it in a format that is easy for the user to understand, and sends it back to the device. The reformatted JSON data looks like this:

[1628] json

[1629] {

[1630] "answer": "This product's refund policy is a full refund within 30 days"

[1631] }

[1632] The server sends this answer to the terminal as an HTTP response.

[1633] Finally, the device receives the HTTP response from the server, extracts the answer data, and displays it on the user interface. The user can see the answer displayed on the device screen: "The refund policy for this product is a full refund within 30 days."

[1634] The system of the present invention features the incorporation of chatbot software into an online shopping site, allowing users to quickly resolve their questions and smoothly progress through the purchasing process. An example of a specific prompt sentence is "What is the refund policy for this product?"

[1635] The hardware used includes smartphones, tablets, and computers, and the software used includes React and Vue.js for the front end, Python, Flask, and Django for the back end, HTTP and REST for API communication, and OpenAI GPT-3 as a natural language processing engine. These systems improve the user experience and enable quick and efficient resolution of queries.

[1636] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1637] Step 1:

[1638] A user opens a dedicated app or web browser on a smartphone, tablet, or computer, enters a question, and presses the "Send" button. The input is the text data of the question. For example, the user might enter, "What is the refund policy for this product?" The output is the text question data sent from the device to the server.

[1639] Step 2:

[1640] The terminal converts the question data entered by the user into JSON format. The input is a text question, and the data processing involves converting this text into the following JSON format:

[1641] json

[1642] {

[1643] "question": "What is the refund policy for this item?"

[1644] }

[1645] The output is the question data in JSON format.

[1646] Step 3:

[1647] The terminal sends JSON-formatted question data to the server as an HTTP POST request. The input is JSON-formatted question data, and the data processing involves creating an HTTP request. The output is the HTTP request sent to the server.

[1648] Step 4:

[1649] The server parses the received HTTP POST request and extracts the question body. The input is the HTTP request, and the data operation is the parsing of JSON data. The output is the extracted question body. For example, the extracted sentence is "What is the refund policy for this product?"

[1650] Step 5:

[1651] The server converts the extracted question text into an API request to send to the natural language processing engine. The input is the question text, and the data processing involves converting it into the API request format as follows:

[1652] json

[1653] {

[1654] "prompt": "What is the refund policy for this item?",

[1655] "max_tokens": 10

[1656] }

[1657] The output is data in the form of an API request.

[1658] Step 6:

[1659] The server sends data in the form of an API request to the natural language processing engine. Generative AI models used include OpenAI GPT-3. The input is the API request data, and data processing involves sending an HTTP request. The output is the response data returned by the natural language processing engine.

[1660] Step 7:

[1661] The natural language processing engine generates an answer based on the question it receives. The input is a question in the form of an API request, and the data calculation is the generation of an answer by the NLP engine. For example, if the input question is "What is the refund policy for this product?", the generated answer will be "The refund policy for this product is a full refund within 30 days." The output is the answer data in JSON format.

[1662] Step 8:

[1663] The server receives the response data returned by the natural language processing engine, reformats it, and sends it to the terminal. The input is the response data in JSON format, and the data processing is to convert it into the final JSON format as shown below:

[1664] json

[1665] {

[1666] "answer": "This product's refund policy is a full refund within 30 days"

[1667] }

[1668] The output is the reformatted JSON response data.

[1669] Step 9:

[1670] The terminal analyzes the JSON formatted response data received from the server and displays it on the user interface. The input is the JSON formatted response data, and the data operation is data binding to the UI component. The output is the response displayed on the user interface. The user can confirm the response displayed on the screen of their electronic device, which states, "The refund policy for this product is a full refund within 30 days."

[1671] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1672] The system of the present invention allows a user to input a question via an electronic device and not only provides an answer to the question in real time, but also recognizes the user's emotions and provides more appropriate feedback.

[1673] The user opens a dedicated app or web browser on an electronic device such as a smartphone, tablet, or computer. The user enters "What is the answer to 1+1?" in the question input field and presses the send button. The emotion engine analyzes the user's emotions from this input and connects that emotional information to the system.

[1674] The device converts the question entered by the user into JSON format and emotional information, and sends it to the server. The JSON format data looks like this:

[1675] json

[1676] {

[1677] "question": "What is the answer to 1+1?",

[1678] "emotion": "neutral"

[1679] }

[1680] This JSON data and emotion information is sent to the server as an HTTP POST request.

[1681] The server receives the HTTP POST request from the device, analyzes the data, and extracts the question body and sentiment information. The extracted question and sentiment information are converted into an API request to be sent to the natural language processing engine. For example, make the following API request:

[1682] json

[1683] {

[1684] "prompt": "What is the answer to 1+1?",

[1685] "emotion": "neutral",

[1686] "max_tokens": 10

[1687] }

[1688] The server sends this API request to the natural language processing engine.

[1689] The natural language processing engine analyzes the received question and sentiment information to generate the optimal answer, such as "The answer to 1 + 1 is 2," and returns the answer in JSON format to the server.

[1690] The server receives the response from the NLP engine, reformats it, and sends it back to the device. The reformatted JSON data looks like this:

[1691] json

[1692] {

[1693] "answer": "The answer to 1+1 is 2"

[1694] }

[1695] The server sends this response data to the terminal as an HTTP response.

[1696] The device receives the HTTP response from the server, extracts the answer data and emotional feedback, and displays it on the user interface. The user can then respond based on the feedback. Along with the answer "The answer to 1 + 1 is 2," emotional feedback such as "You look happy!" is also displayed.

[1697] This system not only provides answers to specific questions more quickly and efficiently than traditional tutoring services, but also provides personalized feedback based on the user's emotions. The system automatically handles the entire process from submitting a question to displaying an answer based on the user's emotions, providing users with a more personalized service faster.

[1698] The processing flow will be explained below.

[1699] Step 1:

[1700] The user opens a dedicated app or web browser, enters "What is the answer to 1+1?" in the question input field, and presses the send button.

[1701] Step 2:

[1702] The terminal acquires the question entered by the user and sends the question content to the emotion engine.

[1703] Step 3:

[1704] The emotion engine analyzes the question and recognizes the user's emotion. In this case, the emotion engine determines the emotion as "neutral."

[1705] Step 4:

[1706] The device converts the acquired question and the recognition result from the emotion engine into JSON format. The JSON data at this time looks like this:

[1707] json

[1708] {

[1709] "question": "What is the answer to 1+1?",

[1710] "emotion": "neutral"

[1711] }

[1712] Step 5:

[1713] The device sends JSON format data to the server as an HTTP POST request.

[1714] Step 6:

[1715] The server receives the HTTP POST request from the device, analyzes the data, and extracts the question text and sentiment information.

[1716] Step 7:

[1717] The server converts the extracted question and sentiment information into an API request to send to the natural language processing engine. An example of an API request is as follows:

[1718] json

[1719] {

[1720] "prompt": "What is the answer to 1+1?",

[1721] "emotion": "neutral",

[1722] "max_tokens": 10

[1723] }

[1724] Step 8:

[1725] The server sends this API request to the natural language processing engine.

[1726] Step 9:

[1727] The natural language processing engine analyzes the received question and sentiment information to generate the most appropriate answer, such as "The answer to 1 + 1 is 2."

[1728] Step 10:

[1729] The natural language processing engine returns the generated answer in JSON format to the server. The returned JSON data is as follows:

[1730] json

[1731] {

[1732] "response": "1+1 equals 2"

[1733] }

[1734] Step 11:

[1735] The server receives the response from the natural language processing engine, reformats it if necessary, and sends the reformatted data to the device in the following JSON format:

[1736] json

[1737] {

[1738] "answer": "The answer to 1+1 is 2",

[1739] "emotion_feedback": "Thank you!"

[1740] }

[1741] Step 12:

[1742] The device receives the HTTP response from the server and extracts the response data and emotional feedback.

[1743] Step 13:

[1744] The device displays the extracted answer and emotional feedback on the user interface. For example, the answer "The answer to 1+1 is 2" is displayed along with emotional feedback such as "Thank you!"

[1745] This series of steps allows users to not only get a quick and appropriate answer to their question, but also receive feedback based on their emotions.

[1746] Example 2

[1747] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1748] Conventional question-answering systems only provide answers to questions entered by the user, and are therefore unable to provide feedback that takes into account the user's emotional state. This has resulted in a lack of individualized responses and personalized services for users. Furthermore, there has been no system that can analyze a user's emotions and provide optimal answers based on those emotions, making it difficult to improve the user experience.

[1749] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for a user to input a question and emotion information, a means for converting the question and emotion information into JSON format, and a means for transmitting the question and emotion information to a natural language processing engine and generating an answer. This makes it possible to provide an optimal answer based on the user's emotion.

[1750] "User" refers to a person who uses the system to enter questions and receive answers.

[1751] "Electronic device" refers to a device, such as a smartphone, tablet, or computer, that a user uses to enter a question.

[1752] A "question" refers to a question that a user asks the system.

[1753] "Emotion analysis engine" refers to software or algorithms that analyze a user's emotional state based on questions entered by the user.

[1754] "Emotion information" refers to data that represents the emotional state analyzed by the emotion analysis engine from the user's question.

[1755] "Format" refers to a specific data format for sending questions and emotion information to the server.

[1756] "Server" refers to a computer system that analyzes data received from a terminal and communicates with a natural language processing engine to generate an answer.

[1757] A "natural language processing engine" refers to software or algorithms that generate appropriate answers based on input questions and emotional information.

[1758] "Answer" refers to information generated by a natural language processing engine in response to a user's question.

[1759] "Reformatting" refers to the process of converting the answers received from the natural language processing engine into a format that is easy for the user to understand.

[1760] "User interface" refers to the graphical display area and operation portion through which a user interacts with a system.

[1761] "Emotional feedback" refers to additional feedback or comments provided based on the user's emotional information.

[1762] "JSON format" refers to a lightweight data exchange format for expressing data in text format.

[1763] "API Request" means a communication request to send a question and sentiment information to a natural language processing engine and receive a response.

[1764] The present invention provides a system that allows a user to input a question using an electronic device and receives feedback based on the answer and emotions to the question. Specifically, a user uses an electronic device such as a smartphone, tablet, or computer and opens a dedicated application or web browser. When the user inputs a question and presses the send button, the system operates in the following steps.

[1765] First, the device receives a question entered by the user. At this time, the question is analyzed in real time by a sentiment analysis engine to obtain the user's emotional information. This sentiment analysis uses a general algorithm for identifying emotions from voice or text. For example, the sentiment analysis API of IBM Watson or Microsoft Azure can be considered.

[1766] The device then converts the question and emotion information into JSON format data, which makes it easier to transmit and analyze the data, and sends the generated JSON data to the server as an HTTP POST request.

[1767] The server analyzes the HTTP POST request received from the device and extracts the question and sentiment information separately. Based on this extracted data, the server sends an API request to a natural language processing engine. The natural language processing engine uses advanced text analysis algorithms such as OpenAI GPT-3 and Google BERT. This engine analyzes the user's question and sentiment information and generates the optimal answer.

[1768] The generated answer is sent back to the server, which then reformats it and sends it to the device. The reformatted answer data is displayed to the user on a user interface. The user is provided with the answer to their question as well as sentiment-based feedback, allowing them to get a more personalized answer quickly.

[1769] Specific examples

[1770] If a user types "What is the answer to 1+1?", the sentiment analysis engine will recognize the user's sentiment as "neutral." It will then generate JSON data containing the following:

[1771] json

[1772] {

[1773] "question": "What is the answer to 1+1?",

[1774] "emotion": "neutral"

[1775] }

[1776] This data is sent to the server, which extracts the question "What is the answer to 1+1?" and the emotion "neutral." The following prompt is sent to the natural language processing engine:

[1777] "What is the answer to 1 + 1?" (Neutral emotion, maximum tokens: 10)

[1778] The natural language processing engine responds with "1 + 1 equals 2," which is then reformatted and sent to the device, where it displays the specific answer to the question along with feedback such as "You look happy!" This allows users to get answers to their questions quickly and efficiently, while also receiving personalized feedback based on their emotions.

[1779] Unlike conventional question-answering systems, this system provides personalized feedback based on the user's emotions, improving the user experience.

[1780] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1781] Step 1: User enters question

[1782] A user opens a dedicated application or web browser on a smartphone, tablet, or computer, enters a question in the question input field, for example, "What is the answer to 1+1?", and presses the submit button. This action sends the entered question to the system.

[1783] Input: A question typed by the user (e.g., "What is the answer to 1+1?")

[1784] Output: Question data entered by the user

[1785] Step 2: The device analyzes emotions

[1786] The device sends the question received from the user to a sentiment analysis engine, which analyzes the user's sentiment. This analysis uses an algorithm that extracts sentiment from text, and emotional information such as "neutral" is obtained.

[1787] Input: User question data

[1788] Output: User question data and sentiment information (e.g., "neutral")

[1789] Step 3: The device converts the question and emotion information into JSON format.

[1790] The device converts the user's question and the analyzed emotion information into JSON format. Specifically, it generates the following JSON data:

[1791] json

[1792] {

[1793] "question": "What is the answer to 1+1?",

[1794] "emotion": "neutral"

[1795] }

[1796] Input: User question data and emotion information

[1797] Output: JSON format data

[1798] Step 4: The device sends the data to the server

[1799] The device sends the generated JSON data as an HTTP POST request to the server, which then uses a network communication library to send the data.

[1800] Input: JSON format data

[1801] Output: HTTP POST request sent to the server

[1802] Step 5: The server receives and parses the data

[1803] The server analyzes the HTTP POST request received from the device, extracts JSON data from the request body, and then extracts the question and sentiment information from this data separately and passes them on to the next stage of processing.

[1804] Input: HTTP POST request

[1805] Output: Extracted question and sentiment information

[1806] Step 6: The server sends a request to the natural language processing engine

[1807] The server converts the extracted question and sentiment information into an API request to be sent to the natural language processing engine, for example in the following format:

[1808] Prompt: "What is the answer to 1 + 1?" (Emotion: Neutral, Max Tokens: 10)

[1809] Input: Extracted question and sentiment information

[1810] Output: API request to the natural language processing engine

[1811] Step 7: The natural language processing engine generates the answer

[1812] The natural language processing engine analyzes the received question and emotional information to generate the optimal answer, such as "The answer to 1 + 1 is 2."

[1813] Input: API request

[1814] Output: Generated response data

[1815] Step 8: Server Reformats Response Data

[1816] The server reformats the response data received from the natural language processing engine and converts it into a format that is easy for the user to understand, for example, into a JSON format like this:

[1817] json

[1818] {

[1819] "answer": "The answer to 1+1 is 2"

[1820] }

[1821] Input: Generated response data

[1822] Output: Reformatted response data

[1823] Step 9: The server sends the reformatted data to the device

[1824] The server then sends the reformatted response data to the terminal as an HTTP response. The data is then sent to the terminal via network communication.

[1825] Input: Reformatted response data

[1826] Output: Sending HTTP response to the terminal

[1827] Step 10: Your device displays your answer and emotional feedback

[1828] The device analyzes the HTTP response received from the server and displays the answer and emotional feedback on the user interface. For example, the answer "The answer to 1 + 1 is 2" and the emotional feedback "You look happy!" are displayed.

[1829] Input: HTTP response from the server

[1830] Output: Display of answer and emotional feedback to the user

[1831] (Application example 2)

[1832] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1833] In conventional systems, even if a user inputs a question, they are unable to receive emotional feedback and are simply provided with a mechanical answer. This means that personalized feedback based on the user's emotions is not provided, which can lead to a decrease in work efficiency and employee satisfaction, especially in stressful environments such as factories.

[1834] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for analyzing a received question and analyzing the user's emotion via an emotion analysis engine, means for transmitting the question and the analyzed emotion information to a natural language processing engine, and means for the natural language processing engine to generate an answer to the question and feedback based on the emotion and return it to the server. This makes it possible to provide personalized feedback based on the user's emotion in real time.

[1835] 1. "Means for users to input questions via electronic devices" refers to devices that provide an interface for users to input questions in natural language using electronic devices such as smartphones, tablets, and computers.

[1836] 2. "Means for transmitting the question to the server in a format" refers to the protocol and hardware that converts the input question into an appropriate data format (e.g., JSON format) and transmits it to the server via a network.

[1837] 3. "Sentiment analysis engine" refers to algorithms and software that analyze and extract user emotions from input text data.

[1838] 4. A "natural language processing engine" is an algorithm or software that analyzes text data, performs natural language processing, and generates answers to questions.

[1839] 5. "Means for reformatting the response received by the server and transmitting it to the electronic device" refers to protocols and hardware that receive the response returned from the natural language processing engine, convert it into an appropriate display format, and transmit it to the electronic device.

[1840] 6. "Emotion-based feedback" refers to additional comments or information provided based on an analysis of a user's emotions to generate a personalized response to the user.

[1841] 7. "User interface" means the screen and operating environment through which a user can input and confirm questions via an electronic device.

[1842] 8. "API Request" is a protocol for sending specific data to another software or service and receiving the processing results.

[1843] A system for implementing the present invention allows a user to input a question via an electronic device and receive a real-time answer. The system uses a sentiment analysis engine to analyze the user's sentiment, and a natural language processing engine to provide personalized feedback based on the sentiment.

[1844] The overall approach of the system is as follows: First, the user inputs a question using a smartphone, tablet, smart glasses, or computer. The input question is converted into an appropriate data format, such as JSON, and sent to the server. At this stage, the user's emotional information is also sent along with the question data.

[1845] The server analyzes the received question and emotion data and executes the user's emotion through an emotion analysis engine. As a result, the question and emotion information are obtained. The server then sends this question and emotion information to a natural language processing engine. The natural language processing engine generates an answer to the question and also generates feedback based on the user's emotion. If the user is feeling a certain emotion, this feedback provides a response appropriate to that emotion.

[1846] The generated answers and emotional feedback are then sent back to the server, which then reformats and sends the data to the user's electronic device, where the user can finally view the answers to the questions and the emotional feedback.

[1847] For example, if a factory worker uses smart glasses to ask, "Please tell me how to install this part," their emotions will also be analyzed. If the system detects that the worker is nervous, it will display feedback such as, "Here's how to install this part. Please relax and continue working." In this way, personalized feedback is provided according to the user's emotions.

[1848] An example of a prompt for the generative AI model is as follows:

[1849] "Please tell me how to install this part" is entered.

[1850] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1851] Step 1:

[1852] A user inputs a question via an electronic device. The electronic device receives the input as text data and analyzes the user's emotions using a sentiment analysis engine. This analysis provides the question entered by the user and emotional information when entering the question.

[1853] input:

[1854] A text question entered by the user (e.g., "How do I install this part?")

[1855] The user's emotional state

[1856] output:

[1857] Question text

[1858] Emotional information (e.g., "I'm nervous")

[1859] Step 2:

[1860] The device formats the question and emotion information into JSON format and sends it to the server using an HTTP POST request.

[1861] input:

[1862] Question text (e.g., "How do I install this part?")

[1863] Emotional information (e.g., "I'm nervous")

[1864] output:

[1865] JSON format data

[1866] json

[1867] {

[1868] "question": "How do I install this part?",

[1869] "emotion": "nervous"

[1870] }

[1871] Step 3:

[1872] The server analyzes the received question and sentiment information and sends it to the natural language processing engine. The data sent to the natural language processing engine is in the form of an API request.

[1873] input:

[1874] Question and sentiment data in JSON format

[1875] output:

[1876] API requests to the natural language processing engine

[1877] {

[1878] "prompt": "How do I install this part?",

[1879] "emotion": "nervous",

[1880] "max_tokens": 50

[1881] }

[1882] Step 4:

[1883] A natural language processing engine parses the API request and generates answers and sentiment feedback for the question, which is then sent back to the server in JSON format.

[1884] input:

[1885] API requests to the natural language processing engine

[1886] output:

[1887] Feedback based on questions and sentiment

[1888] json

[1889] {

[1890] "answer": "Here's how to install this part.",

[1891] "emotion_feedback": "Just relax and keep working"

[1892] }

[1893] Step 5:

[1894] The server then reformats the received answers and feedback and sends them to the electronic device, sending the data as an HTTP response.

[1895] input:

[1896] Answers and feedback from natural language processing engines

[1897] output:

[1898] Formatted answers and feedback

[1899] json

[1900] {

[1901] "answer": "Here's how to install this part.",

[1902] "emotion_feedback": "Just relax and keep working"

[1903] }

[1904] Step 6:

[1905] The device receives the HTTP response from the server and displays the answer and feedback on the user interface, allowing the user to review the displayed information and take action based on it.

[1906] input:

[1907] Formatted answers and feedback

[1908] output:

[1909] Answers and feedback displayed on the user interface

[1910] Answer: Here's how to install this part:

[1911] Feedback: Relax and keep working

[1912] These steps provide real-time, emotion-based, personalized feedback to user-entered questions.

[1913] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1914] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1915] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1916] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1917] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1918] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1919] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1920] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1921] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1922] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1923] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1924] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1925] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1926] 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.

[1927] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1928] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1929] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1930] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1931] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1932] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1933] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1934] The following is further disclosed regarding the above embodiment.

[1935] (Claim 1)

[1936] means for a user to input a question via an electronic device;

[1937] means for transmitting the query as a format to a server;

[1938] means for analyzing the question received by the server and transmitting the question to a natural language processing engine;

[1939] means for the natural language processing engine to generate an answer to the question and return it to the server;

[1940] means for reformatting and transmitting the response received by the server to the electronic device;

[1941] The system includes means for the electronic device to display the answers on a user interface.

[1942] (Claim 2)

[1943] The system of claim 1 , further comprising: means for converting the query into a JSON format.

[1944] (Claim 3)

[1945] The system of claim 1 , wherein the natural language processing engine includes means for receiving a question via an API request and generating an answer.

[1946] "Example 1"

[1947] (Claim 1)

[1948] a means for a user to input a question via the device;

[1949] means for converting the query into a JSON format and transmitting the JSON format to a server;

[1950] means for analyzing the question received by the server and transmitting the question to a natural language processing engine;

[1951] means for the natural language processing engine to generate an answer to the question and return it to the server;

[1952] means for reformatting and transmitting the response received by the server to the device;

[1953] The system includes means for the device to display the answers on a user interface.

[1954] (Claim 2)

[1955] The system of claim 1 , further comprising means for sending the question to a natural language processing engine via an API request.

[1956] (Claim 3)

[1957] The system of claim 1 , wherein the natural language processing engine includes means for receiving a question via an API request and generating an answer.

[1958] "Application Example 1"

[1959] (Claim 1)

[1960] means for a user to input a question via an electronic device;

[1961] means for transmitting the query as a format to a server;

[1962] means for analyzing the question received by the server and transmitting the question to a natural language processing engine;

[1963] means for the natural language processing engine to generate an answer to the question and return it to the server;

[1964] means for reformatting and transmitting the response received by the server to the electronic device;

[1965] means for the electronic device to display the answers on a user interface;

[1966] A system that includes chatbot software integrated into an online shopping site that allows users to ask questions about products and services in real time.

[1967] (Claim 2)

[1968] The system of claim 1 , further comprising: means for converting the query into a JSON format.

[1969] (Claim 3)

[1970] The system of claim 1 , wherein the natural language processing engine includes means for receiving a question via an API request and generating an answer.

[1971] "Example 2: Combining Emotion Engines"

[1972] (Claim 1)

[1973] means for a user to input a question via an electronic device;

[1974] means for analyzing the user's emotions from the question using a sentiment analysis engine;

[1975] means for transmitting the question and emotion information to a server in a format;

[1976] means for analyzing the question and emotion information received by the server and transmitting the same to a natural language processing engine;

[1977] means for the natural language processing engine to generate an answer based on the question and emotion information and return the answer to the server;

[1978] means for reformatting and transmitting the response received by the server to the electronic device;

[1979] The system further includes means for the electronic device to display the answer and emotional feedback on a user interface.

[1980] (Claim 2)

[1981] The system of claim 1 , further comprising: means for converting the question and sentiment information into a JSON format.

[1982] (Claim 3)

[1983] 10. The system of claim 1, wherein the natural language processing engine includes means for receiving a question and sentiment information via an API request and generating an answer.

[1984] "Application example 2 when combining emotion engines"

[1985] (Claim 1)

[1986] means for a user to input a question via an electronic device;

[1987] means for transmitting the query as a format to a server;

[1988] A means for analyzing the question received by the server and analyzing the user's emotions through an emotion analysis engine;

[1989] means for the server to send the question and the analyzed emotion information to a natural language processing engine;

[1990] means for the natural language processing engine to generate feedback based on the answer and sentiment to the question and return it to the server;

[1991] means for reformatting and transmitting the answers and feedback received by the server to the electronic device;

[1992] The system includes means for the electronic device to display the answers and feedback on a user interface.

[1993] (Claim 2)

[1994] The system of claim 1 , further comprising: means for converting the query into a JSON format.

[1995] (Claim 3)

[1996] 10. The system of claim 1, wherein the natural language processing engine includes means for receiving questions via API requests and generating feedback based on answers and sentiment. [Explanation of symbols]

[1997] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for a user to input a question via an electronic device; means for transmitting the query as a format to a server; means for analyzing the question received by the server and sending it to a natural language processing engine; means for the natural language processing engine to generate an answer to the question and return it to the server; means for reformatting and transmitting the response received by the server to the electronic device; The system includes means for the electronic device to display the answers on a user interface.

2. The system of claim 1 , further comprising: means for converting the query into a JSON format.

3. The system of claim 1 , wherein the natural language processing engine includes means for receiving a question via an API request and generating an answer.

Images