System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving a question about an environment from a user; means for transmitting the received question to a natural language processing engine to analyze the question; means for generating an answer based on a result of the analysis; and means for returning the generated answer to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In modern society, interest in environmentally conscious lifestyles is growing, but there is a lack of knowledge and information about the specific actions and choices individuals should take. Therefore, there is a need for a means by which users can easily ask questions about the environment and receive quick and accurate advice. However, existing technologies do not adequately provide systems that can generate appropriate answers to users' questions. [Means for solving the problem]

[0005] The present invention provides a system that accepts a question about the environment from a user, transmits the question to a natural language processing engine for analysis, generates an answer based on the analysis results, and returns the generated answer to the user. Specifically, the system includes (1) a means for accepting a question from a user, (2) a means for transmitting the accepted question to a natural language processing engine for analysis, (3) a means for generating an answer based on the analysis results, and (4) a means for returning the generated answer to the user. It is also desirable that, when transmitting the accepted question to the natural language processing engine, the system also includes a means for generating an appropriate prompt according to the content of the question and a means for displaying the generated answer on the user's interface. In this way, a system can be provided that allows a user to easily obtain appropriate advice about the environment.

[0006] "User" refers to anyone who uses the system, including individuals and organizations who ask questions about the environment.

[0007] "Environmental Questions" refers to questions or inquiries about environmental protection, sustainable living, and eco-friendly products and services.

[0008] A "natural language processing engine" refers to a computer program that understands, analyzes, and generates natural human language, and in this invention it is used to analyze user questions and generate appropriate responses.

[0009] "Analyzing" refers to the process of linguistically analyzing the question received from the user and understanding its content and intent.

[0010] "Generating an answer" refers to the natural language processing engine creating an appropriate response to the user's question based on the analysis results.

[0011] "Replying" refers to the means of communicating the generated answer to the user, specifically, displaying or transmitting the answer through a user interface.

[0012] "Generating a prompt" refers to creating text or instructions that trigger the natural language processing engine to perform analysis.

[0013] "User interface" refers to an interface that includes a screen and input means for a user to interact with a system. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

[0022] [First embodiment]

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

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

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

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

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

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

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

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

[0031] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0033] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

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

[0035] The present invention is a system that allows users to ask questions about the environment and provides appropriate advice and information in response to those questions. This system mainly consists of a server, a terminal, and a user.

[0036] System Overview

[0037] 1. User inputs a question:

[0038] Users open a web browser on their device (PC, smartphone, etc.) and access the EcoChat web page. They then enter their environmental questions using the interface provided.

[0039] 2. Sending and receiving questions:

[0040] When a user enters a question and presses the submit button, the device creates an HTTP POST request containing the question and sends it to the server. The server receives this request and parses the user's question, which is sent in JSON format.

[0041] 3. Question analysis and answer generation:

[0042] The server sends the received question to a natural language processing engine (e.g., a general natural language processing service) for analysis. Based on the analysis results, the server generates an appropriate answer to the question.

[0043] 4. Reply and display answers:

[0044] The server sends the generated answer back to the user's device as an HTTP response, which then receives the response and displays it in a user-friendly format.

[0045] Specific Examples

[0046] For example, suppose a user inputs a question such as, "Tell me about eco-friendly detergents." The roles of the user, server, and terminal are explained in detail below.

[0047] 1. User Action:

[0048] The user accesses the EcoChat web page, enters "Tell me about eco-friendly detergents" in the question input field, and presses the send button to send the question.

[0049] 2. Server processing:

[0050] The server receives the question and sends it to a natural language processing engine, which generates a response such as, "For eco-friendly detergents, we recommend those that contain biodegradable ingredients. For example, brand XX detergent is good." The server formats this response and sends it back to the user's device.

[0051] 3. Display terminal:

[0052] The terminal displays the response received from the server in the display area, allowing the user to quickly receive appropriate advice for the question they entered.

[0053] In this way, the system of the present invention makes it possible to instantly provide appropriate information in response to users' environmental concerns and questions. The system as a whole efficiently carries out a series of processes: accepting a user's question, analyzing it, generating an appropriate answer, and returning it to the user. As a result, users can obtain useful information to make environmentally conscious choices, and play a role in promoting sustainable lifestyles.

[0054] The processing flow will be explained below.

[0055] Step 1:

[0056] Users access the EcoChat web page through their device's web browser and enter environmental questions.

[0057] Step 2:

[0058] Once the user has finished entering their question, they press the submit button, which causes the device to make an HTTP POST request.

[0059] Step 3:

[0060] The device sends an HTTP POST request to the server containing the user's question, structured in JSON format.

[0061] Step 4:

[0062] The server receives the HTTP POST request and extracts the JSON formatted data from the request body.

[0063] Step 5:

[0064] The server retrieves the user's question from the extracted data and prepares it to be sent to a natural language processing engine.

[0065] Step 6:

[0066] The server sends the user's question to a natural language processing engine for analysis, which parses the question based on pre-defined prompts.

[0067] Step 7:

[0068] A natural language processing engine analyzes the user's question and generates an appropriate response based on the context, such as, "Eco-friendly detergents are recommended if they contain biodegradable ingredients."

[0069] Step 8:

[0070] The server formats the response received from the natural language processing engine and creates an HTTP response to send back to the user.

[0071] Step 9:

[0072] The server sends the created HTTP response to the terminal, which contains the generated response.

[0073] Step 10:

[0074] The terminal analyzes the HTTP response received from the server and displays the response contained therein on the user interface.

[0075] Step 11:

[0076] The user checks the response from the server through the terminal interface and receives specific advice and information regarding the question.

[0077] Example 1

[0078] 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."

[0079] In modern society, interest in environmental issues is growing, and many users want to make environmentally conscious choices in their daily lives. However, with so much information available, it is difficult for users to get quick and accurate answers to specific environmental questions. Furthermore, when specialized knowledge is required, much of the information is difficult for average users to understand, which can lead to incorrect choices. There is a need for a system that can solve these problems and allow users to easily obtain useful environmental information.

[0080] 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.

[0081] In this invention, the server includes means for accepting questions about the environment from users, means for transmitting the accepted questions as HTTP POST requests, means for analyzing the accepted questions and transmitting them to a natural language processing engine for analysis, means for returning generated answers to the user's terminal as HTTP responses, and means for displaying the answers received by the user's terminal. This enables users to easily input specific questions about the environment and receive prompt and accurate answers to those questions.

[0082] "User" refers to any individual or entity that uses the System to ask an environmental question.

[0083] "Environmental Questions" refers to specific inquiries related to the environment, such as eco-friendly products, recycling methods, and environmental protection activities.

[0084] An "HTTP POST request" is a type of protocol used to send a user's question to a server, and is used to send data securely.

[0085] "Server" refers to a computer system that receives a user's question, analyzes it, uses a natural language processing engine to generate an answer, and then returns it to the user.

[0086] A "natural language processing engine" refers to a software program or computer system that uses artificial intelligence techniques to analyze a user's question and generate an appropriate answer.

[0087] An "HTTP response" is a type of protocol used by a server to send a response to a user's device, and includes response data to the user's request.

[0088] A "terminal" is a device through which a user enters questions and receives answers, specifically a PC or smartphone.

[0089] "Interface" refers to the means of providing user-friendly user interaction, such as screens and input fields that users use to interact with a system.

[0090] A "prompt" refers to appropriate keywords or phrases generated based on the content of the question, and acts as an aid to the natural language processing engine in analyzing the question.

[0091] The present invention is a system that allows users to ask questions about the environment and provides appropriate advice and information in response to those questions. The entire system mainly consists of a server, a terminal, and a user.

[0092] User question input

[0093] Users access the EcoChat web page from a web browser on their device (PC, smartphone, etc.). This web page provides an interface for entering questions about the environment. Users use this interface to enter their questions and click the send button.

[0094] Submit a Question

[0095] The device sends the user-entered question to the server as an HTTP POST request, with the question packaged in JSON format in the request body. The device waits for a response from the server.

[0096] Receiving and parsing questions

[0097] The server receives the HTTP POST request from the device, parses the JSON format data from the request body, and sends the parsed question to a natural language processing engine (e.g., OpenAI GPT-3).

[0098] Generate answers

[0099] The server sends a question to the natural language processing engine and waits for the analysis result from the engine. The natural language processing engine generates an appropriate answer based on the user's question and sends it back to the server. The server receives the answer and formats it in a format that is easy for the user to read.

[0100] Returning the answer

[0101] The server sends the formatted answer to the terminal as an HTTP response. The response data format is JSON, and is parsed by the terminal.

[0102] Show Answers

[0103] The device parses the received JSON data and renders the answer to the question in the display area of ​​the web page, allowing users to get a quick and accurate answer to their entered question.

[0104] Specific examples

[0105] For example, if a user asks the question "What are recyclable plastics?", the following flow would occur:

[0106] 1. User Action:

[0107] Users visit the EcoChat web page, type in a question such as "Tell me about recyclable plastics," and click the submit button.

[0108] 2. Server processing:

[0109] The server receives this question and sends it to a natural language processing engine, which generates an answer: "Recyclable plastics include PET (polyethylene terephthalate) and HDPE (high-density polyethylene)." The server formats this and sends it back to the device.

[0110] 3. Display terminal:

[0111] The terminal displays the received answers in a display area, allowing the user to review the appropriate information for the question.

[0112] Prompt Sentence Examples

[0113] An example of a prompt that a user might enter is, "Tell me about eco-friendly detergents." By using such a prompt, the system can provide an appropriate answer immediately.

[0114] In this way, an efficient system is realized to provide users with fast and relevant information in response to their environmental questions and inquiries, which can serve as a catalyst for sustainable lifestyles.

[0115] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0116] Step 1: User enters question

[0117] The user accesses the EcoChat web page using a PC or smartphone, enters "Please tell me about eco-friendly detergents" in the question input field, and presses the send button. This input data is temporarily stored on the device and used in the next step.

[0118] Step 2: Submit your question

[0119] The device creates an HTTP POST request containing the question entered by the user. The request body contains the question in JSON format. For example, it contains data like {"question": "Tell me about eco-friendly detergents."}. This request is then sent to the server.

[0120] Step 3: Receiving and parsing the question

[0121] The server receives the HTTP POST request sent from the device. It extracts JSON data from the received request and obtains the user's question. For example, data like {"question": "Please tell me about eco-friendly detergents."} is extracted. This question is then sent to a natural language processing engine.

[0122] Step 4: Generate an answer

[0123] The server sends the question to a natural language processing engine (e.g., OpenAI GPT-3). The natural language processing engine analyzes the received question and generates an appropriate answer. For example, it may generate an answer such as, "For eco-friendly detergents, those containing biodegradable ingredients are recommended. For example, XX brand detergent is good." This generated answer is then sent back to the server.

[0124] Step 5: Format and submit your response

[0125] The server formats the answer returned by the natural language processing engine into a format that is easy for the user to understand. The formatted answer is packaged in JSON format and sent to the terminal as an HTTP response. For example, the following response data is generated: {"answer": "Eco-friendly detergents are recommended that contain biodegradable ingredients. For example, XX brand detergent is good."}

[0126] Step 6: View your answers

[0127] The device analyzes the HTTP response received from the server. It extracts the answer from the JSON data and renders it in the display area of ​​the web page. For example, it displays an answer such as, "Eco-friendly detergents are recommended to contain biodegradable ingredients. For example, XX brand detergent is good." The user can obtain appropriate information for the question they entered.

[0128] In this way, data is input, processed, and output between the user, terminal, and server at each step, resulting in a system that efficiently provides answers to questions.

[0129] (Application example 1)

[0130] 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."

[0131] Conventional environmental information systems have had the problem of making it difficult for users to obtain appropriate answers when they request environmental information about specific products or services. In particular, there are few systems that suggest eco-friendly products or brands, and the provision of information to support sustainable choices is insufficient. The purpose of this invention is to quickly and appropriately provide useful information to help users make environmentally conscious choices.

[0132] 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.

[0133] In this invention, the server includes means for accepting questions about the environment from users, means for transmitting the accepted questions to a natural language processing engine for analysis, means for generating answers based on the analysis results, means for suggesting environmentally friendly products based on the questions entered by the users, and means for returning the generated answers to the users. This allows users to not only receive appropriate answers to their questions about the environment, but also receive suggestions for eco-friendly products and brands.

[0134] The "means for accepting questions about the environment from users" is a function for receiving environment-related questions entered by users and incorporating them into the system.

[0135] The "means for transmitting the received question to a natural language processing engine for analysis" is a function for transmitting question data to the engine in order to analyze the question received from the user using natural language processing technology.

[0136] "Means for generating answers based on analysis results" refers to a function for creating appropriate answers to users' questions using the results analyzed by the natural language processing engine.

[0137] "Means for returning the generated answer to the user" refers to a function that refers to a communication means or display means for delivering the generated answer to the user.

[0138] "A means of suggesting environmentally friendly products based on questions entered by the user" is a function that searches for and suggests eco-friendly products and brands based on the content of the user's questions.

[0139] "Means for displaying on the user's interface" refers to a function for visually displaying answers and suggestions on the user's device so that the user can easily view the results.

[0140] Overall system configuration

[0141] This invention is a system that allows users to input questions about the environment and receive appropriate advice and product suggestions. The system mainly consists of a server, a terminal, and a user interface.

[0142] User Actions

[0143] Users access the dedicated application using a device such as a smartphone or PC. The application interface has a question input field where users can enter questions such as, "Please tell me about environmentally friendly clothing."

[0144] Server Processing

[0145] The server receives questions sent by users. The received questions are first sent to a natural language processing engine for analysis. Examples of natural language processing engines used here include Google Cloud Natural Language API and OpenAI's GPT-3. Based on the analysis results, the server generates an appropriate answer.

[0146] Another feature of the present invention is that it has a function to suggest eco-friendly products based on the analyzed question. For example, in response to the question "Tell me about environmentally friendly clothing," clothing brands and products that use biodegradable materials will be suggested.

[0147] Response to the user

[0148] The generated answers and product suggestions are sent back to the user's device as an HTTP response, which is then displayed in an easy-to-read interface. Specifically, the user's device displays a list of eco-friendly clothing brands and products, along with a detailed description of each product and why it is eco-friendly.

[0149] Examples and prompts

[0150] As a concrete example, consider the case where a user types in "Tell me about eco-friendly clothing." This question is parsed by a natural language processing engine as follows:

[0151] "Clothing brands that use biodegradable materials include XXX and YYY. They are environmentally conscious and use sustainable materials."

[0152] Example prompt sentence:

[0153] "Tell me about eco-friendly clothing."

[0154] This invention allows users to quickly obtain appropriate information and eco-friendly product suggestions in response to environmental questions, resulting in access to useful information to promote sustainable lifestyles.

[0155] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0156] Step 1:

[0157] A user starts the application on a terminal and enters a question about the environment in the question input field. For example, the user enters "Tell me about environmentally friendly clothing." This is the input data. The user presses the send button to send the question to the server.

[0158] Step 2:

[0159] The device sends the user-entered questions about the environment to the server as an HTTP POST request, where the question data is converted to JSON format. The input data is the user's question, and the output data is JSON-formatted question data.

[0160] Step 3:

[0161] The server sends the received question data to a natural language processing engine, which uses, for example, Google Cloud Natural Language API or OpenAI's GPT-3. The input data is the question data in JSON format, and the analysis results are generated as output data.

[0162] Step 4:

[0163] The server receives the analysis results from the natural language processing engine and generates answers based on them. It also makes eco-friendly product suggestions based on the user's question. The input data is the analysis results, and the output data is the answers and product suggestions.

[0164] Step 5:

[0165] The server sends the generated answer and eco-friendly product suggestions back to the user's terminal as an HTTP response. The input data are the generated answer and product suggestions, and the HTTP response is generated as output data.

[0166] Step 6:

[0167] The terminal analyzes the HTTP response received from the server and displays it in an easy-to-read interface for the user. The input data is the HTTP response, and the output data is the answer and product suggestions that are displayed to the user.

[0168] The system allows users to quickly and easily get relevant answers to their environmental questions and suggestions for eco-friendly products.

[0169] 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.

[0170] The present invention is a system that allows users to ask questions about the environment and provides appropriate advice and information in response to those questions. This system further improves the user experience by combining an emotion engine that analyzes the user's emotional state.

[0171] System Overview

[0172] 1. User inputs a question:

[0173] Users open a web browser on their device (PC, smartphone, etc.) and access the EcoChat web page, where they enter their environmental questions using the interface provided.

[0174] 2. Submitting Questions and Emotional Data:

[0175] When a user enters a question and presses the send button, the device sends an HTTP POST request to the server that includes the question along with the user's emotional data obtained using technologies such as facial recognition and text analysis.

[0176] 3. Receiving and analyzing question and emotion data:

[0177] The server parses the received HTTP POST request and extracts the user's question and emotion data from the JSON format data, which is based on facial expressions and sentiment analysis of the input text.

[0178] 4. Question content and emotional state analysis:

[0179] The received question is sent to a natural language processing engine for content analysis. In parallel, an emotion engine analyzes the user's emotional state (e.g., joy, sadness, anger, etc.). The server then adjusts the content and tone of the response based on the analysis results.

[0180] 5. Generate answers:

[0181] By comparing the analysis results from the natural language processing engine with the emotional data from the emotion engine, the server generates an answer that suits the user's emotional state. For example, if a user asks, "Tell me about eco-friendly detergents," and the emotion engine recognizes that the user is feeling depressed, the server will generate an answer with a positive, encouraging tone.

[0182] 6. Replying and displaying answers:

[0183] The server sends the generated answer as an HTTP response to the user's device, which then analyzes the response and displays it to the user in an appropriate interface. The analyzed emotion results can also be displayed in the user interface.

[0184] Specific Examples

[0185] For example, if a user types in a question like, "Tell me about eco-friendly detergents," the invention works as follows:

[0186] 1. User Action:

[0187] A user accesses the EcoChat web page and enters "Please tell me about eco-friendly detergents" in the question input field. By pressing the send button, emotion data is sent along with the question.

[0188] 2. Server processing:

[0189] The server receives the question and emotion data and sends the question to a natural language processing engine for analysis. At the same time, the emotion engine analyzes the user's emotion and determines that the user is depressed. The server integrates this information and generates a response with an encouraging tone.

[0190] 3. Display terminal:

[0191] The device displays the response received from the server in the display area, allowing the user to quickly receive an appropriate response that takes into account their current emotions, along with specific advice for their question.

[0192] In this way, the system of the present invention can instantly provide appropriate information and emotionally sensitive advice in response to a user's environmental concerns or questions. The system as a whole efficiently performs a series of processes: accepting a user's question and emotional data, analyzing them, and generating an appropriate answer to return to the user. As a result, the user can obtain useful information for making environmentally conscious choices and receive an emotionally sensitive response, thereby promoting a sustainable lifestyle.

[0193] The processing flow will be explained below.

[0194] Step 1:

[0195] Users access the EcoChat web page using their device's web browser and enter environmental questions.

[0196] Step 2:

[0197] After entering a question, the user presses the send button. This action causes the device to create an HTTP POST request that includes the question and emotional data (emotional state determined by facial expression analysis and text analysis).

[0198] Step 3:

[0199] The device sends an HTTP POST request containing the user's question and emotion data to the server.

[0200] Step 4:

[0201] The server receives the HTTP POST request and extracts JSON-formatted data from the request body, which contains the user's question and sentiment data.

[0202] Step 5:

[0203] The server obtains the user's question from the extracted data and first sends the question to a natural language processing engine for analysis.

[0204] Step 6:

[0205] In parallel, the server sends emotion data to the emotion engine to analyze the user's emotional state. This emotion data is obtained from the user's facial expressions and input text.

[0206] Step 7:

[0207] A natural language processing engine analyzes the user's question and generates an answer based on the content.

[0208] Step 8:

[0209] An emotion engine analyzes the user's emotional state and determines, for example, whether the user is depressed, happy, angry, etc.

[0210] Step 9:

[0211] The server compares the answers from the natural language processing engine with the emotional data from the emotion engine to generate a final answer with a tone and content that suits the user's emotional state.

[0212] Step 10:

[0213] The server formats the generated answer as an HTTP response and sends it to the user's device.

[0214] Step 11:

[0215] The device analyzes the received HTTP response and displays the answer and a message reflecting the user's emotional state on the interface.

[0216] Step 12:

[0217] Users can view responses through the device interface and get specific advice and information to answer their questions, while emotion-sensitive responses improve the user experience.

[0218] Example 2

[0219] 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."

[0220] In modern society, users are increasingly asking questions about the environment. However, conventional systems have difficulty providing quick and appropriate answers to these questions. Furthermore, they generate answers without taking the user's emotional state into account, resulting in a poor user experience. Therefore, there is a need for a system that can analyze the user's questions and emotional state and provide appropriate information and advice that takes their emotions into consideration.

[0221] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0222] In this invention, the server includes means for accepting questions about the environment from a user, means for analyzing the accepted questions and the emotional state of the user, means for generating an answer based on the analysis result and the emotional state, and means for returning the generated answer to the user, thereby enabling the user to quickly obtain specific advice in response to the question and a response that takes into consideration the user's emotions.

[0223] A "user" is an individual or entity that enters a question into the system and receives a response.

[0224] The "means for accepting questions" refers to an interface or mechanism for obtaining questions entered by users.

[0225] A "natural language processing engine" is an algorithm or system that analyzes input text data and understands and processes its content.

[0226] "Means of analysis" refers to the mechanisms and processes used to analyze acquired data and understand its content and characteristics.

[0227] "Emotional state" is data that indicates the user's emotions, including emotions such as joy, sadness, and anger.

[0228] An "emotion engine" is an algorithm or system for analyzing a user's emotional state.

[0229] The "answer generation means" is the mechanism or process for creating an appropriate answer based on the analyzed data.

[0230] A "means for responding to the user" is the interface or mechanism for providing the generated answer to the user.

[0231] A "prompt" is an instruction or guideline that the system generates in response to a question.

[0232] An "interface" is a screen or operating means that allows a user to interact with a system.

[0233] "Analysis results" are the conclusions and information obtained from data by natural language processing engines and emotion engines.

[0234] The present invention is a system that allows users to ask questions about the environment and provides appropriate advice and information in response to those questions. This system further improves the user experience by combining an emotion engine that analyzes the user's emotional state.

[0235] A user opens a web browser on a device (PC, smartphone, etc.) and accesses the EcoChat web page. The user uses the interface provided to enter an environmental question. For example, they might enter, "Tell me about eco-friendly detergents."

[0236] When a user enters a question and presses the send button, the device uses JavaScript to send an HTTP POST request to the server, which includes the question and the user's emotion data obtained using technologies such as facial recognition and text analysis. OpenCV is used for facial recognition, and an NLP library is used for text emotion analysis.

[0237] The server parses the received HTTP POST request and extracts the user's question and emotion data from the JSON format data. The server processes the request and analyzes the data using the Python Flask framework. To analyze the emotional state, it uses an emotion engine built using machine learning frameworks such as TensorFlow and PyTorch.

[0238] Next, the received question is sent to a natural language processing engine (e.g., GPT-3), which analyzes the question content. At the same time, an emotion engine analyzes the user's emotional state (e.g., joy, sadness, anger, etc.). For example, if a question and emotion data are sent such as "Tell me about eco-friendly detergents," the server sends the question to the GPT-3 engine for analysis, while the emotion engine analyzes the user's emotional state.

[0239] The server combines the analysis results from the natural language processing engine with the emotional data from the emotion engine to generate an answer appropriate to the user's emotional state. If the server recognizes that the user is feeling down based on the emotional data, it generates an answer with a positive and encouraging tone. For example, it generates an answer such as, "Eco-friendly detergents are a good choice for the planet because they use environmentally friendly ingredients. Cheer up, and every choice you make is a step towards protecting the planet!"

[0240] The server sends the generated answer to the user's device as an HTTP response. The device then parses the received JSON data using JavaScript and displays the answer and sentiment analysis results in a designated area of ​​the webpage. This allows the user to quickly receive specific advice and an appropriate response that takes sentiment into consideration.

[0241] As a concrete example, if a user inputs the question "Tell me about eco-friendly detergents," the system works as follows:

[0242] 1. User Action:

[0243] The user accesses the EcoChat web page and enters "Please tell me about eco-friendly detergents" in the question input field. Then, the user presses the send button to send the question along with the emotion data.

[0244] 2. Server processing:

[0245] The server receives the question and emotion data and sends the question to a natural language processing engine for analysis. At the same time, the emotion engine analyzes the user's emotion and determines that the user is depressed. The server integrates this information and generates a response with an encouraging tone.

[0246] 3. Display terminal:

[0247] The terminal displays the response received from the server in the display area, allowing the user to quickly receive an appropriate response that takes into account their current feelings, along with specific advice for the question.

[0248] An example prompt might be, "User: Tell me about eco-friendly detergent. Emotional state: Depressed Question: Eco-friendly detergent Answer: Eco-friendly detergents are a green choice because they use environmentally friendly ingredients. Cheer up, every choice is a step towards saving the planet!"

[0249] In this way, the system of the present invention can instantly provide appropriate information and emotionally sensitive advice in response to a user's environmental concerns or questions. The system as a whole efficiently performs a series of processes: accepting a user's question and emotional data, analyzing them, generating an appropriate answer, and returning it to the user. As a result, the user can obtain useful information for making environmentally conscious choices and receive an emotionally sensitive response, thereby promoting a sustainable lifestyle.

[0250] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0251] Step 1:

[0252] Question input from the user

[0253] The user opens a web browser on their device and accesses the EcoChat web page. Using the interface provided, the user enters a question into the question input field. For example, they might enter, "Tell me about eco-friendly detergents." Once the user has completed their input and pressed the submit button, their input will proceed to the next step.

[0254] Input: User question text

[0255] Output: Question text ready to send

[0256] Step 2:

[0257] Submitting questions and sentiment data

[0258] When the user presses the send button, the device uses JavaScript to send the question along with the user's emotional data obtained using facial recognition and text analysis techniques. The device uses OpenCV to recognize faces from camera images and an NLP library to analyze the emotions in the text data. This information is sent to the server as an HTTP POST request.

[0259] Input: Question text, facial image data, text data

[0260] Output: HTTP POST request (question text and sentiment data)

[0261] Step 3:

[0262] Receiving and analyzing questions and sentiment data

[0263] The server receives the HTTP POST request and parses its contents. Using the Flask framework, the server extracts the user's question and sentiment data from the received JSON data. The question text is converted into a format that can be sent to a natural language processing engine, and the sentiment data is converted into the format required for analysis.

[0264] Input: HTTP POST request

[0265] Output: Question text, sentiment data

[0266] Step 4:

[0267] Question content and emotional state analysis

[0268] The server sends the extracted question to a natural language processing engine (e.g., GPT-3) to analyze the question. At the same time, an emotion engine (e.g., a TensorFlow model) analyzes the received emotion data and identifies the user's emotional state (e.g., happy, sad, depressed). The analysis results are used in the next step.

[0269] Input: Question text, emotion data

[0270] Output: Question analysis results, emotional state data

[0271] Step 5:

[0272] Generate answers

[0273] The server integrates the analysis results obtained from the natural language processing engine with the emotional state data obtained from the emotion engine. The server uses a generative AI model to generate an appropriate response that is appropriate for the user's emotional state. For example, if the user is feeling down, the server selects and generates a response with a positive and encouraging tone.

[0274] Input: Question analysis results, emotional state data

[0275] Output: Emotionally sensitive answer text

[0276] Step 6:

[0277] Replying and displaying answers

[0278] The server sends the generated answer to the user's device as an HTTP response. The device then parses the received JSON data using JavaScript and displays the answer and sentiment analysis results in a designated area of ​​the webpage. This allows the user to quickly receive specific advice and a response that takes sentiment into consideration.

[0279] Input: Emotionally sensitive answer text

[0280] Output: Answers and emotional states displayed in a user interface

[0281] Through these processing steps, users can quickly receive specific advice and appropriate responses that take their emotions into consideration, providing useful information for making environmentally conscious choices and a pleasant user experience.

[0282] (Application example 2)

[0283] 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."

[0284] Modern brick-and-mortar stores are required to provide quick and accurate answers to questions about the environment. However, it is difficult to properly understand the emotional state of customers and respond based on that, making improving customer satisfaction a challenge. It is also difficult for employees to provide appropriate information in real time while serving customers. To solve these issues, a system using emotion analysis is needed.

[0285] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0286] In this invention, the server includes means for accepting questions about the environment from users, means for transmitting the accepted questions to a natural language processing engine for analysis, means for generating answers based on the analysis results, means for returning the generated answers to the users, means for analyzing the emotional state of the users, and means for integrating the questions and emotional data to generate answers appropriate to the emotions of the users. This makes it possible to provide quick and accurate answers while taking the emotional state of the users into consideration, thereby improving customer satisfaction.

[0287] The "means for receiving a question about the environment from a user" refers to a means for a user to use an input device to send a question about the environment to the system.

[0288] "Means for sending the received question to a natural language processing engine for analysis" refers to means for the system to send the question received from the user to a natural language processing engine and analyze its content.

[0289] "Means for generating an answer based on the analysis results" refers to the means by which the system generates an appropriate answer based on the analysis results from the natural language processing engine.

[0290] The "means for returning the generated answer to the user" refers to the means by which the system notifies or displays the generated answer to the user.

[0291] "Means for analyzing the user's emotional state" refers to means for analyzing the user's emotions and psychological state using voice analysis, facial recognition technology, etc.

[0292] "Means for integrating questions and emotional data to generate answers that match the user's emotions" refers to means for integrating the content of a question from a user with emotional data and, based on that, generating an answer with a tone and content that is optimal for the user's emotional state.

[0293] The "means for displaying the generated answer on a display device" is a means for displaying the answer generated by the system on a display device used in the store.

[0294] MODE FOR CARRYING OUT THE INVENTION

[0295] This invention is a system for accepting customer questions in a physical store and providing appropriate answers. This system also analyzes the user's emotional state and provides answers that take emotions into consideration, thereby improving customer satisfaction. Specific embodiments are described below.

[0296] System Hardware and Software

[0297] The main hardware of the system is as follows:

[0298] Smart glasses (e.g., Google Glass): A device that receives customer questions via voice and displays the analysis results.

[0299] Server: The central system that analyzes questions and sentiment data and generates appropriate answers.

[0300] The main software of the system is as follows:

[0301] Speech Recognition Module: Software that converts customer speech into text.

[0302] Natural language processing engine: Software that analyzes incoming questions.

[0303] Sentiment analysis engine: Software that analyzes user emotions based on voice and facial recognition.

[0304] Smart Glasses SDK: Software development kit for controlling smart glasses and displaying results.

[0305] Processing the data

[0306] The server processes the data as follows:

[0307] 1. Acquire voice input: Acquire customer's voice input through smart glasses. The voice recognition module converts the voice into text.

[0308] 2. Emotion analysis: Using voice data, the emotion analysis engine analyzes the user's emotions, thereby recognizing their emotional state, such as whether they are interested or anxious.

[0309] 3. Question analysis: The natural language processing engine analyzes and understands the question.

[0310] 4. Answer Generation: Based on the analysis results and emotional data, the server generates an appropriate answer. Based on the emotional data, it selects the most appropriate tone, such as a positive and encouraging tone or a calm and specific tone.

[0311] 5. Displaying the answer: The generated answer is displayed on the smart glasses so that the appropriate response can be provided to the customer.

[0312] Specific examples

[0313] For example, consider the following scenario in a brick-and-mortar store:

[0314] 1. Customer: Ask, "What eco-friendly products do you recommend these days?"

[0315] 2. Smart glasses (voice input and sentiment analysis): The voice recognition module converts the customer's question into text, and the sentiment analysis engine analyzes it as "interested."

[0316] 3. Server: Analyzes the question using a natural language processing engine and generates an answer with a positive tone based on the emotional data of "interested."

[0317] 4. Smart glasses (displaying answers): The store clerk displays answers such as, "How about this eco bag? It's very popular."

[0318] Prompt Sentence Examples

[0319] An example of a prompt is as follows:

[0320] "A customer is interested in eco-friendly products and has a question: 'What eco-friendly products do you recommend these days?' They seem interested. Use the right tone to provide specific recommendations."

[0321] In this way, the system can improve the efficiency of customer service in physical stores and increase customer satisfaction.

[0322] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0323] Step 1:

[0324] The user wears the smart glasses and receives customer questions by voice. The input is the customer's voice question, and the output is voice data. Specifically, the smart glasses' microphone picks up the customer's voice, and the built-in voice recognition module acquires the voice data.

[0325] Step 2:

[0326] The speech recognition module converts speech data into text. The input is speech data and the output is text data. Specifically, the speech recognition algorithm analyzes the speech waveform and converts it into a string of characters.

[0327] Step 3:

[0328] The smart glasses send text data to an emotion analysis engine to analyze the user's emotional state. The input is text data and voice data, and the output is emotion data. Specifically, the emotion analysis engine performs calculations to infer emotions from the tone of voice and text.

[0329] Step 4:

[0330] The smart glasses send text data and emotion data to the server. The input is text data and emotion data, and the output is an HTTP POST request to the server. Specifically, the communication module of the smart glasses converts the data into packets and sends them to the server.

[0331] Step 5:

[0332] The server analyzes the received text data and emotion data and generates an appropriate response using a generative AI model for automatic responses. The input is text data and emotion data, and the output is the response text. Specifically, the generative AI model integrates the text and emotion to construct the optimal response sentence.

[0333] Step 6:

[0334] The server sends the generated answer text to the smart glasses. The input is the answer text, and the output is an HTTP response to the smart glasses. Specifically, the server's communication module converts the data into packets and sends them to the smart glasses.

[0335] Step 7:

[0336] The smart glasses display the received answer text to the store clerk. The input is the answer text received from the server, and the output is the answer displayed on the display of the smart glasses. In specific operation, the display device of the smart glasses visually displays the text for the store clerk to confirm.

[0337] 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.

[0338] 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.

[0339] 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.

[0340] [Second embodiment]

[0341] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0342] 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.

[0343] 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).

[0344] 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.

[0345] 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.

[0346] 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).

[0347] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0348] 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.

[0349] 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.

[0350] 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.

[0351] 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.

[0352] 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."

[0353] The present invention is a system that allows users to ask questions about the environment and provides appropriate advice and information in response to those questions. This system mainly consists of a server, a terminal, and a user.

[0354] System Overview

[0355] 1. User inputs a question:

[0356] Users open a web browser on their device (PC, smartphone, etc.) and access the EcoChat web page. They then enter their environmental questions using the interface provided.

[0357] 2. Sending and receiving questions:

[0358] When a user enters a question and presses the submit button, the device creates an HTTP POST request containing the question and sends it to the server. The server receives this request and parses the user's question, which is sent in JSON format.

[0359] 3. Question analysis and answer generation:

[0360] The server sends the received question to a natural language processing engine (e.g., a general natural language processing service) for analysis. Based on the analysis results, the server generates an appropriate answer to the question.

[0361] 4. Reply and display answers:

[0362] The server sends the generated answer back to the user's device as an HTTP response, which then receives the response and displays it in a user-friendly format.

[0363] Specific Examples

[0364] For example, suppose a user inputs a question such as, "Tell me about eco-friendly detergents." The roles of the user, server, and terminal are explained in detail below.

[0365] 1. User Action:

[0366] The user accesses the EcoChat web page, enters "Tell me about eco-friendly detergents" in the question input field, and presses the send button to send the question.

[0367] 2. Server processing:

[0368] The server receives the question and sends it to a natural language processing engine, which generates a response such as, "For eco-friendly detergents, we recommend those that contain biodegradable ingredients. For example, brand XX detergent is good." The server formats this response and sends it back to the user's device.

[0369] 3. Display terminal:

[0370] The terminal displays the response received from the server in the display area, allowing the user to quickly receive appropriate advice for the question they entered.

[0371] In this way, the system of the present invention makes it possible to instantly provide appropriate information in response to users' environmental concerns and questions. The system as a whole efficiently carries out a series of processes: accepting a user's question, analyzing it, generating an appropriate answer, and returning it to the user. As a result, users can obtain useful information to make environmentally conscious choices, and play a role in promoting sustainable lifestyles.

[0372] The processing flow will be explained below.

[0373] Step 1:

[0374] Users access the EcoChat web page through their device's web browser and enter environmental questions.

[0375] Step 2:

[0376] Once the user has finished entering their question, they press the submit button, which causes the device to make an HTTP POST request.

[0377] Step 3:

[0378] The device sends an HTTP POST request to the server containing the user's question, structured in JSON format.

[0379] Step 4:

[0380] The server receives the HTTP POST request and extracts the JSON formatted data from the request body.

[0381] Step 5:

[0382] The server retrieves the user's question from the extracted data and prepares it to be sent to a natural language processing engine.

[0383] Step 6:

[0384] The server sends the user's question to a natural language processing engine for analysis, which parses the question based on pre-defined prompts.

[0385] Step 7:

[0386] A natural language processing engine analyzes the user's question and generates an appropriate response based on the context, such as, "Eco-friendly detergents are recommended if they contain biodegradable ingredients."

[0387] Step 8:

[0388] The server formats the response received from the natural language processing engine and creates an HTTP response to send back to the user.

[0389] Step 9:

[0390] The server sends the created HTTP response to the terminal, which contains the generated response.

[0391] Step 10:

[0392] The terminal analyzes the HTTP response received from the server and displays the response contained therein on the user interface.

[0393] Step 11:

[0394] The user checks the response from the server through the terminal interface and receives specific advice and information regarding the question.

[0395] Example 1

[0396] 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."

[0397] In modern society, interest in environmental issues is growing, and many users want to make environmentally conscious choices in their daily lives. However, with so much information available, it is difficult for users to get quick and accurate answers to specific environmental questions. Furthermore, when specialized knowledge is required, much of the information is difficult for average users to understand, which can lead to incorrect choices. There is a need for a system that can solve these problems and allow users to easily obtain useful environmental information.

[0398] 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.

[0399] In this invention, the server includes means for accepting questions about the environment from users, means for transmitting the accepted questions as HTTP POST requests, means for analyzing the accepted questions and transmitting them to a natural language processing engine for analysis, means for returning generated answers to the user's terminal as HTTP responses, and means for displaying the answers received by the user's terminal. This enables users to easily input specific questions about the environment and receive prompt and accurate answers to those questions.

[0400] "User" refers to any individual or entity that uses the System to ask an environmental question.

[0401] "Environmental Questions" refers to specific inquiries related to the environment, such as eco-friendly products, recycling methods, and environmental protection activities.

[0402] An "HTTP POST request" is a type of protocol used to send a user's question to a server, and is used to send data securely.

[0403] "Server" refers to a computer system that receives a user's question, analyzes it, uses a natural language processing engine to generate an answer, and then returns it to the user.

[0404] A "natural language processing engine" refers to a software program or computer system that uses artificial intelligence techniques to analyze a user's question and generate an appropriate answer.

[0405] An "HTTP response" is a type of protocol used by a server to send a response to a user's device, and includes response data to the user's request.

[0406] A "terminal" is a device through which a user enters questions and receives answers, specifically a PC or smartphone.

[0407] "Interface" refers to the means of providing user-friendly user interaction, such as screens and input fields that users use to interact with a system.

[0408] A "prompt" refers to appropriate keywords or phrases generated based on the content of the question, and acts as an aid to the natural language processing engine in analyzing the question.

[0409] The present invention is a system that allows users to ask questions about the environment and provides appropriate advice and information in response to those questions. The entire system mainly consists of a server, a terminal, and a user.

[0410] User question input

[0411] Users access the EcoChat web page from a web browser on their device (PC, smartphone, etc.). This web page provides an interface for entering questions about the environment. Users use this interface to enter their questions and click the send button.

[0412] Submit a Question

[0413] The device sends the user-entered question to the server as an HTTP POST request, with the question packaged in JSON format in the request body. The device waits for a response from the server.

[0414] Receiving and parsing questions

[0415] The server receives the HTTP POST request from the device, parses the JSON format data from the request body, and sends the parsed question to a natural language processing engine (e.g., OpenAI GPT-3).

[0416] Generate answers

[0417] The server sends a question to the natural language processing engine and waits for the analysis result from the engine. The natural language processing engine generates an appropriate answer based on the user's question and sends it back to the server. The server receives the answer and formats it in a format that is easy for the user to read.

[0418] Returning the answer

[0419] The server sends the formatted answer to the terminal as an HTTP response. The response data format is JSON, and is parsed by the terminal.

[0420] Show Answers

[0421] The device parses the received JSON data and renders the answer to the question in the display area of ​​the web page, allowing users to get a quick and accurate answer to their entered question.

[0422] Specific examples

[0423] For example, if a user asks the question "What are recyclable plastics?", the following flow would occur:

[0424] 1. User Action:

[0425] Users visit the EcoChat web page, type in a question such as "Tell me about recyclable plastics," and click the submit button.

[0426] 2. Server processing:

[0427] The server receives this question and sends it to a natural language processing engine, which generates an answer: "Recyclable plastics include PET (polyethylene terephthalate) and HDPE (high-density polyethylene)." The server formats this and sends it back to the device.

[0428] 3. Display terminal:

[0429] The terminal displays the received answers in a display area, allowing the user to review the appropriate information for the question.

[0430] Prompt Sentence Examples

[0431] An example of a prompt that a user might enter is, "Tell me about eco-friendly detergents." By using such a prompt, the system can provide an appropriate answer immediately.

[0432] In this way, an efficient system is realized to provide users with fast and relevant information in response to their environmental questions and inquiries, which can serve as a catalyst for sustainable lifestyles.

[0433] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0434] Step 1: User enters question

[0435] The user accesses the EcoChat web page using a PC or smartphone, enters "Please tell me about eco-friendly detergents" in the question input field, and presses the send button. This input data is temporarily stored on the device and used in the next step.

[0436] Step 2: Submit your question

[0437] The device creates an HTTP POST request containing the question entered by the user. The request body contains the question in JSON format. For example, it contains data like {"question": "Tell me about eco-friendly detergents."}. This request is then sent to the server.

[0438] Step 3: Receiving and parsing the question

[0439] The server receives the HTTP POST request sent from the device. It extracts JSON data from the received request and obtains the user's question. For example, data like {"question": "Please tell me about eco-friendly detergents."} is extracted. This question is then sent to a natural language processing engine.

[0440] Step 4: Generate an answer

[0441] The server sends the question to a natural language processing engine (e.g., OpenAI GPT-3). The natural language processing engine analyzes the received question and generates an appropriate answer. For example, it may generate an answer such as, "For eco-friendly detergents, those containing biodegradable ingredients are recommended. For example, XX brand detergent is good." This generated answer is then sent back to the server.

[0442] Step 5: Format and submit your response

[0443] The server formats the answer returned by the natural language processing engine into a format that is easy for the user to understand. The formatted answer is packaged in JSON format and sent to the terminal as an HTTP response. For example, the following response data is generated: {"answer": "Eco-friendly detergents are recommended that contain biodegradable ingredients. For example, XX brand detergent is good."}

[0444] Step 6: View your answers

[0445] The device analyzes the HTTP response received from the server. It extracts the answer from the JSON data and renders it in the display area of ​​the web page. For example, it displays an answer such as, "Eco-friendly detergents are recommended to contain biodegradable ingredients. For example, XX brand detergent is good." The user can obtain appropriate information for the question they entered.

[0446] In this way, data is input, processed, and output between the user, terminal, and server at each step, resulting in a system that efficiently provides answers to questions.

[0447] (Application example 1)

[0448] 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."

[0449] Conventional environmental information systems have had the problem of making it difficult for users to obtain appropriate answers when they request environmental information about specific products or services. In particular, there are few systems that suggest eco-friendly products or brands, and the provision of information to support sustainable choices is insufficient. The purpose of this invention is to quickly and appropriately provide useful information to help users make environmentally conscious choices.

[0450] 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.

[0451] In this invention, the server includes means for accepting questions about the environment from users, means for transmitting the accepted questions to a natural language processing engine for analysis, means for generating answers based on the analysis results, means for suggesting environmentally friendly products based on the questions entered by the users, and means for returning the generated answers to the users. This allows users to not only receive appropriate answers to their questions about the environment, but also receive suggestions for eco-friendly products and brands.

[0452] The "means for accepting questions about the environment from users" is a function for receiving environment-related questions entered by users and incorporating them into the system.

[0453] The "means for transmitting the received question to a natural language processing engine for analysis" is a function for transmitting question data to the engine in order to analyze the question received from the user using natural language processing technology.

[0454] "Means for generating answers based on analysis results" refers to a function for creating appropriate answers to users' questions using the results analyzed by the natural language processing engine.

[0455] "Means for returning the generated answer to the user" refers to a function that refers to a communication means or display means for delivering the generated answer to the user.

[0456] "A means of suggesting environmentally friendly products based on questions entered by the user" is a function that searches for and suggests eco-friendly products and brands based on the content of the user's questions.

[0457] "Means for displaying on the user's interface" refers to a function for visually displaying answers and suggestions on the user's device so that the user can easily view the results.

[0458] Overall system configuration

[0459] This invention is a system that allows users to input questions about the environment and receive appropriate advice and product suggestions. The system mainly consists of a server, a terminal, and a user interface.

[0460] User Actions

[0461] Users access the dedicated application using a device such as a smartphone or PC. The application interface has a question input field where users can enter questions such as, "Please tell me about environmentally friendly clothing."

[0462] Server Processing

[0463] The server receives questions sent by users. The received questions are first sent to a natural language processing engine for analysis. Examples of natural language processing engines used here include Google Cloud Natural Language API and OpenAI's GPT-3. Based on the analysis results, the server generates an appropriate answer.

[0464] Another feature of the present invention is that it has a function to suggest eco-friendly products based on the analyzed question. For example, in response to the question "Tell me about environmentally friendly clothing," clothing brands and products that use biodegradable materials will be suggested.

[0465] Response to the user

[0466] The generated answers and product suggestions are sent back to the user's device as an HTTP response, which is then displayed in an easy-to-read interface. Specifically, the user's device displays a list of eco-friendly clothing brands and products, along with a detailed description of each product and why it is eco-friendly.

[0467] Examples and prompts

[0468] As a concrete example, consider the case where a user types in "Tell me about eco-friendly clothing." This question is parsed by a natural language processing engine as follows:

[0469] "Clothing brands that use biodegradable materials include XXX and YYY. They are environmentally conscious and use sustainable materials."

[0470] Example prompt sentence:

[0471] "Tell me about eco-friendly clothing."

[0472] This invention allows users to quickly obtain appropriate information and eco-friendly product suggestions in response to environmental questions, resulting in access to useful information to promote sustainable lifestyles.

[0473] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0474] Step 1:

[0475] A user starts the application on a terminal and enters a question about the environment in the question input field. For example, the user enters "Tell me about environmentally friendly clothing." This is the input data. The user presses the send button to send the question to the server.

[0476] Step 2:

[0477] The device sends the user-entered questions about the environment to the server as an HTTP POST request, where the question data is converted to JSON format. The input data is the user's question, and the output data is JSON-formatted question data.

[0478] Step 3:

[0479] The server sends the received question data to a natural language processing engine, which uses, for example, Google Cloud Natural Language API or OpenAI's GPT-3. The input data is the question data in JSON format, and the analysis results are generated as output data.

[0480] Step 4:

[0481] The server receives the analysis results from the natural language processing engine and generates answers based on them. It also makes eco-friendly product suggestions based on the user's question. The input data is the analysis results, and the output data is the answers and product suggestions.

[0482] Step 5:

[0483] The server sends the generated answer and eco-friendly product suggestions back to the user's terminal as an HTTP response. The input data are the generated answer and product suggestions, and the HTTP response is generated as output data.

[0484] Step 6:

[0485] The terminal analyzes the HTTP response received from the server and displays it in an easy-to-read interface for the user. The input data is the HTTP response, and the output data is the answer and product suggestions that are displayed to the user.

[0486] The system allows users to quickly and easily get relevant answers to their environmental questions and suggestions for eco-friendly products.

[0487] 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.

[0488] The present invention is a system that allows users to ask questions about the environment and provides appropriate advice and information in response to those questions. This system further improves the user experience by combining an emotion engine that analyzes the user's emotional state.

[0489] System Overview

[0490] 1. User inputs a question:

[0491] Users open a web browser on their device (PC, smartphone, etc.) and access the EcoChat web page, where they enter their environmental questions using the interface provided.

[0492] 2. Submitting Questions and Emotional Data:

[0493] When a user enters a question and presses the send button, the device sends an HTTP POST request to the server that includes the question along with the user's emotional data obtained using technologies such as facial recognition and text analysis.

[0494] 3. Receiving and analyzing question and emotion data:

[0495] The server parses the received HTTP POST request and extracts the user's question and emotion data from the JSON format data, which is based on facial expressions and sentiment analysis of the input text.

[0496] 4. Question content and emotional state analysis:

[0497] The received question is sent to a natural language processing engine for content analysis. In parallel, an emotion engine analyzes the user's emotional state (e.g., joy, sadness, anger, etc.). The server then adjusts the content and tone of the response based on the analysis results.

[0498] 5. Generate answers:

[0499] By comparing the analysis results from the natural language processing engine with the emotional data from the emotion engine, the server generates an answer that suits the user's emotional state. For example, if a user asks, "Tell me about eco-friendly detergents," and the emotion engine recognizes that the user is feeling depressed, the server will generate an answer with a positive, encouraging tone.

[0500] 6. Replying and displaying answers:

[0501] The server sends the generated answer as an HTTP response to the user's device, which then analyzes the response and displays it to the user in an appropriate interface. The analyzed emotion results can also be displayed in the user interface.

[0502] Specific Examples

[0503] For example, if a user types in a question like, "Tell me about eco-friendly detergents," the invention works as follows:

[0504] 1. User Action:

[0505] A user accesses the EcoChat web page and enters "Please tell me about eco-friendly detergents" in the question input field. By pressing the send button, emotion data is sent along with the question.

[0506] 2. Server processing:

[0507] The server receives the question and emotion data and sends the question to a natural language processing engine for analysis. At the same time, the emotion engine analyzes the user's emotion and determines that the user is depressed. The server integrates this information and generates a response with an encouraging tone.

[0508] 3. Display terminal:

[0509] The device displays the response received from the server in the display area, allowing the user to quickly receive an appropriate response that takes into account their current emotions, along with specific advice for their question.

[0510] In this way, the system of the present invention can instantly provide appropriate information and emotionally sensitive advice in response to a user's environmental concerns or questions. The system as a whole efficiently performs a series of processes: accepting a user's question and emotional data, analyzing them, and generating an appropriate answer to return to the user. As a result, the user can obtain useful information for making environmentally conscious choices and receive an emotionally sensitive response, thereby promoting a sustainable lifestyle.

[0511] The processing flow will be explained below.

[0512] Step 1:

[0513] Users access the EcoChat web page using their device's web browser and enter environmental questions.

[0514] Step 2:

[0515] After entering a question, the user presses the send button. This action causes the device to create an HTTP POST request that includes the question and emotional data (emotional state determined by facial expression analysis and text analysis).

[0516] Step 3:

[0517] The device sends an HTTP POST request containing the user's question and emotion data to the server.

[0518] Step 4:

[0519] The server receives the HTTP POST request and extracts JSON-formatted data from the request body, which contains the user's question and sentiment data.

[0520] Step 5:

[0521] The server obtains the user's question from the extracted data and first sends the question to a natural language processing engine for analysis.

[0522] Step 6:

[0523] In parallel, the server sends emotion data to the emotion engine to analyze the user's emotional state. This emotion data is obtained from the user's facial expressions and input text.

[0524] Step 7:

[0525] A natural language processing engine analyzes the user's question and generates an answer based on the content.

[0526] Step 8:

[0527] An emotion engine analyzes the user's emotional state and determines, for example, whether the user is depressed, happy, angry, etc.

[0528] Step 9:

[0529] The server compares the answers from the natural language processing engine with the emotional data from the emotion engine to generate a final answer with a tone and content that suits the user's emotional state.

[0530] Step 10:

[0531] The server formats the generated answer as an HTTP response and sends it to the user's device.

[0532] Step 11:

[0533] The device analyzes the received HTTP response and displays the answer and a message reflecting the user's emotional state on the interface.

[0534] Step 12:

[0535] Users can view responses through the device interface and get specific advice and information to answer their questions, while emotion-sensitive responses improve the user experience.

[0536] Example 2

[0537] 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."

[0538] In modern society, users are increasingly asking questions about the environment. However, conventional systems have difficulty providing quick and appropriate answers to these questions. Furthermore, they generate answers without taking the user's emotional state into account, resulting in a poor user experience. Therefore, there is a need for a system that can analyze the user's questions and emotional state and provide appropriate information and advice that takes their emotions into consideration.

[0539] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0540] In this invention, the server includes means for accepting questions about the environment from a user, means for analyzing the accepted questions and the emotional state of the user, means for generating an answer based on the analysis result and the emotional state, and means for returning the generated answer to the user, thereby enabling the user to quickly obtain specific advice in response to the question and a response that takes into consideration the user's emotions.

[0541] A "user" is an individual or entity that enters a question into the system and receives a response.

[0542] The "means for accepting questions" refers to an interface or mechanism for obtaining questions entered by users.

[0543] A "natural language processing engine" is an algorithm or system that analyzes input text data and understands and processes its content.

[0544] "Means of analysis" refers to the mechanisms and processes used to analyze acquired data and understand its content and characteristics.

[0545] "Emotional state" is data that indicates the user's emotions, including emotions such as joy, sadness, and anger.

[0546] An "emotion engine" is an algorithm or system for analyzing a user's emotional state.

[0547] The "answer generation means" is the mechanism or process for creating an appropriate answer based on the analyzed data.

[0548] A "means for responding to the user" is the interface or mechanism for providing the generated answer to the user.

[0549] A "prompt" is an instruction or guideline that the system generates in response to a question.

[0550] An "interface" is a screen or operating means that allows a user to interact with a system.

[0551] "Analysis results" are the conclusions and information obtained from data by natural language processing engines and emotion engines.

[0552] The present invention is a system that allows users to ask questions about the environment and provides appropriate advice and information in response to those questions. This system further improves the user experience by combining an emotion engine that analyzes the user's emotional state.

[0553] A user opens a web browser on a device (PC, smartphone, etc.) and accesses the EcoChat web page. The user uses the interface provided to enter an environmental question. For example, they might enter, "Tell me about eco-friendly detergents."

[0554] When a user enters a question and presses the send button, the device uses JavaScript to send an HTTP POST request to the server, which includes the question and the user's emotion data obtained using technologies such as facial recognition and text analysis. OpenCV is used for facial recognition, and an NLP library is used for text emotion analysis.

[0555] The server parses the received HTTP POST request and extracts the user's question and emotion data from the JSON format data. The server processes the request and analyzes the data using the Python Flask framework. To analyze the emotional state, it uses an emotion engine built using machine learning frameworks such as TensorFlow and PyTorch.

[0556] Next, the received question is sent to a natural language processing engine (e.g., GPT-3), which analyzes the question content. At the same time, an emotion engine analyzes the user's emotional state (e.g., joy, sadness, anger, etc.). For example, if a question and emotion data are sent such as "Tell me about eco-friendly detergents," the server sends the question to the GPT-3 engine for analysis, while the emotion engine analyzes the user's emotional state.

[0557] The server combines the analysis results from the natural language processing engine with the emotional data from the emotion engine to generate an answer appropriate to the user's emotional state. If the server recognizes that the user is feeling down based on the emotional data, it generates an answer with a positive and encouraging tone. For example, it generates an answer such as, "Eco-friendly detergents are a good choice for the planet because they use environmentally friendly ingredients. Cheer up, and every choice you make is a step towards protecting the planet!"

[0558] The server sends the generated answer to the user's device as an HTTP response. The device then parses the received JSON data using JavaScript and displays the answer and sentiment analysis results in a designated area of ​​the webpage. This allows the user to quickly receive specific advice and an appropriate response that takes sentiment into consideration.

[0559] As a concrete example, if a user inputs the question "Tell me about eco-friendly detergents," the system works as follows:

[0560] 1. User Action:

[0561] The user accesses the EcoChat web page and enters "Please tell me about eco-friendly detergents" in the question input field. Then, the user presses the send button to send the question along with the emotion data.

[0562] 2. Server processing:

[0563] The server receives the question and emotion data and sends the question to a natural language processing engine for analysis. At the same time, the emotion engine analyzes the user's emotion and determines that the user is depressed. The server integrates this information and generates a response with an encouraging tone.

[0564] 3. Display terminal:

[0565] The terminal displays the response received from the server in the display area, allowing the user to quickly receive an appropriate response that takes into account their current feelings, along with specific advice for the question.

[0566] An example prompt might be, "User: Tell me about eco-friendly detergent. Emotional state: Depressed Question: Eco-friendly detergent Answer: Eco-friendly detergents are a green choice because they use environmentally friendly ingredients. Cheer up, every choice is a step towards saving the planet!"

[0567] In this way, the system of the present invention can instantly provide appropriate information and emotionally sensitive advice in response to a user's environmental concerns or questions. The system as a whole efficiently performs a series of processes: accepting a user's question and emotional data, analyzing them, generating an appropriate answer, and returning it to the user. As a result, the user can obtain useful information for making environmentally conscious choices and receive an emotionally sensitive response, thereby promoting a sustainable lifestyle.

[0568] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0569] Step 1:

[0570] Question input from the user

[0571] The user opens a web browser on their device and accesses the EcoChat web page. Using the interface provided, the user enters a question into the question input field. For example, they might enter, "Tell me about eco-friendly detergents." Once the user has completed their input and pressed the submit button, their input will proceed to the next step.

[0572] Input: User question text

[0573] Output: Question text ready to send

[0574] Step 2:

[0575] Submitting questions and sentiment data

[0576] When the user presses the send button, the device uses JavaScript to send the question along with the user's emotional data obtained using facial recognition and text analysis techniques. The device uses OpenCV to recognize faces from camera images and an NLP library to analyze the emotions in the text data. This information is sent to the server as an HTTP POST request.

[0577] Input: Question text, facial image data, text data

[0578] Output: HTTP POST request (question text and sentiment data)

[0579] Step 3:

[0580] Receiving and analyzing questions and sentiment data

[0581] The server receives the HTTP POST request and parses its contents. Using the Flask framework, the server extracts the user's question and sentiment data from the received JSON data. The question text is converted into a format that can be sent to a natural language processing engine, and the sentiment data is converted into the format required for analysis.

[0582] Input: HTTP POST request

[0583] Output: Question text, sentiment data

[0584] Step 4:

[0585] Question content and emotional state analysis

[0586] The server sends the extracted question to a natural language processing engine (e.g., GPT-3) to analyze the question. At the same time, an emotion engine (e.g., a TensorFlow model) analyzes the received emotion data and identifies the user's emotional state (e.g., happy, sad, depressed). The analysis results are used in the next step.

[0587] Input: Question text, emotion data

[0588] Output: Question analysis results, emotional state data

[0589] Step 5:

[0590] Generate answers

[0591] The server integrates the analysis results obtained from the natural language processing engine with the emotional state data obtained from the emotion engine. The server uses a generative AI model to generate an appropriate response that is appropriate for the user's emotional state. For example, if the user is feeling down, the server selects and generates a response with a positive and encouraging tone.

[0592] Input: Question analysis results, emotional state data

[0593] Output: Emotionally sensitive answer text

[0594] Step 6:

[0595] Replying and displaying answers

[0596] The server sends the generated answer to the user's device as an HTTP response. The device then parses the received JSON data using JavaScript and displays the answer and sentiment analysis results in a designated area of ​​the webpage. This allows the user to quickly receive specific advice and a response that takes sentiment into consideration.

[0597] Input: Emotionally sensitive answer text

[0598] Output: Answers and emotional states displayed in a user interface

[0599] Through these processing steps, users can quickly receive specific advice and appropriate responses that take their emotions into consideration, providing useful information for making environmentally conscious choices and a pleasant user experience.

[0600] (Application example 2)

[0601] 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."

[0602] Modern brick-and-mortar stores are required to provide quick and accurate answers to questions about the environment. However, it is difficult to properly understand the emotional state of customers and respond based on that, making improving customer satisfaction a challenge. It is also difficult for employees to provide appropriate information in real time while serving customers. To solve these issues, a system using emotion analysis is needed.

[0603] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0604] In this invention, the server includes means for accepting questions about the environment from users, means for transmitting the accepted questions to a natural language processing engine for analysis, means for generating answers based on the analysis results, means for returning the generated answers to the users, means for analyzing the emotional state of the users, and means for integrating the questions and emotional data to generate answers appropriate to the emotions of the users. This makes it possible to provide quick and accurate answers while taking the emotional state of the users into consideration, thereby improving customer satisfaction.

[0605] The "means for receiving a question about the environment from a user" refers to a means for a user to use an input device to send a question about the environment to the system.

[0606] "Means for sending the received question to a natural language processing engine for analysis" refers to means for the system to send the question received from the user to a natural language processing engine and analyze its content.

[0607] "Means for generating an answer based on the analysis results" refers to the means by which the system generates an appropriate answer based on the analysis results from the natural language processing engine.

[0608] The "means for returning the generated answer to the user" refers to the means by which the system notifies or displays the generated answer to the user.

[0609] "Means for analyzing the user's emotional state" refers to means for analyzing the user's emotions and psychological state using voice analysis, facial recognition technology, etc.

[0610] "Means for integrating questions and emotional data to generate answers that match the user's emotions" refers to means for integrating the content of a question from a user with emotional data and, based on that, generating an answer with a tone and content that is optimal for the user's emotional state.

[0611] The "means for displaying the generated answer on a display device" is a means for displaying the answer generated by the system on a display device used in the store.

[0612] MODE FOR CARRYING OUT THE INVENTION

[0613] This invention is a system for accepting customer questions in a physical store and providing appropriate answers. This system also analyzes the user's emotional state and provides answers that take emotions into consideration, thereby improving customer satisfaction. Specific embodiments are described below.

[0614] System Hardware and Software

[0615] The main hardware of the system is as follows:

[0616] Smart glasses (e.g., Google Glass): A device that receives customer questions via voice and displays the analysis results.

[0617] Server: The central system that analyzes questions and sentiment data and generates appropriate answers.

[0618] The main software of the system is as follows:

[0619] Speech Recognition Module: Software that converts customer speech into text.

[0620] Natural language processing engine: Software that analyzes incoming questions.

[0621] Sentiment analysis engine: Software that analyzes user emotions based on voice and facial recognition.

[0622] Smart Glasses SDK: Software development kit for controlling smart glasses and displaying results.

[0623] Processing the data

[0624] The server processes the data as follows:

[0625] 1. Acquire voice input: Acquire customer's voice input through smart glasses. The voice recognition module converts the voice into text.

[0626] 2. Emotion analysis: Using voice data, the emotion analysis engine analyzes the user's emotions, thereby recognizing their emotional state, such as whether they are interested or anxious.

[0627] 3. Question analysis: The natural language processing engine analyzes and understands the question.

[0628] 4. Answer Generation: Based on the analysis results and emotional data, the server generates an appropriate answer. Based on the emotional data, it selects the most appropriate tone, such as a positive and encouraging tone or a calm and specific tone.

[0629] 5. Displaying the answer: The generated answer is displayed on the smart glasses so that the appropriate response can be provided to the customer.

[0630] Specific examples

[0631] For example, consider the following scenario in a brick-and-mortar store:

[0632] 1. Customer: Ask, "What eco-friendly products do you recommend these days?"

[0633] 2. Smart glasses (voice input and sentiment analysis): The voice recognition module converts the customer's question into text, and the sentiment analysis engine analyzes it as "interested."

[0634] 3. Server: Analyzes the question using a natural language processing engine and generates an answer with a positive tone based on the emotional data of "interested."

[0635] 4. Smart glasses (displaying answers): The store clerk displays answers such as, "How about this eco bag? It's very popular."

[0636] Prompt Sentence Examples

[0637] An example of a prompt is as follows:

[0638] "A customer is interested in eco-friendly products and has a question: 'What eco-friendly products do you recommend these days?' They seem interested. Use the right tone to provide specific recommendations."

[0639] In this way, the system can improve the efficiency of customer service in physical stores and increase customer satisfaction.

[0640] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0641] Step 1:

[0642] The user wears the smart glasses and receives customer questions by voice. The input is the customer's voice question, and the output is voice data. Specifically, the smart glasses' microphone picks up the customer's voice, and the built-in voice recognition module acquires the voice data.

[0643] Step 2:

[0644] The speech recognition module converts speech data into text. The input is speech data and the output is text data. Specifically, the speech recognition algorithm analyzes the speech waveform and converts it into a string of characters.

[0645] Step 3:

[0646] The smart glasses send text data to an emotion analysis engine to analyze the user's emotional state. The input is text data and voice data, and the output is emotion data. Specifically, the emotion analysis engine performs calculations to infer emotions from the tone of voice and text.

[0647] Step 4:

[0648] The smart glasses send text data and emotion data to the server. The input is text data and emotion data, and the output is an HTTP POST request to the server. Specifically, the communication module of the smart glasses converts the data into packets and sends them to the server.

[0649] Step 5:

[0650] The server analyzes the received text data and emotion data and generates an appropriate response using a generative AI model for automatic responses. The input is text data and emotion data, and the output is the response text. Specifically, the generative AI model integrates the text and emotion to construct the optimal response sentence.

[0651] Step 6:

[0652] The server sends the generated answer text to the smart glasses. The input is the answer text, and the output is an HTTP response to the smart glasses. Specifically, the server's communication module converts the data into packets and sends them to the smart glasses.

[0653] Step 7:

[0654] The smart glasses display the received answer text to the store clerk. The input is the answer text received from the server, and the output is the answer displayed on the display of the smart glasses. In specific operation, the display device of the smart glasses visually displays the text for the store clerk to confirm.

[0655] 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.

[0656] 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.

[0657] 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.

[0658] [Third embodiment]

[0659] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0660] 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.

[0661] 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).

[0662] 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.

[0663] 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.

[0664] 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).

[0665] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0666] 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.

[0667] 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.

[0668] 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.

[0669] 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.

[0670] 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."

[0671] The present invention is a system that allows users to ask questions about the environment and provides appropriate advice and information in response to those questions. This system mainly consists of a server, a terminal, and a user.

[0672] System Overview

[0673] 1. User inputs a question:

[0674] Users open a web browser on their device (PC, smartphone, etc.) and access the EcoChat web page. They then enter their environmental questions using the interface provided.

[0675] 2. Sending and receiving questions:

[0676] When a user enters a question and presses the submit button, the device creates an HTTP POST request containing the question and sends it to the server. The server receives this request and parses the user's question, which is sent in JSON format.

[0677] 3. Question analysis and answer generation:

[0678] The server sends the received question to a natural language processing engine (e.g., a general natural language processing service) for analysis. Based on the analysis results, the server generates an appropriate answer to the question.

[0679] 4. Reply and display answers:

[0680] The server sends the generated answer back to the user's device as an HTTP response, which then receives the response and displays it in a user-friendly format.

[0681] Specific Examples

[0682] For example, suppose a user inputs a question such as, "Tell me about eco-friendly detergents." The roles of the user, server, and terminal are explained in detail below.

[0683] 1. User Action:

[0684] The user accesses the EcoChat web page, enters "Tell me about eco-friendly detergents" in the question input field, and presses the send button to send the question.

[0685] 2. Server processing:

[0686] The server receives the question and sends it to a natural language processing engine, which generates a response such as, "For eco-friendly detergents, we recommend those that contain biodegradable ingredients. For example, brand XX detergent is good." The server formats this response and sends it back to the user's device.

[0687] 3. Display terminal:

[0688] The terminal displays the response received from the server in the display area, allowing the user to quickly receive appropriate advice for the question they entered.

[0689] In this way, the system of the present invention makes it possible to instantly provide appropriate information in response to users' environmental concerns and questions. The system as a whole efficiently carries out a series of processes: accepting a user's question, analyzing it, generating an appropriate answer, and returning it to the user. As a result, users can obtain useful information to make environmentally conscious choices, and play a role in promoting sustainable lifestyles.

[0690] The processing flow will be explained below.

[0691] Step 1:

[0692] Users access the EcoChat web page through their device's web browser and enter environmental questions.

[0693] Step 2:

[0694] Once the user has finished entering their question, they press the submit button, which causes the device to make an HTTP POST request.

[0695] Step 3:

[0696] The device sends an HTTP POST request to the server containing the user's question, structured in JSON format.

[0697] Step 4:

[0698] The server receives the HTTP POST request and extracts the JSON formatted data from the request body.

[0699] Step 5:

[0700] The server retrieves the user's question from the extracted data and prepares it to be sent to a natural language processing engine.

[0701] Step 6:

[0702] The server sends the user's question to a natural language processing engine for analysis, which parses the question based on pre-defined prompts.

[0703] Step 7:

[0704] A natural language processing engine analyzes the user's question and generates an appropriate response based on the context, such as, "Eco-friendly detergents are recommended if they contain biodegradable ingredients."

[0705] Step 8:

[0706] The server formats the response received from the natural language processing engine and creates an HTTP response to send back to the user.

[0707] Step 9:

[0708] The server sends the created HTTP response to the terminal, which contains the generated response.

[0709] Step 10:

[0710] The terminal analyzes the HTTP response received from the server and displays the response contained therein on the user interface.

[0711] Step 11:

[0712] The user checks the response from the server through the terminal interface and receives specific advice and information regarding the question.

[0713] Example 1

[0714] 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."

[0715] In modern society, interest in environmental issues is growing, and many users want to make environmentally conscious choices in their daily lives. However, with so much information available, it is difficult for users to get quick and accurate answers to specific environmental questions. Furthermore, when specialized knowledge is required, much of the information is difficult for average users to understand, which can lead to incorrect choices. There is a need for a system that can solve these problems and allow users to easily obtain useful environmental information.

[0716] 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.

[0717] In this invention, the server includes means for accepting questions about the environment from users, means for transmitting the accepted questions as HTTP POST requests, means for analyzing the accepted questions and transmitting them to a natural language processing engine for analysis, means for returning generated answers to the user's terminal as HTTP responses, and means for displaying the answers received by the user's terminal. This enables users to easily input specific questions about the environment and receive prompt and accurate answers to those questions.

[0718] "User" refers to any individual or entity that uses the System to ask an environmental question.

[0719] "Environmental Questions" refers to specific inquiries related to the environment, such as eco-friendly products, recycling methods, and environmental protection activities.

[0720] An "HTTP POST request" is a type of protocol used to send a user's question to a server, and is used to send data securely.

[0721] "Server" refers to a computer system that receives a user's question, analyzes it, uses a natural language processing engine to generate an answer, and then returns it to the user.

[0722] A "natural language processing engine" refers to a software program or computer system that uses artificial intelligence techniques to analyze a user's question and generate an appropriate answer.

[0723] An "HTTP response" is a type of protocol used by a server to send a response to a user's device, and includes response data to the user's request.

[0724] A "terminal" is a device through which a user enters questions and receives answers, specifically a PC or smartphone.

[0725] "Interface" refers to the means of providing user-friendly user interaction, such as screens and input fields that users use to interact with a system.

[0726] A "prompt" refers to appropriate keywords or phrases generated based on the content of the question, and acts as an aid to the natural language processing engine in analyzing the question.

[0727] The present invention is a system that allows users to ask questions about the environment and provides appropriate advice and information in response to those questions. The entire system mainly consists of a server, a terminal, and a user.

[0728] User question input

[0729] Users access the EcoChat web page from a web browser on their device (PC, smartphone, etc.). This web page provides an interface for entering questions about the environment. Users use this interface to enter their questions and click the send button.

[0730] Submit a Question

[0731] The device sends the user-entered question to the server as an HTTP POST request, with the question packaged in JSON format in the request body. The device waits for a response from the server.

[0732] Receiving and parsing questions

[0733] The server receives the HTTP POST request from the device, parses the JSON format data from the request body, and sends the parsed question to a natural language processing engine (e.g., OpenAI GPT-3).

[0734] Generate answers

[0735] The server sends a question to the natural language processing engine and waits for the analysis result from the engine. The natural language processing engine generates an appropriate answer based on the user's question and sends it back to the server. The server receives the answer and formats it in a format that is easy for the user to read.

[0736] Returning the answer

[0737] The server sends the formatted answer to the terminal as an HTTP response. The response data format is JSON, and is parsed by the terminal.

[0738] Show Answers

[0739] The device parses the received JSON data and renders the answer to the question in the display area of ​​the web page, allowing users to get a quick and accurate answer to their entered question.

[0740] Specific examples

[0741] For example, if a user asks the question "What are recyclable plastics?", the following flow would occur:

[0742] 1. User Action:

[0743] Users visit the EcoChat web page, type in a question such as "Tell me about recyclable plastics," and click the submit button.

[0744] 2. Server processing:

[0745] The server receives this question and sends it to a natural language processing engine, which generates an answer: "Recyclable plastics include PET (polyethylene terephthalate) and HDPE (high-density polyethylene)." The server formats this and sends it back to the device.

[0746] 3. Display terminal:

[0747] The terminal displays the received answers in a display area, allowing the user to review the appropriate information for the question.

[0748] Prompt Sentence Examples

[0749] An example of a prompt that a user might enter is, "Tell me about eco-friendly detergents." By using such a prompt, the system can provide an appropriate answer immediately.

[0750] In this way, an efficient system is realized to provide users with fast and relevant information in response to their environmental questions and inquiries, which can serve as a catalyst for sustainable lifestyles.

[0751] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0752] Step 1: User enters question

[0753] The user accesses the EcoChat web page using a PC or smartphone, enters "Please tell me about eco-friendly detergents" in the question input field, and presses the send button. This input data is temporarily stored on the device and used in the next step.

[0754] Step 2: Submit your question

[0755] The device creates an HTTP POST request containing the question entered by the user. The request body contains the question in JSON format. For example, it contains data like {"question": "Tell me about eco-friendly detergents."}. This request is then sent to the server.

[0756] Step 3: Receiving and parsing the question

[0757] The server receives the HTTP POST request sent from the device. It extracts JSON data from the received request and obtains the user's question. For example, data like {"question": "Please tell me about eco-friendly detergents."} is extracted. This question is then sent to a natural language processing engine.

[0758] Step 4: Generate an answer

[0759] The server sends the question to a natural language processing engine (e.g., OpenAI GPT-3). The natural language processing engine analyzes the received question and generates an appropriate answer. For example, it may generate an answer such as, "For eco-friendly detergents, those containing biodegradable ingredients are recommended. For example, XX brand detergent is good." This generated answer is then sent back to the server.

[0760] Step 5: Format and submit your response

[0761] The server formats the answer returned by the natural language processing engine into a format that is easy for the user to understand. The formatted answer is packaged in JSON format and sent to the terminal as an HTTP response. For example, the following response data is generated: {"answer": "Eco-friendly detergents are recommended that contain biodegradable ingredients. For example, XX brand detergent is good."}

[0762] Step 6: View your answers

[0763] The device analyzes the HTTP response received from the server. It extracts the answer from the JSON data and renders it in the display area of ​​the web page. For example, it displays an answer such as, "Eco-friendly detergents are recommended to contain biodegradable ingredients. For example, XX brand detergent is good." The user can obtain appropriate information for the question they entered.

[0764] In this way, data is input, processed, and output between the user, terminal, and server at each step, resulting in a system that efficiently provides answers to questions.

[0765] (Application example 1)

[0766] 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."

[0767] Conventional environmental information systems have had the problem of making it difficult for users to obtain appropriate answers when they request environmental information about specific products or services. In particular, there are few systems that suggest eco-friendly products or brands, and the provision of information to support sustainable choices is insufficient. The purpose of this invention is to quickly and appropriately provide useful information to help users make environmentally conscious choices.

[0768] 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.

[0769] In this invention, the server includes means for accepting questions about the environment from users, means for transmitting the accepted questions to a natural language processing engine for analysis, means for generating answers based on the analysis results, means for suggesting environmentally friendly products based on the questions entered by the users, and means for returning the generated answers to the users. This allows users to not only receive appropriate answers to their questions about the environment, but also receive suggestions for eco-friendly products and brands.

[0770] The "means for accepting questions about the environment from users" is a function for receiving environment-related questions entered by users and incorporating them into the system.

[0771] The "means for transmitting the received question to a natural language processing engine for analysis" is a function for transmitting question data to the engine in order to analyze the question received from the user using natural language processing technology.

[0772] "Means for generating answers based on analysis results" refers to a function for creating appropriate answers to users' questions using the results analyzed by the natural language processing engine.

[0773] "Means for returning the generated answer to the user" refers to a function that refers to a communication means or display means for delivering the generated answer to the user.

[0774] "A means of suggesting environmentally friendly products based on questions entered by the user" is a function that searches for and suggests eco-friendly products and brands based on the content of the user's questions.

[0775] "Means for displaying on the user's interface" refers to a function for visually displaying answers and suggestions on the user's device so that the user can easily view the results.

[0776] Overall system configuration

[0777] This invention is a system that allows users to input questions about the environment and receive appropriate advice and product suggestions. The system mainly consists of a server, a terminal, and a user interface.

[0778] User Actions

[0779] Users access the dedicated application using a device such as a smartphone or PC. The application interface has a question input field where users can enter questions such as, "Please tell me about environmentally friendly clothing."

[0780] Server Processing

[0781] The server receives questions sent by users. The received questions are first sent to a natural language processing engine for analysis. Examples of natural language processing engines used here include Google Cloud Natural Language API and OpenAI's GPT-3. Based on the analysis results, the server generates an appropriate answer.

[0782] Another feature of the present invention is that it has a function to suggest eco-friendly products based on the analyzed question. For example, in response to the question "Tell me about environmentally friendly clothing," clothing brands and products that use biodegradable materials will be suggested.

[0783] Response to the user

[0784] The generated answers and product suggestions are sent back to the user's device as an HTTP response, which is then displayed in an easy-to-read interface. Specifically, the user's device displays a list of eco-friendly clothing brands and products, along with a detailed description of each product and why it is eco-friendly.

[0785] Examples and prompts

[0786] As a concrete example, consider the case where a user types in "Tell me about eco-friendly clothing." This question is parsed by a natural language processing engine as follows:

[0787] "Clothing brands that use biodegradable materials include XXX and YYY. They are environmentally conscious and use sustainable materials."

[0788] Example prompt sentence:

[0789] "Tell me about eco-friendly clothing."

[0790] This invention allows users to quickly obtain appropriate information and eco-friendly product suggestions in response to environmental questions, resulting in access to useful information to promote sustainable lifestyles.

[0791] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0792] Step 1:

[0793] A user starts the application on a terminal and enters a question about the environment in the question input field. For example, the user enters "Tell me about environmentally friendly clothing." This is the input data. The user presses the send button to send the question to the server.

[0794] Step 2:

[0795] The device sends the user-entered questions about the environment to the server as an HTTP POST request, where the question data is converted to JSON format. The input data is the user's question, and the output data is JSON-formatted question data.

[0796] Step 3:

[0797] The server sends the received question data to a natural language processing engine, which uses, for example, Google Cloud Natural Language API or OpenAI's GPT-3. The input data is the question data in JSON format, and the analysis results are generated as output data.

[0798] Step 4:

[0799] The server receives the analysis results from the natural language processing engine and generates answers based on them. It also makes eco-friendly product suggestions based on the user's question. The input data is the analysis results, and the output data is the answers and product suggestions.

[0800] Step 5:

[0801] The server sends the generated answer and eco-friendly product suggestions back to the user's terminal as an HTTP response. The input data are the generated answer and product suggestions, and the HTTP response is generated as output data.

[0802] Step 6:

[0803] The terminal analyzes the HTTP response received from the server and displays it in an easy-to-read interface for the user. The input data is the HTTP response, and the output data is the answer and product suggestions that are displayed to the user.

[0804] The system allows users to quickly and easily get relevant answers to their environmental questions and suggestions for eco-friendly products.

[0805] 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.

[0806] The present invention is a system that allows users to ask questions about the environment and provides appropriate advice and information in response to those questions. This system further improves the user experience by combining an emotion engine that analyzes the user's emotional state.

[0807] System Overview

[0808] 1. User inputs a question:

[0809] Users open a web browser on their device (PC, smartphone, etc.) and access the EcoChat web page, where they enter their environmental questions using the interface provided.

[0810] 2. Submitting Questions and Emotional Data:

[0811] When a user enters a question and presses the send button, the device sends an HTTP POST request to the server that includes the question along with the user's emotional data obtained using technologies such as facial recognition and text analysis.

[0812] 3. Receiving and analyzing question and emotion data:

[0813] The server parses the received HTTP POST request and extracts the user's question and emotion data from the JSON format data, which is based on facial expressions and sentiment analysis of the input text.

[0814] 4. Question content and emotional state analysis:

[0815] The received question is sent to a natural language processing engine for content analysis. In parallel, an emotion engine analyzes the user's emotional state (e.g., joy, sadness, anger, etc.). The server then adjusts the content and tone of the response based on the analysis results.

[0816] 5. Generate answers:

[0817] By comparing the analysis results from the natural language processing engine with the emotional data from the emotion engine, the server generates an answer that suits the user's emotional state. For example, if a user asks, "Tell me about eco-friendly detergents," and the emotion engine recognizes that the user is feeling depressed, the server will generate an answer with a positive, encouraging tone.

[0818] 6. Replying and displaying answers:

[0819] The server sends the generated answer as an HTTP response to the user's device, which then analyzes the response and displays it to the user in an appropriate interface. The analyzed emotion results can also be displayed in the user interface.

[0820] Specific Examples

[0821] For example, if a user types in a question like, "Tell me about eco-friendly detergents," the invention works as follows:

[0822] 1. User Action:

[0823] A user accesses the EcoChat web page and enters "Please tell me about eco-friendly detergents" in the question input field. By pressing the send button, emotion data is sent along with the question.

[0824] 2. Server processing:

[0825] The server receives the question and emotion data and sends the question to a natural language processing engine for analysis. At the same time, the emotion engine analyzes the user's emotion and determines that the user is depressed. The server integrates this information and generates a response with an encouraging tone.

[0826] 3. Display terminal:

[0827] The device displays the response received from the server in the display area, allowing the user to quickly receive an appropriate response that takes into account their current emotions, along with specific advice for their question.

[0828] In this way, the system of the present invention can instantly provide appropriate information and emotionally sensitive advice in response to a user's environmental concerns or questions. The system as a whole efficiently performs a series of processes: accepting a user's question and emotional data, analyzing them, and generating an appropriate answer to return to the user. As a result, the user can obtain useful information for making environmentally conscious choices and receive an emotionally sensitive response, thereby promoting a sustainable lifestyle.

[0829] The processing flow will be explained below.

[0830] Step 1:

[0831] Users access the EcoChat web page using their device's web browser and enter environmental questions.

[0832] Step 2:

[0833] After entering a question, the user presses the send button. This action causes the device to create an HTTP POST request that includes the question and emotional data (emotional state determined by facial expression analysis and text analysis).

[0834] Step 3:

[0835] The device sends an HTTP POST request containing the user's question and emotion data to the server.

[0836] Step 4:

[0837] The server receives the HTTP POST request and extracts JSON-formatted data from the request body, which contains the user's question and sentiment data.

[0838] Step 5:

[0839] The server obtains the user's question from the extracted data and first sends the question to a natural language processing engine for analysis.

[0840] Step 6:

[0841] In parallel, the server sends emotion data to the emotion engine to analyze the user's emotional state. This emotion data is obtained from the user's facial expressions and input text.

[0842] Step 7:

[0843] A natural language processing engine analyzes the user's question and generates an answer based on the content.

[0844] Step 8:

[0845] An emotion engine analyzes the user's emotional state and determines, for example, whether the user is depressed, happy, angry, etc.

[0846] Step 9:

[0847] The server compares the answers from the natural language processing engine with the emotional data from the emotion engine to generate a final answer with a tone and content that suits the user's emotional state.

[0848] Step 10:

[0849] The server formats the generated answer as an HTTP response and sends it to the user's device.

[0850] Step 11:

[0851] The device analyzes the received HTTP response and displays the answer and a message reflecting the user's emotional state on the interface.

[0852] Step 12:

[0853] Users can view responses through the device interface and get specific advice and information to answer their questions, while emotion-sensitive responses improve the user experience.

[0854] Example 2

[0855] 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."

[0856] In modern society, users are increasingly asking questions about the environment. However, conventional systems have difficulty providing quick and appropriate answers to these questions. Furthermore, they generate answers without taking the user's emotional state into account, resulting in a poor user experience. Therefore, there is a need for a system that can analyze the user's questions and emotional state and provide appropriate information and advice that takes their emotions into consideration.

[0857] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0858] In this invention, the server includes means for accepting questions about the environment from a user, means for analyzing the accepted questions and the emotional state of the user, means for generating an answer based on the analysis result and the emotional state, and means for returning the generated answer to the user, thereby enabling the user to quickly obtain specific advice in response to the question and a response that takes into consideration the user's emotions.

[0859] A "user" is an individual or entity that enters a question into the system and receives a response.

[0860] The "means for accepting questions" refers to an interface or mechanism for obtaining questions entered by users.

[0861] A "natural language processing engine" is an algorithm or system that analyzes input text data and understands and processes its content.

[0862] "Means of analysis" refers to the mechanisms and processes used to analyze acquired data and understand its content and characteristics.

[0863] "Emotional state" is data that indicates the user's emotions, including emotions such as joy, sadness, and anger.

[0864] An "emotion engine" is an algorithm or system for analyzing a user's emotional state.

[0865] The "answer generation means" is the mechanism or process for creating an appropriate answer based on the analyzed data.

[0866] A "means for responding to the user" is the interface or mechanism for providing the generated answer to the user.

[0867] A "prompt" is an instruction or guideline that the system generates in response to a question.

[0868] An "interface" is a screen or operating means that allows a user to interact with a system.

[0869] "Analysis results" are the conclusions and information obtained from data by natural language processing engines and emotion engines.

[0870] The present invention is a system that allows users to ask questions about the environment and provides appropriate advice and information in response to those questions. This system further improves the user experience by combining an emotion engine that analyzes the user's emotional state.

[0871] A user opens a web browser on a device (PC, smartphone, etc.) and accesses the EcoChat web page. The user uses the interface provided to enter an environmental question. For example, they might enter, "Tell me about eco-friendly detergents."

[0872] When a user enters a question and presses the send button, the device uses JavaScript to send an HTTP POST request to the server, which includes the question and the user's emotion data obtained using technologies such as facial recognition and text analysis. OpenCV is used for facial recognition, and an NLP library is used for text emotion analysis.

[0873] The server parses the received HTTP POST request and extracts the user's question and emotion data from the JSON format data. The server processes the request and analyzes the data using the Python Flask framework. To analyze the emotional state, it uses an emotion engine built using machine learning frameworks such as TensorFlow and PyTorch.

[0874] Next, the received question is sent to a natural language processing engine (e.g., GPT-3), which analyzes the question content. At the same time, an emotion engine analyzes the user's emotional state (e.g., joy, sadness, anger, etc.). For example, if a question and emotion data are sent such as "Tell me about eco-friendly detergents," the server sends the question to the GPT-3 engine for analysis, while the emotion engine analyzes the user's emotional state.

[0875] The server combines the analysis results from the natural language processing engine with the emotional data from the emotion engine to generate an answer appropriate to the user's emotional state. If the server recognizes that the user is feeling down based on the emotional data, it generates an answer with a positive and encouraging tone. For example, it generates an answer such as, "Eco-friendly detergents are a good choice for the planet because they use environmentally friendly ingredients. Cheer up, and every choice you make is a step towards protecting the planet!"

[0876] The server sends the generated answer to the user's device as an HTTP response. The device then parses the received JSON data using JavaScript and displays the answer and sentiment analysis results in a designated area of ​​the webpage. This allows the user to quickly receive specific advice and an appropriate response that takes sentiment into consideration.

[0877] As a concrete example, if a user inputs the question "Tell me about eco-friendly detergents," the system works as follows:

[0878] 1. User Action:

[0879] The user accesses the EcoChat web page and enters "Please tell me about eco-friendly detergents" in the question input field. Then, the user presses the send button to send the question along with the emotion data.

[0880] 2. Server processing:

[0881] The server receives the question and emotion data and sends the question to a natural language processing engine for analysis. At the same time, the emotion engine analyzes the user's emotion and determines that the user is depressed. The server integrates this information and generates a response with an encouraging tone.

[0882] 3. Display terminal:

[0883] The terminal displays the response received from the server in the display area, allowing the user to quickly receive an appropriate response that takes into account their current feelings, along with specific advice for the question.

[0884] An example prompt might be, "User: Tell me about eco-friendly detergent. Emotional state: Depressed Question: Eco-friendly detergent Answer: Eco-friendly detergents are a green choice because they use environmentally friendly ingredients. Cheer up, every choice is a step towards saving the planet!"

[0885] In this way, the system of the present invention can instantly provide appropriate information and emotionally sensitive advice in response to a user's environmental concerns or questions. The system as a whole efficiently performs a series of processes: accepting a user's question and emotional data, analyzing them, generating an appropriate answer, and returning it to the user. As a result, the user can obtain useful information for making environmentally conscious choices and receive an emotionally sensitive response, thereby promoting a sustainable lifestyle.

[0886] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0887] Step 1:

[0888] Question input from the user

[0889] The user opens a web browser on their device and accesses the EcoChat web page. Using the interface provided, the user enters a question into the question input field. For example, they might enter, "Tell me about eco-friendly detergents." Once the user has completed their input and pressed the submit button, their input will proceed to the next step.

[0890] Input: User question text

[0891] Output: Question text ready to send

[0892] Step 2:

[0893] Submitting questions and sentiment data

[0894] When the user presses the send button, the device uses JavaScript to send the question along with the user's emotional data obtained using facial recognition and text analysis techniques. The device uses OpenCV to recognize faces from camera images and an NLP library to analyze the emotions in the text data. This information is sent to the server as an HTTP POST request.

[0895] Input: Question text, facial image data, text data

[0896] Output: HTTP POST request (question text and sentiment data)

[0897] Step 3:

[0898] Receiving and analyzing questions and sentiment data

[0899] The server receives the HTTP POST request and parses its contents. Using the Flask framework, the server extracts the user's question and sentiment data from the received JSON data. The question text is converted into a format that can be sent to a natural language processing engine, and the sentiment data is converted into the format required for analysis.

[0900] Input: HTTP POST request

[0901] Output: Question text, sentiment data

[0902] Step 4:

[0903] Question content and emotional state analysis

[0904] The server sends the extracted question to a natural language processing engine (e.g., GPT-3) to analyze the question. At the same time, an emotion engine (e.g., a TensorFlow model) analyzes the received emotion data and identifies the user's emotional state (e.g., happy, sad, depressed). The analysis results are used in the next step.

[0905] Input: Question text, emotion data

[0906] Output: Question analysis results, emotional state data

[0907] Step 5:

[0908] Generate answers

[0909] The server integrates the analysis results obtained from the natural language processing engine with the emotional state data obtained from the emotion engine. The server uses a generative AI model to generate an appropriate response that is appropriate for the user's emotional state. For example, if the user is feeling down, the server selects and generates a response with a positive and encouraging tone.

[0910] Input: Question analysis results, emotional state data

[0911] Output: Emotionally sensitive answer text

[0912] Step 6:

[0913] Replying and displaying answers

[0914] The server sends the generated answer to the user's device as an HTTP response. The device then parses the received JSON data using JavaScript and displays the answer and sentiment analysis results in a designated area of ​​the webpage. This allows the user to quickly receive specific advice and a response that takes sentiment into consideration.

[0915] Input: Emotionally sensitive answer text

[0916] Output: Answers and emotional states displayed in a user interface

[0917] Through these processing steps, users can quickly receive specific advice and appropriate responses that take their emotions into consideration, providing useful information for making environmentally conscious choices and a pleasant user experience.

[0918] (Application example 2)

[0919] 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."

[0920] Modern brick-and-mortar stores are required to provide quick and accurate answers to questions about the environment. However, it is difficult to properly understand the emotional state of customers and respond based on that, making improving customer satisfaction a challenge. It is also difficult for employees to provide appropriate information in real time while serving customers. To solve these issues, a system using emotion analysis is needed.

[0921] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0922] In this invention, the server includes means for accepting questions about the environment from users, means for transmitting the accepted questions to a natural language processing engine for analysis, means for generating answers based on the analysis results, means for returning the generated answers to the users, means for analyzing the emotional state of the users, and means for integrating the questions and emotional data to generate answers appropriate to the emotions of the users. This makes it possible to provide quick and accurate answers while taking the emotional state of the users into consideration, thereby improving customer satisfaction.

[0923] The "means for receiving a question about the environment from a user" refers to a means for a user to use an input device to send a question about the environment to the system.

[0924] "Means for sending the received question to a natural language processing engine for analysis" refers to means for the system to send the question received from the user to a natural language processing engine and analyze its content.

[0925] "Means for generating an answer based on the analysis results" refers to the means by which the system generates an appropriate answer based on the analysis results from the natural language processing engine.

[0926] The "means for returning the generated answer to the user" refers to the means by which the system notifies or displays the generated answer to the user.

[0927] "Means for analyzing the user's emotional state" refers to means for analyzing the user's emotions and psychological state using voice analysis, facial recognition technology, etc.

[0928] "Means for integrating questions and emotional data to generate answers that match the user's emotions" refers to means for integrating the content of a question from a user with emotional data and, based on that, generating an answer with a tone and content that is optimal for the user's emotional state.

[0929] The "means for displaying the generated answer on a display device" is a means for displaying the answer generated by the system on a display device used in the store.

[0930] MODE FOR CARRYING OUT THE INVENTION

[0931] This invention is a system for accepting customer questions in a physical store and providing appropriate answers. This system also analyzes the user's emotional state and provides answers that take emotions into consideration, thereby improving customer satisfaction. Specific embodiments are described below.

[0932] System Hardware and Software

[0933] The main hardware of the system is as follows:

[0934] Smart glasses (e.g., Google Glass): A device that receives customer questions via voice and displays the analysis results.

[0935] Server: The central system that analyzes questions and sentiment data and generates appropriate answers.

[0936] The main software of the system is as follows:

[0937] Speech Recognition Module: Software that converts customer speech into text.

[0938] Natural language processing engine: Software that analyzes incoming questions.

[0939] Sentiment analysis engine: Software that analyzes user emotions based on voice and facial recognition.

[0940] Smart Glasses SDK: Software development kit for controlling smart glasses and displaying results.

[0941] Processing the data

[0942] The server processes the data as follows:

[0943] 1. Acquire voice input: Acquire customer's voice input through smart glasses. The voice recognition module converts the voice into text.

[0944] 2. Emotion analysis: Using voice data, the emotion analysis engine analyzes the user's emotions, thereby recognizing their emotional state, such as whether they are interested or anxious.

[0945] 3. Question analysis: The natural language processing engine analyzes and understands the question.

[0946] 4. Answer Generation: Based on the analysis results and emotional data, the server generates an appropriate answer. Based on the emotional data, it selects the most appropriate tone, such as a positive and encouraging tone or a calm and specific tone.

[0947] 5. Displaying the answer: The generated answer is displayed on the smart glasses so that the appropriate response can be provided to the customer.

[0948] Specific examples

[0949] For example, consider the following scenario in a brick-and-mortar store:

[0950] 1. Customer: Ask, "What eco-friendly products do you recommend these days?"

[0951] 2. Smart glasses (voice input and sentiment analysis): The voice recognition module converts the customer's question into text, and the sentiment analysis engine analyzes it as "interested."

[0952] 3. Server: Analyzes the question using a natural language processing engine and generates an answer with a positive tone based on the emotional data of "interested."

[0953] 4. Smart glasses (displaying answers): The store clerk displays answers such as, "How about this eco bag? It's very popular."

[0954] Prompt Sentence Examples

[0955] An example of a prompt is as follows:

[0956] "A customer is interested in eco-friendly products and has a question: 'What eco-friendly products do you recommend these days?' They seem interested. Use the right tone to provide specific recommendations."

[0957] In this way, the system can improve the efficiency of customer service in physical stores and increase customer satisfaction.

[0958] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0959] Step 1:

[0960] The user wears the smart glasses and receives customer questions by voice. The input is the customer's voice question, and the output is voice data. Specifically, the smart glasses' microphone picks up the customer's voice, and the built-in voice recognition module acquires the voice data.

[0961] Step 2:

[0962] The speech recognition module converts speech data into text. The input is speech data and the output is text data. Specifically, the speech recognition algorithm analyzes the speech waveform and converts it into a string of characters.

[0963] Step 3:

[0964] The smart glasses send text data to an emotion analysis engine to analyze the user's emotional state. The input is text data and voice data, and the output is emotion data. Specifically, the emotion analysis engine performs calculations to infer emotions from the tone of voice and text.

[0965] Step 4:

[0966] The smart glasses send text data and emotion data to the server. The input is text data and emotion data, and the output is an HTTP POST request to the server. Specifically, the communication module of the smart glasses converts the data into packets and sends them to the server.

[0967] Step 5:

[0968] The server analyzes the received text data and emotion data and generates an appropriate response using a generative AI model for automatic responses. The input is text data and emotion data, and the output is the response text. Specifically, the generative AI model integrates the text and emotion to construct the optimal response sentence.

[0969] Step 6:

[0970] The server sends the generated answer text to the smart glasses. The input is the answer text, and the output is an HTTP response to the smart glasses. Specifically, the server's communication module converts the data into packets and sends them to the smart glasses.

[0971] Step 7:

[0972] The smart glasses display the received answer text to the store clerk. The input is the answer text received from the server, and the output is the answer displayed on the display of the smart glasses. In specific operation, the display device of the smart glasses visually displays the text for the store clerk to confirm.

[0973] 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.

[0974] 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.

[0975] 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.

[0976] [Fourth embodiment]

[0977] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0978] 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.

[0979] 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).

[0980] 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.

[0981] 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.

[0982] 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).

[0983] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0984] 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.

[0985] 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.

[0986] 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.

[0987] 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.

[0988] 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.

[0989] 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."

[0990] The present invention is a system that allows users to ask questions about the environment and provides appropriate advice and information in response to those questions. This system mainly consists of a server, a terminal, and a user.

[0991] System Overview

[0992] 1. User inputs a question:

[0993] Users open a web browser on their device (PC, smartphone, etc.) and access the EcoChat web page. They then enter their environmental questions using the interface provided.

[0994] 2. Sending and receiving questions:

[0995] When a user enters a question and presses the submit button, the device creates an HTTP POST request containing the question and sends it to the server. The server receives this request and parses the user's question, which is sent in JSON format.

[0996] 3. Question analysis and answer generation:

[0997] The server sends the received question to a natural language processing engine (e.g., a general natural language processing service) for analysis. Based on the analysis results, the server generates an appropriate answer to the question.

[0998] 4. Reply and display answers:

[0999] The server sends the generated answer back to the user's device as an HTTP response, which then receives the response and displays it in a user-friendly format.

[1000] Specific Examples

[1001] For example, suppose a user inputs a question such as, "Tell me about eco-friendly detergents." The roles of the user, server, and terminal are explained in detail below.

[1002] 1. User Action:

[1003] The user accesses the EcoChat web page, enters "Tell me about eco-friendly detergents" in the question input field, and presses the send button to send the question.

[1004] 2. Server processing:

[1005] The server receives the question and sends it to a natural language processing engine, which generates a response such as, "For eco-friendly detergents, we recommend those that contain biodegradable ingredients. For example, brand XX detergent is good." The server formats this response and sends it back to the user's device.

[1006] 3. Display terminal:

[1007] The terminal displays the response received from the server in the display area, allowing the user to quickly receive appropriate advice for the question they entered.

[1008] In this way, the system of the present invention makes it possible to instantly provide appropriate information in response to users' environmental concerns and questions. The system as a whole efficiently carries out a series of processes: accepting a user's question, analyzing it, generating an appropriate answer, and returning it to the user. As a result, users can obtain useful information to make environmentally conscious choices, and play a role in promoting sustainable lifestyles.

[1009] The processing flow will be explained below.

[1010] Step 1:

[1011] Users access the EcoChat web page through their device's web browser and enter environmental questions.

[1012] Step 2:

[1013] Once the user has finished entering their question, they press the submit button, which causes the device to make an HTTP POST request.

[1014] Step 3:

[1015] The device sends an HTTP POST request to the server containing the user's question, structured in JSON format.

[1016] Step 4:

[1017] The server receives the HTTP POST request and extracts the JSON formatted data from the request body.

[1018] Step 5:

[1019] The server retrieves the user's question from the extracted data and prepares it to be sent to a natural language processing engine.

[1020] Step 6:

[1021] The server sends the user's question to a natural language processing engine for analysis, which parses the question based on pre-defined prompts.

[1022] Step 7:

[1023] A natural language processing engine analyzes the user's question and generates an appropriate response based on the context, such as, "Eco-friendly detergents are recommended if they contain biodegradable ingredients."

[1024] Step 8:

[1025] The server formats the response received from the natural language processing engine and creates an HTTP response to send back to the user.

[1026] Step 9:

[1027] The server sends the created HTTP response to the terminal, which contains the generated response.

[1028] Step 10:

[1029] The terminal analyzes the HTTP response received from the server and displays the response contained therein on the user interface.

[1030] Step 11:

[1031] The user checks the response from the server through the terminal interface and receives specific advice and information regarding the question.

[1032] Example 1

[1033] 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."

[1034] In modern society, interest in environmental issues is growing, and many users want to make environmentally conscious choices in their daily lives. However, with so much information available, it is difficult for users to get quick and accurate answers to specific environmental questions. Furthermore, when specialized knowledge is required, much of the information is difficult for average users to understand, which can lead to incorrect choices. There is a need for a system that can solve these problems and allow users to easily obtain useful environmental information.

[1035] 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.

[1036] In this invention, the server includes means for accepting questions about the environment from users, means for transmitting the accepted questions as HTTP POST requests, means for analyzing the accepted questions and transmitting them to a natural language processing engine for analysis, means for returning generated answers to the user's terminal as HTTP responses, and means for displaying the answers received by the user's terminal. This enables users to easily input specific questions about the environment and receive prompt and accurate answers to those questions.

[1037] "User" refers to any individual or entity that uses the System to ask an environmental question.

[1038] "Environmental Questions" refers to specific inquiries related to the environment, such as eco-friendly products, recycling methods, and environmental protection activities.

[1039] An "HTTP POST request" is a type of protocol used to send a user's question to a server, and is used to send data securely.

[1040] "Server" refers to a computer system that receives a user's question, analyzes it, uses a natural language processing engine to generate an answer, and then returns it to the user.

[1041] A "natural language processing engine" refers to a software program or computer system that uses artificial intelligence techniques to analyze a user's question and generate an appropriate answer.

[1042] An "HTTP response" is a type of protocol used by a server to send a response to a user's device, and includes response data to the user's request.

[1043] A "terminal" is a device through which a user enters questions and receives answers, specifically a PC or smartphone.

[1044] "Interface" refers to the means of providing user-friendly user interaction, such as screens and input fields that users use to interact with a system.

[1045] A "prompt" refers to appropriate keywords or phrases generated based on the content of the question, and acts as an aid to the natural language processing engine in analyzing the question.

[1046] The present invention is a system that allows users to ask questions about the environment and provides appropriate advice and information in response to those questions. The entire system mainly consists of a server, a terminal, and a user.

[1047] User question input

[1048] Users access the EcoChat web page from a web browser on their device (PC, smartphone, etc.). This web page provides an interface for entering questions about the environment. Users use this interface to enter their questions and click the send button.

[1049] Submit a Question

[1050] The device sends the user-entered question to the server as an HTTP POST request, with the question packaged in JSON format in the request body. The device waits for a response from the server.

[1051] Receiving and parsing questions

[1052] The server receives the HTTP POST request from the device, parses the JSON format data from the request body, and sends the parsed question to a natural language processing engine (e.g., OpenAI GPT-3).

[1053] Generate answers

[1054] The server sends a question to the natural language processing engine and waits for the analysis result from the engine. The natural language processing engine generates an appropriate answer based on the user's question and sends it back to the server. The server receives the answer and formats it in a format that is easy for the user to read.

[1055] Returning the answer

[1056] The server sends the formatted answer to the terminal as an HTTP response. The response data format is JSON, and is parsed by the terminal.

[1057] Show Answers

[1058] The device parses the received JSON data and renders the answer to the question in the display area of ​​the web page, allowing users to get a quick and accurate answer to their entered question.

[1059] Specific examples

[1060] For example, if a user asks the question "What are recyclable plastics?", the following flow would occur:

[1061] 1. User Action:

[1062] Users visit the EcoChat web page, type in a question such as "Tell me about recyclable plastics," and click the submit button.

[1063] 2. Server processing:

[1064] The server receives this question and sends it to a natural language processing engine, which generates an answer: "Recyclable plastics include PET (polyethylene terephthalate) and HDPE (high-density polyethylene)." The server formats this and sends it back to the device.

[1065] 3. Display terminal:

[1066] The terminal displays the received answers in a display area, allowing the user to review the appropriate information for the question.

[1067] Prompt Sentence Examples

[1068] An example of a prompt that a user might enter is, "Tell me about eco-friendly detergents." By using such a prompt, the system can provide an appropriate answer immediately.

[1069] In this way, an efficient system is realized to provide users with fast and relevant information in response to their environmental questions and inquiries, which can serve as a catalyst for sustainable lifestyles.

[1070] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1071] Step 1: User enters question

[1072] The user accesses the EcoChat web page using a PC or smartphone, enters "Please tell me about eco-friendly detergents" in the question input field, and presses the send button. This input data is temporarily stored on the device and used in the next step.

[1073] Step 2: Submit your question

[1074] The device creates an HTTP POST request containing the question entered by the user. The request body contains the question in JSON format. For example, it contains data like {"question": "Tell me about eco-friendly detergents."}. This request is then sent to the server.

[1075] Step 3: Receiving and parsing the question

[1076] The server receives the HTTP POST request sent from the device. It extracts JSON data from the received request and obtains the user's question. For example, data like {"question": "Please tell me about eco-friendly detergents."} is extracted. This question is then sent to a natural language processing engine.

[1077] Step 4: Generate an answer

[1078] The server sends the question to a natural language processing engine (e.g., OpenAI GPT-3). The natural language processing engine analyzes the received question and generates an appropriate answer. For example, it may generate an answer such as, "For eco-friendly detergents, those containing biodegradable ingredients are recommended. For example, XX brand detergent is good." This generated answer is then sent back to the server.

[1079] Step 5: Format and submit your response

[1080] The server formats the answer returned by the natural language processing engine into a format that is easy for the user to understand. The formatted answer is packaged in JSON format and sent to the terminal as an HTTP response. For example, the following response data is generated: {"answer": "Eco-friendly detergents are recommended that contain biodegradable ingredients. For example, XX brand detergent is good."}

[1081] Step 6: View your answers

[1082] The device analyzes the HTTP response received from the server. It extracts the answer from the JSON data and renders it in the display area of ​​the web page. For example, it displays an answer such as, "Eco-friendly detergents are recommended to contain biodegradable ingredients. For example, XX brand detergent is good." The user can obtain appropriate information for the question they entered.

[1083] In this way, data is input, processed, and output between the user, terminal, and server at each step, resulting in a system that efficiently provides answers to questions.

[1084] (Application example 1)

[1085] 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."

[1086] Conventional environmental information systems have had the problem of making it difficult for users to obtain appropriate answers when they request environmental information about specific products or services. In particular, there are few systems that suggest eco-friendly products or brands, and the provision of information to support sustainable choices is insufficient. The purpose of this invention is to quickly and appropriately provide useful information to help users make environmentally conscious choices.

[1087] 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.

[1088] In this invention, the server includes means for accepting questions about the environment from users, means for transmitting the accepted questions to a natural language processing engine for analysis, means for generating answers based on the analysis results, means for suggesting environmentally friendly products based on the questions entered by the users, and means for returning the generated answers to the users. This allows users to not only receive appropriate answers to their questions about the environment, but also receive suggestions for eco-friendly products and brands.

[1089] The "means for accepting questions about the environment from users" is a function for receiving environment-related questions entered by users and incorporating them into the system.

[1090] The "means for transmitting the received question to a natural language processing engine for analysis" is a function for transmitting question data to the engine in order to analyze the question received from the user using natural language processing technology.

[1091] "Means for generating answers based on analysis results" refers to a function for creating appropriate answers to users' questions using the results analyzed by the natural language processing engine.

[1092] "Means for returning the generated answer to the user" refers to a function that refers to a communication means or display means for delivering the generated answer to the user.

[1093] "A means of suggesting environmentally friendly products based on questions entered by the user" is a function that searches for and suggests eco-friendly products and brands based on the content of the user's questions.

[1094] "Means for displaying on the user's interface" refers to a function for visually displaying answers and suggestions on the user's device so that the user can easily view the results.

[1095] Overall system configuration

[1096] This invention is a system that allows users to input questions about the environment and receive appropriate advice and product suggestions. The system mainly consists of a server, a terminal, and a user interface.

[1097] User Actions

[1098] Users access the dedicated application using a device such as a smartphone or PC. The application interface has a question input field where users can enter questions such as, "Please tell me about environmentally friendly clothing."

[1099] Server Processing

[1100] The server receives questions sent by users. The received questions are first sent to a natural language processing engine for analysis. Examples of natural language processing engines used here include Google Cloud Natural Language API and OpenAI's GPT-3. Based on the analysis results, the server generates an appropriate answer.

[1101] Another feature of the present invention is that it has a function to suggest eco-friendly products based on the analyzed question. For example, in response to the question "Tell me about environmentally friendly clothing," clothing brands and products that use biodegradable materials will be suggested.

[1102] Response to the user

[1103] The generated answers and product suggestions are sent back to the user's device as an HTTP response, which is then displayed in an easy-to-read interface. Specifically, the user's device displays a list of eco-friendly clothing brands and products, along with a detailed description of each product and why it is eco-friendly.

[1104] Examples and prompts

[1105] As a concrete example, consider the case where a user types in "Tell me about eco-friendly clothing." This question is parsed by a natural language processing engine as follows:

[1106] "Clothing brands that use biodegradable materials include XXX and YYY. They are environmentally conscious and use sustainable materials."

[1107] Example prompt sentence:

[1108] "Tell me about eco-friendly clothing."

[1109] This invention allows users to quickly obtain appropriate information and eco-friendly product suggestions in response to environmental questions, resulting in access to useful information to promote sustainable lifestyles.

[1110] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1111] Step 1:

[1112] A user starts the application on a terminal and enters a question about the environment in the question input field. For example, the user enters "Tell me about environmentally friendly clothing." This is the input data. The user presses the send button to send the question to the server.

[1113] Step 2:

[1114] The device sends the user-entered questions about the environment to the server as an HTTP POST request, where the question data is converted to JSON format. The input data is the user's question, and the output data is JSON-formatted question data.

[1115] Step 3:

[1116] The server sends the received question data to a natural language processing engine, which uses, for example, Google Cloud Natural Language API or OpenAI's GPT-3. The input data is the question data in JSON format, and the analysis results are generated as output data.

[1117] Step 4:

[1118] The server receives the analysis results from the natural language processing engine and generates answers based on them. It also makes eco-friendly product suggestions based on the user's question. The input data is the analysis results, and the output data is the answers and product suggestions.

[1119] Step 5:

[1120] The server sends the generated answer and eco-friendly product suggestions back to the user's terminal as an HTTP response. The input data are the generated answer and product suggestions, and the HTTP response is generated as output data.

[1121] Step 6:

[1122] The terminal analyzes the HTTP response received from the server and displays it in an easy-to-read interface for the user. The input data is the HTTP response, and the output data is the answer and product suggestions that are displayed to the user.

[1123] The system allows users to quickly and easily get relevant answers to their environmental questions and suggestions for eco-friendly products.

[1124] 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.

[1125] The present invention is a system that allows users to ask questions about the environment and provides appropriate advice and information in response to those questions. This system further improves the user experience by combining an emotion engine that analyzes the user's emotional state.

[1126] System Overview

[1127] 1. User inputs a question:

[1128] Users open a web browser on their device (PC, smartphone, etc.) and access the EcoChat web page, where they enter their environmental questions using the interface provided.

[1129] 2. Submitting Questions and Emotional Data:

[1130] When a user enters a question and presses the send button, the device sends an HTTP POST request to the server that includes the question along with the user's emotional data obtained using technologies such as facial recognition and text analysis.

[1131] 3. Receiving and analyzing question and emotion data:

[1132] The server parses the received HTTP POST request and extracts the user's question and emotion data from the JSON format data, which is based on facial expressions and sentiment analysis of the input text.

[1133] 4. Question content and emotional state analysis:

[1134] The received question is sent to a natural language processing engine for content analysis. In parallel, an emotion engine analyzes the user's emotional state (e.g., joy, sadness, anger, etc.). The server then adjusts the content and tone of the response based on the analysis results.

[1135] 5. Generate answers:

[1136] By comparing the analysis results from the natural language processing engine with the emotional data from the emotion engine, the server generates an answer that suits the user's emotional state. For example, if a user asks, "Tell me about eco-friendly detergents," and the emotion engine recognizes that the user is feeling depressed, the server will generate an answer with a positive, encouraging tone.

[1137] 6. Replying and displaying answers:

[1138] The server sends the generated answer as an HTTP response to the user's device, which then analyzes the response and displays it to the user in an appropriate interface. The analyzed emotion results can also be displayed in the user interface.

[1139] Specific Examples

[1140] For example, if a user types in a question like, "Tell me about eco-friendly detergents," the invention works as follows:

[1141] 1. User Action:

[1142] A user accesses the EcoChat web page and enters "Please tell me about eco-friendly detergents" in the question input field. By pressing the send button, emotion data is sent along with the question.

[1143] 2. Server processing:

[1144] The server receives the question and emotion data and sends the question to a natural language processing engine for analysis. At the same time, the emotion engine analyzes the user's emotion and determines that the user is depressed. The server integrates this information and generates a response with an encouraging tone.

[1145] 3. Display terminal:

[1146] The device displays the response received from the server in the display area, allowing the user to quickly receive an appropriate response that takes into account their current emotions, along with specific advice for their question.

[1147] In this way, the system of the present invention can instantly provide appropriate information and emotionally sensitive advice in response to a user's environmental concerns or questions. The system as a whole efficiently performs a series of processes: accepting a user's question and emotional data, analyzing them, and generating an appropriate answer to return to the user. As a result, the user can obtain useful information for making environmentally conscious choices and receive an emotionally sensitive response, thereby promoting a sustainable lifestyle.

[1148] The processing flow will be explained below.

[1149] Step 1:

[1150] Users access the EcoChat web page using their device's web browser and enter environmental questions.

[1151] Step 2:

[1152] After entering a question, the user presses the send button. This action causes the device to create an HTTP POST request that includes the question and emotional data (emotional state determined by facial expression analysis and text analysis).

[1153] Step 3:

[1154] The device sends an HTTP POST request containing the user's question and emotion data to the server.

[1155] Step 4:

[1156] The server receives the HTTP POST request and extracts JSON-formatted data from the request body, which contains the user's question and sentiment data.

[1157] Step 5:

[1158] The server obtains the user's question from the extracted data and first sends the question to a natural language processing engine for analysis.

[1159] Step 6:

[1160] In parallel, the server sends emotion data to the emotion engine to analyze the user's emotional state. This emotion data is obtained from the user's facial expressions and input text.

[1161] Step 7:

[1162] A natural language processing engine analyzes the user's question and generates an answer based on the content.

[1163] Step 8:

[1164] An emotion engine analyzes the user's emotional state and determines, for example, whether the user is depressed, happy, angry, etc.

[1165] Step 9:

[1166] The server compares the answers from the natural language processing engine with the emotional data from the emotion engine to generate a final answer with a tone and content that suits the user's emotional state.

[1167] Step 10:

[1168] The server formats the generated answer as an HTTP response and sends it to the user's device.

[1169] Step 11:

[1170] The device analyzes the received HTTP response and displays the answer and a message reflecting the user's emotional state on the interface.

[1171] Step 12:

[1172] Users can view responses through the device interface and get specific advice and information to answer their questions, while emotion-sensitive responses improve the user experience.

[1173] Example 2

[1174] 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."

[1175] In modern society, users are increasingly asking questions about the environment. However, conventional systems have difficulty providing quick and appropriate answers to these questions. Furthermore, they generate answers without taking the user's emotional state into account, resulting in a poor user experience. Therefore, there is a need for a system that can analyze the user's questions and emotional state and provide appropriate information and advice that takes their emotions into consideration.

[1176] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1177] In this invention, the server includes means for accepting questions about the environment from a user, means for analyzing the accepted questions and the emotional state of the user, means for generating an answer based on the analysis result and the emotional state, and means for returning the generated answer to the user, thereby enabling the user to quickly obtain specific advice in response to the question and a response that takes into consideration the user's emotions.

[1178] A "user" is an individual or entity that enters a question into the system and receives a response.

[1179] The "means for accepting questions" refers to an interface or mechanism for obtaining questions entered by users.

[1180] A "natural language processing engine" is an algorithm or system that analyzes input text data and understands and processes its content.

[1181] "Means of analysis" refers to the mechanisms and processes used to analyze acquired data and understand its content and characteristics.

[1182] "Emotional state" is data that indicates the user's emotions, including emotions such as joy, sadness, and anger.

[1183] An "emotion engine" is an algorithm or system for analyzing a user's emotional state.

[1184] The "answer generation means" is the mechanism or process for creating an appropriate answer based on the analyzed data.

[1185] A "means for responding to the user" is the interface or mechanism for providing the generated answer to the user.

[1186] A "prompt" is an instruction or guideline that the system generates in response to a question.

[1187] An "interface" is a screen or operating means that allows a user to interact with a system.

[1188] "Analysis results" are the conclusions and information obtained from data by natural language processing engines and emotion engines.

[1189] The present invention is a system that allows users to ask questions about the environment and provides appropriate advice and information in response to those questions. This system further improves the user experience by combining an emotion engine that analyzes the user's emotional state.

[1190] A user opens a web browser on a device (PC, smartphone, etc.) and accesses the EcoChat web page. The user uses the interface provided to enter an environmental question. For example, they might enter, "Tell me about eco-friendly detergents."

[1191] When a user enters a question and presses the send button, the device uses JavaScript to send an HTTP POST request to the server, which includes the question and the user's emotion data obtained using technologies such as facial recognition and text analysis. OpenCV is used for facial recognition, and an NLP library is used for text emotion analysis.

[1192] The server parses the received HTTP POST request and extracts the user's question and emotion data from the JSON format data. The server processes the request and analyzes the data using the Python Flask framework. To analyze the emotional state, it uses an emotion engine built using machine learning frameworks such as TensorFlow and PyTorch.

[1193] Next, the received question is sent to a natural language processing engine (e.g., GPT-3), which analyzes the question content. At the same time, an emotion engine analyzes the user's emotional state (e.g., joy, sadness, anger, etc.). For example, if a question and emotion data are sent such as "Tell me about eco-friendly detergents," the server sends the question to the GPT-3 engine for analysis, while the emotion engine analyzes the user's emotional state.

[1194] The server combines the analysis results from the natural language processing engine with the emotional data from the emotion engine to generate an answer appropriate to the user's emotional state. If the server recognizes that the user is feeling down based on the emotional data, it generates an answer with a positive and encouraging tone. For example, it generates an answer such as, "Eco-friendly detergents are a good choice for the planet because they use environmentally friendly ingredients. Cheer up, and every choice you make is a step towards protecting the planet!"

[1195] The server sends the generated answer to the user's device as an HTTP response. The device then parses the received JSON data using JavaScript and displays the answer and sentiment analysis results in a designated area of ​​the webpage. This allows the user to quickly receive specific advice and an appropriate response that takes sentiment into consideration.

[1196] As a concrete example, if a user inputs the question "Tell me about eco-friendly detergents," the system works as follows:

[1197] 1. User Action:

[1198] The user accesses the EcoChat web page and enters "Please tell me about eco-friendly detergents" in the question input field. Then, the user presses the send button to send the question along with the emotion data.

[1199] 2. Server processing:

[1200] The server receives the question and emotion data and sends the question to a natural language processing engine for analysis. At the same time, the emotion engine analyzes the user's emotion and determines that the user is depressed. The server integrates this information and generates a response with an encouraging tone.

[1201] 3. Display terminal:

[1202] The terminal displays the response received from the server in the display area, allowing the user to quickly receive an appropriate response that takes into account their current feelings, along with specific advice for the question.

[1203] An example prompt might be, "User: Tell me about eco-friendly detergent. Emotional state: Depressed Question: Eco-friendly detergent Answer: Eco-friendly detergents are a green choice because they use environmentally friendly ingredients. Cheer up, every choice is a step towards saving the planet!"

[1204] In this way, the system of the present invention can instantly provide appropriate information and emotionally sensitive advice in response to a user's environmental concerns or questions. The system as a whole efficiently performs a series of processes: accepting a user's question and emotional data, analyzing them, generating an appropriate answer, and returning it to the user. As a result, the user can obtain useful information for making environmentally conscious choices and receive an emotionally sensitive response, thereby promoting a sustainable lifestyle.

[1205] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1206] Step 1:

[1207] Question input from the user

[1208] The user opens a web browser on their device and accesses the EcoChat web page. Using the interface provided, the user enters a question into the question input field. For example, they might enter, "Tell me about eco-friendly detergents." Once the user has completed their input and pressed the submit button, their input will proceed to the next step.

[1209] Input: User question text

[1210] Output: Question text ready to send

[1211] Step 2:

[1212] Submitting questions and sentiment data

[1213] When the user presses the send button, the device uses JavaScript to send the question along with the user's emotional data obtained using facial recognition and text analysis techniques. The device uses OpenCV to recognize faces from camera images and an NLP library to analyze the emotions in the text data. This information is sent to the server as an HTTP POST request.

[1214] Input: Question text, facial image data, text data

[1215] Output: HTTP POST request (question text and sentiment data)

[1216] Step 3:

[1217] Receiving and analyzing questions and sentiment data

[1218] The server receives the HTTP POST request and parses its contents. Using the Flask framework, the server extracts the user's question and sentiment data from the received JSON data. The question text is converted into a format that can be sent to a natural language processing engine, and the sentiment data is converted into the format required for analysis.

[1219] Input: HTTP POST request

[1220] Output: Question text, sentiment data

[1221] Step 4:

[1222] Question content and emotional state analysis

[1223] The server sends the extracted question to a natural language processing engine (e.g., GPT-3) to analyze the question. At the same time, an emotion engine (e.g., a TensorFlow model) analyzes the received emotion data and identifies the user's emotional state (e.g., happy, sad, depressed). The analysis results are used in the next step.

[1224] Input: Question text, emotion data

[1225] Output: Question analysis results, emotional state data

[1226] Step 5:

[1227] Generate answers

[1228] The server integrates the analysis results obtained from the natural language processing engine with the emotional state data obtained from the emotion engine. The server uses a generative AI model to generate an appropriate response that is appropriate for the user's emotional state. For example, if the user is feeling down, the server selects and generates a response with a positive and encouraging tone.

[1229] Input: Question analysis results, emotional state data

[1230] Output: Emotionally sensitive answer text

[1231] Step 6:

[1232] Replying and displaying answers

[1233] The server sends the generated answer to the user's device as an HTTP response. The device then parses the received JSON data using JavaScript and displays the answer and sentiment analysis results in a designated area of ​​the webpage. This allows the user to quickly receive specific advice and a response that takes sentiment into consideration.

[1234] Input: Emotionally sensitive answer text

[1235] Output: Answers and emotional states displayed in a user interface

[1236] Through these processing steps, users can quickly receive specific advice and appropriate responses that take their emotions into consideration, providing useful information for making environmentally conscious choices and a pleasant user experience.

[1237] (Application example 2)

[1238] 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."

[1239] Modern brick-and-mortar stores are required to provide quick and accurate answers to questions about the environment. However, it is difficult to properly understand the emotional state of customers and respond based on that, making improving customer satisfaction a challenge. It is also difficult for employees to provide appropriate information in real time while serving customers. To solve these issues, a system using emotion analysis is needed.

[1240] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1241] In this invention, the server includes means for accepting questions about the environment from users, means for transmitting the accepted questions to a natural language processing engine for analysis, means for generating answers based on the analysis results, means for returning the generated answers to the users, means for analyzing the emotional state of the users, and means for integrating the questions and emotional data to generate answers appropriate to the emotions of the users. This makes it possible to provide quick and accurate answers while taking the emotional state of the users into consideration, thereby improving customer satisfaction.

[1242] The "means for receiving a question about the environment from a user" refers to a means for a user to use an input device to send a question about the environment to the system.

[1243] "Means for sending the received question to a natural language processing engine for analysis" refers to means for the system to send the question received from the user to a natural language processing engine and analyze its content.

[1244] "Means for generating an answer based on the analysis results" refers to the means by which the system generates an appropriate answer based on the analysis results from the natural language processing engine.

[1245] The "means for returning the generated answer to the user" refers to the means by which the system notifies or displays the generated answer to the user.

[1246] "Means for analyzing the user's emotional state" refers to means for analyzing the user's emotions and psychological state using voice analysis, facial recognition technology, etc.

[1247] "Means for integrating questions and emotional data to generate answers that match the user's emotions" refers to means for integrating the content of a question from a user with emotional data and, based on that, generating an answer with a tone and content that is optimal for the user's emotional state.

[1248] The "means for displaying the generated answer on a display device" is a means for displaying the answer generated by the system on a display device used in the store.

[1249] MODE FOR CARRYING OUT THE INVENTION

[1250] This invention is a system for accepting customer questions in a physical store and providing appropriate answers. This system also analyzes the user's emotional state and provides answers that take emotions into consideration, thereby improving customer satisfaction. Specific embodiments are described below.

[1251] System Hardware and Software

[1252] The main hardware of the system is as follows:

[1253] Smart glasses (e.g., Google Glass): A device that receives customer questions via voice and displays the analysis results.

[1254] Server: The central system that analyzes questions and sentiment data and generates appropriate answers.

[1255] The main software of the system is as follows:

[1256] Speech Recognition Module: Software that converts customer speech into text.

[1257] Natural language processing engine: Software that analyzes incoming questions.

[1258] Sentiment analysis engine: Software that analyzes user emotions based on voice and facial recognition.

[1259] Smart Glasses SDK: Software development kit for controlling smart glasses and displaying results.

[1260] Processing the data

[1261] The server processes the data as follows:

[1262] 1. Acquire voice input: Acquire customer's voice input through smart glasses. The voice recognition module converts the voice into text.

[1263] 2. Emotion analysis: Using voice data, the emotion analysis engine analyzes the user's emotions, thereby recognizing their emotional state, such as whether they are interested or anxious.

[1264] 3. Question analysis: The natural language processing engine analyzes and understands the question.

[1265] 4. Answer Generation: Based on the analysis results and emotional data, the server generates an appropriate answer. Based on the emotional data, it selects the most appropriate tone, such as a positive and encouraging tone or a calm and specific tone.

[1266] 5. Displaying the answer: The generated answer is displayed on the smart glasses so that the appropriate response can be provided to the customer.

[1267] Specific examples

[1268] For example, consider the following scenario in a brick-and-mortar store:

[1269] 1. Customer: Ask, "What eco-friendly products do you recommend these days?"

[1270] 2. Smart glasses (voice input and sentiment analysis): The voice recognition module converts the customer's question into text, and the sentiment analysis engine analyzes it as "interested."

[1271] 3. Server: Analyzes the question using a natural language processing engine and generates an answer with a positive tone based on the emotional data of "interested."

[1272] 4. Smart glasses (displaying answers): The store clerk displays answers such as, "How about this eco bag? It's very popular."

[1273] Prompt Sentence Examples

[1274] An example of a prompt is as follows:

[1275] "A customer is interested in eco-friendly products and has a question: 'What eco-friendly products do you recommend these days?' They seem interested. Use the right tone to provide specific recommendations."

[1276] In this way, the system can improve the efficiency of customer service in physical stores and increase customer satisfaction.

[1277] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1278] Step 1:

[1279] The user wears the smart glasses and receives customer questions by voice. The input is the customer's voice question, and the output is voice data. Specifically, the smart glasses' microphone picks up the customer's voice, and the built-in voice recognition module acquires the voice data.

[1280] Step 2:

[1281] The speech recognition module converts speech data into text. The input is speech data and the output is text data. Specifically, the speech recognition algorithm analyzes the speech waveform and converts it into a string of characters.

[1282] Step 3:

[1283] The smart glasses send text data to an emotion analysis engine to analyze the user's emotional state. The input is text data and voice data, and the output is emotion data. Specifically, the emotion analysis engine performs calculations to infer emotions from the tone of voice and text.

[1284] Step 4:

[1285] The smart glasses send text data and emotion data to the server. The input is text data and emotion data, and the output is an HTTP POST request to the server. Specifically, the communication module of the smart glasses converts the data into packets and sends them to the server.

[1286] Step 5:

[1287] The server analyzes the received text data and emotion data and generates an appropriate response using a generative AI model for automatic responses. The input is text data and emotion data, and the output is the response text. Specifically, the generative AI model integrates the text and emotion to construct the optimal response sentence.

[1288] Step 6:

[1289] The server sends the generated answer text to the smart glasses. The input is the answer text, and the output is an HTTP response to the smart glasses. Specifically, the server's communication module converts the data into packets and sends them to the smart glasses.

[1290] Step 7:

[1291] The smart glasses display the received answer text to the store clerk. The input is the answer text received from the server, and the output is the answer displayed on the display of the smart glasses. In specific operation, the display device of the smart glasses visually displays the text for the store clerk to confirm.

[1292] 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.

[1293] 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.

[1294] 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.

[1295] 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.

[1296] 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.

[1297] 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.

[1298] 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).

[1299] 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.

[1300] 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."

[1301] 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.

[1302] 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).

[1303] 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.

[1304] 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.

[1305] 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.

[1306] 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.

[1307] 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.

[1308] 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.

[1309] 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.

[1310] 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.

[1311] 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.

[1312] 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.

[1313] The following is further disclosed regarding the above embodiment.

[1314] (Claim 1)

[1315] A means of receiving environmental questions from users;

[1316] A means for sending the received questions to a natural language processing engine for analysis;

[1317] means for generating an answer based on the analysis results;

[1318] a means for returning the generated answer to the user;

[1319] A system including:

[1320] (Claim 2)

[1321] 2. The system according to claim 1, further comprising means for generating an appropriate prompt according to the content of the question when transmitting the received question to the natural language processing engine.

[1322] (Claim 3)

[1323] 10. The system of claim 1, further comprising means for displaying the generated answers in a user interface.

[1324] "Example 1"

[1325] (Claim 1)

[1326] A means of receiving environmental questions from users;

[1327] A way to send received questions as HTTP POST requests,

[1328] A means for analyzing the question received by the server and transmitting it to a natural language processing engine for analysis;

[1329] A means for returning the generated answer to the user's device as an HTTP response;

[1330] a means for displaying the received response on the user terminal;

[1331] A system including:

[1332] (Claim 2)

[1333] 10. The system of claim 1, further comprising means for generating an appropriate prompt depending on the content of the question.

[1334] (Claim 3)

[1335] 10. The system of claim 1, further comprising means for displaying the generated answers in a user interface.

[1336] "Application Example 1"

[1337] (Claim 1)

[1338] A means of receiving environmental questions from users;

[1339] A means for sending the received questions to a natural language processing engine for analysis;

[1340] means for generating an answer based on the analysis results;

[1341] a means for returning the generated answer to the user;

[1342] A means of suggesting environmentally friendly products based on questions entered by users;

[1343] A system including:

[1344] (Claim 2)

[1345] 2. The system according to claim 1, further comprising: means for generating an appropriate prompt sentence according to the content of the question when transmitting the accepted question to the natural language processing engine.

[1346] (Claim 3)

[1347] 10. The system of claim 1, further comprising means for displaying the generated answers and suggested products in a user interface.

[1348] "Example 2: Combining Emotion Engines"

[1349] (Claim 1)

[1350] A means of receiving environmental questions from users;

[1351] means for analyzing received questions and the emotional state of the user;

[1352] means for generating an answer based on the analysis results and the emotional state;

[1353] a means for returning the generated answer to the user;

[1354] A system including:

[1355] (Claim 2)

[1356] 2. The system according to claim 1, further comprising means for generating an appropriate prompt according to the content of the question when transmitting the received question to the natural language processing engine.

[1357] (Claim 3)

[1358] 10. The system of claim 1, further comprising means for displaying the generated answer in a user interface and displaying the user's emotional state.

[1359] "Application example 2 when combining emotion engines"

[1360] (Claim 1)

[1361] A means of receiving environmental questions from users;

[1362] A means for sending the received questions to a natural language processing engine for analysis;

[1363] means for generating an answer based on the analysis results;

[1364] a means for returning the generated answer to the user;

[1365] a means for analyzing the emotional state of a user;

[1366] A means for integrating questions and emotion data to generate answers that match the user's emotions;

[1367] A system including:

[1368] (Claim 2)

[1369] 10. The system of claim 1, further comprising means for analyzing a user's emotion and generating a response tone based thereon.

[1370] (Claim 3)

[1371] 10. The system of claim 1, further comprising means for displaying the generated answer on a display device. [Explanation of symbols]

[1372] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means of receiving environmental questions from users; A means for sending the received questions to a natural language processing engine for analysis; means for generating an answer based on the analysis results; a means for returning the generated answer to the user; A system including:

2. The system according to claim 1 , further comprising means for generating an appropriate prompt depending on the content of the question when transmitting the accepted question to the natural language processing engine.

3. 10. The system of claim 1, further comprising means for displaying the generated answers in a user interface.

Images