system

Abstract

Provide a system. A method for loading past survey data into a database; means for providing a user interface for the terminal for accepting questions from a user; means for transmitting questions entered through the user interface to a server; A means for the server to receive the question and send a request to an artificial intelligence having a chat generation function; A means for the server to receive a response obtained from the artificial intelligence and return the response to the terminal; The system includes a terminal including means for displaying the answer.

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 conventional investigation work, there were few ways to effectively utilize past investigation data while addressing unclear points in real time, which required users to spend time and effort researching past data, resulting in reduced productivity. In particular, when dealing with large amounts of data or complex data, it was difficult to quickly access the appropriate information, resulting in issues such as reduced accuracy and efficiency of investigations. [Means for solving the problem]

[0005] In order to solve the above problems, the present invention provides the following means. First, past survey data is loaded into a server as a database. Next, a user interface is provided on a terminal that allows a user to input questions. The question input by the user through the terminal is sent to the server. The server receives the question and sends a request to an artificial intelligence (AI) equipped with a chat generation function. The server receives the answer obtained from the AI ​​and returns the answer to the terminal. Finally, the terminal displays the returned answer to the user. This allows the user to obtain answers to their questions in real time while referring to past survey data, thereby improving productivity and making surveys more efficient.

[0006] "Survey data" is a collection of data containing information and results collected from previously conducted surveys or studies.

[0007] A "database" is a data storage system that is structured to allow data to be efficiently stored, managed, and searched.

[0008] A "user interface" is an interface through which a user interacts with a computer system, including GUI elements such as input fields and buttons.

[0009] A "terminal" is hardware such as a computer or smart device that can be directly operated by a user.

[0010] A "server" is a computer system that provides services and resources to other computers and devices on a network.

[0011] "Artificial intelligence with chat generation capabilities" is an artificial intelligence system that has the ability to generate natural language and provide appropriate answers to users' questions.

[0012] A "question" is a request for information that a user enters to resolve an issue they do not understand regarding the survey or data.

[0013] An "answer" is appropriate information or explanation generated by artificial intelligence in response to a user's question. [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 system provided by the present invention begins by loading past survey data into a database. This allows the data to be quickly referenced as needed, allowing users to efficiently use the survey data. Next, the terminal provides the user with a user interface (UI) for entering questions. This UI is configured as a web form including a question input field and a submit button.

[0036] When a user enters a question and presses the send button, the question is sent from the device to the server. The server receives the question and requests the question content from an artificial intelligence (AI) with chat generation functionality. Specifically, the server sends the question in text format to the AI ​​and receives a response from the AI. This AI has natural language processing capabilities and is equipped with a model for generating appropriate responses to user questions.

[0037] The server sends the answer received from the AI ​​back to the device, which then displays the answer to the user. This allows the user to get answers to their questions in real time. Data exchange between the server, device, and user is carried out via network communication, and encrypted communication is used as a security measure.

[0038] Specific examples

[0039] For example, if a user is working on a research project and has a question about a particular data point, such as "What does this data point mean?", they can enter "What does this data point mean?" into a web form on their device. They press submit, which sends the question to the server. The server then sends the question to an AI, which generates an answer to the question. The answer might be something like "This data point indicates a particular trend." The server receives this answer and sends it back to the device, which then displays it to the user, providing instant information.

[0040] This system allows users to refer to past survey data and obtain answers to questions in real time, greatly improving the efficiency of survey work. In addition, AI-based answers are fast and accurate, reducing the burden on users and improving the quality of surveys. This system can be applied to survey work in a variety of fields, providing great convenience.

[0041] The processing flow will be explained below.

[0042] Step 1:

[0043] When the server starts up, it establishes a database connection, which is a storage for past survey data.

[0044] Step 2:

[0045] The server opens the past survey data file and stores each line in the database. At this time, it performs error handling and checks the log to confirm that the data was loaded successfully.

[0046] Step 3:

[0047] The terminal provides a user interface (UI) on a web browser that allows the user to enter a question. The UI is configured as a web form that includes a question input field and a submit button.

[0048] Step 4:

[0049] The user enters a question into a web form on the device and presses the submit button, for example, entering a specific question such as "What does this data point mean?"

[0050] Step 5:

[0051] The terminal catches the form's submit event, converts the question into JSON format using JavaScript (registered trademark), and then sends the question data to the server via asynchronous communication (AJAX).

[0052] Step 6:

[0053] The server receives the question sent by the user, saves the question in a temporary variable, and then constructs data to request the question from an artificial intelligence (AI) with chat generation capabilities.

[0054] Step 7:

[0055] The server issues an API request to send the question to the AI, which includes the question text as well as parameters such as the maximum number of tokens.

[0056] Step 8:

[0057] The AI ​​generates text based on the received question and creates an appropriate answer, which is then sent back to the server in JSON format.

[0058] Step 9:

[0059] The server analyzes the response received from the AI, formats it as needed, and then converts it back into JSON format for sending back to the device.

[0060] Step 10:

[0061] The terminal receives the answer sent back from the server and displays it again on the UI in the web browser, where the answer text is immediately visible to the user.

[0062] Step 11:

[0063] Users can check the answers displayed on their devices and get solutions and explanations for any questions they have, which allows them to carry out their research more efficiently.

[0064] Example 1

[0065] 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."

[0066] There is a need for a system that can efficiently use past survey data and provide quick and accurate answers to user questions. Furthermore, there is a need for a system that can obtain answers to user questions in real time while ensuring communication security. With conventional methods, referencing data and answering questions is time-consuming, resulting in a decrease in work efficiency.

[0067] 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.

[0068] In this invention, the server includes means for loading past survey data into a database, means for providing a user interface for a terminal that accepts questions from users, means for transmitting questions entered through the user interface to the server, means for transmitting requests to the generative AI model, means for receiving answers obtained from the generative AI model and returning the answers to the terminal, means for the terminal to display the answers, and means for ensuring security by encrypting communication between the server and the terminal. This allows users to efficiently use past survey data while obtaining accurate answers in real time.

[0069] A "database" is a specific structure or system for efficiently storing, managing, and retrieving data.

[0070] A "server" is a computer system that processes requests from clients via a network and provides services and data.

[0071] "Terminal" means a computer system or device capable of input and output used by a User.

[0072] A "user interface" is the means or screen configuration through which a user interacts with a system or application.

[0073] "Artificial intelligence" is a technology that allows machines to mimic human intelligent activity and have the ability to understand, learn, and respond.

[0074] A "generative AI model" is a type of artificial intelligence that uses pre-trained neural networks to generate and understand natural language at a near-human level.

[0075] A "web page" is a format for displaying documents and content on the Internet that can be accessed through a web browser.

[0076] "Encrypted communication" is a communication method that uses encryption technology to protect information from third parties when sending and receiving data.

[0077] The system provided by this invention begins by loading past survey data into a database. The server is configured to efficiently manage this data and enable quick reference as needed. It is preferable to use a database management system such as MySQL (registered trademark) or PostgreSQL.

[0078] Next, the terminal provides the user with a user interface (UI) for entering questions. This UI is implemented as a web page built using HTML, CSS, and JavaScript. The UI includes a field for entering questions and a submit button, and is designed to be intuitive for users to use.

[0079] When a user inputs a question and presses the send button, the question is sent from the terminal to the server. This communication is performed asynchronously using the AJAX function, and the question is transmitted to the server via the HTTP / HTTPS protocol.

[0080] When the server receives a question, it sends a request to a generative AI model. For example, OpenAI's GPT-4 (registered trademark) can be used as the generative AI model. The server sends the question to the AI ​​model in text format, and the AI ​​model generates an answer appropriate to the question.

[0081] The server then sends the answer received from the generative AI model back to the device, where it is displayed to the user in the device's browser using JavaScript, allowing the user to receive feedback in real time.

[0082] In addition, data exchange between the server and the device is performed using encrypted communication (SSL / TLS), ensuring security. This encryption reduces the risk of data eavesdropping or tampering.

[0083] Specific examples

[0084] For example, say a user has a question about a particular data point. They enter "What does this data point mean?" into a web form on their device. They press submit, sending the question to a server. The server sends the question to a generative AI model, which generates an answer such as "This data point indicates a particular trend." The server receives this answer and sends it back to the device, which displays it to the user, who receives the information instantly.

[0085] Prompt Sentence Examples

[0086] "What does this data point mean?"

[0087] By using this system, users can efficiently refer to past survey data and obtain accurate answers to questions in real time, significantly improving the efficiency of survey work and reducing the burden on users.

[0088] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0089] Step 1:

[0090] The server loads past survey data into a database. Specifically, the server uses a database management system (e.g., MySQL or PostgreSQL) to import data from a data source (e.g., a CSV file or Excel file). The input is a survey data file, which is then saved in the database and used as output. When storing the data in the database, the data is reshaped and formatted.

[0091] Step 2:

[0092] The terminal provides a user interface (UI). This UI is built using HTML, CSS, and JavaScript and displays a web page containing a question input field and a submit button. The user can enter a question in the input field and proceed to the next step by clicking the submit button. The input is the web browser accessed by the user, and the output is the displayed question input interface.

[0093] Step 3:

[0094] The user enters a question and presses the submit button. The input is the question the user enters in the input field, and the output is the question sent from the device to the server. The device uses JavaScript's AJAX function to asynchronously send the user's question to the server as a POST request.

[0095] Step 4:

[0096] The server receives the user's question and sends a request to the generative AI model. The input is the question received by the server, and the output is a prompt to be sent to the generative AI model. The server uses a Python library, for example, to send an API request to the AI ​​model (e.g., GPT-4).

[0097] Step 5:

[0098] The generative AI model receives a request from the server and generates an answer to the question. Here, the input is the prompt sent from the server, and the output is the text of the answer generated by the AI ​​model. The generative AI model uses a pre-trained neural network and performs natural language processing to generate an appropriate answer.

[0099] Step 6:

[0100] The server receives the answer from the generative AI model and sends it back to the device. The input is the text of the answer received from the generative AI model, and the output is an HTTP response to be sent to the device. The server sends this data back to the device using secure communication (e.g., HTTPS).

[0101] Step 7:

[0102] The terminal displays the answer received from the server in a user interface. The input is the text of the answer received from the server, and the output is the answer displayed to the user. The terminal uses JavaScript to display the received answer in a designated area on the web page.

[0103] Step 8:

[0104] Communication between the server and the terminal is always encrypted. The server sets up an SSL / TLS certificate and implements secure HTTPS communication. This encrypts the communication content and reduces the risk of data eavesdropping or tampering. The input is the entire data to be communicated, and the output is the encrypted data sent and received over the network.

[0105] (Application example 1)

[0106] 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."

[0107] In manufacturing processes within factories, it is difficult to grasp information about work progress and equipment status in real time, which often leads to delayed response to problems. Conventional methods require time to analyze manufacturing data and obtain results, which hinders efficient production activities. In addition, specialized engineers must always be on-site, which increases operational costs. This makes it difficult to improve overall productivity.

[0108] 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.

[0109] In this invention, the server includes means for loading past data into a database, means for providing a terminal interface for accepting questions from users, means for transmitting questions entered through the interface to the server, means for the server to receive the questions and send requests to an intelligence with natural language processing capabilities, means for the server to receive answers from the intelligence and return the answers to the terminal, means for the terminal to display the answers, and industrial equipment with a function for accepting questions about the status of equipment and work progress in manufacturing operations and providing answers in real time. This makes it possible to immediately grasp information such as work progress and equipment failures within the factory, enabling efficient and prompt troubleshooting.

[0110] "Historical data" refers to previously collected information and records related to the manufacturing process.

[0111] A "database" refers to a structured collection of data for efficient management and retrieval of data.

[0112] "Interface" refers to the component of a system that provides the means for users to enter questions and obtain information.

[0113] "Accepting a question" refers to the system recognizing and processing an inquiry from a user.

[0114] A "server" refers to a computer that provides services to users on a network.

[0115] "Natural language processing" refers to technology for understanding and processing human language.

[0116] "Intelligence" refers to systems that use generative AI models and machine learning algorithms to generate answers to user questions.

[0117] "Sending a request" means sending a request to another system for a particular action or piece of information.

[0118] "Industrial equipment" refers to the entire machinery, equipment, and systems used in manufacturing.

[0119] "Equipment status" refers to the current operating status and operating conditions of each piece of equipment and machine in the factory.

[0120] "Work progress" refers to the current progress in the manufacturing process.

[0121] "Storage" refers to hardware or devices for storing digital data.

[0122] "Web page" means a document containing information or an interface that can be accessed over the Internet.

[0123] To implement the present invention, the following system configuration and program are required: This system loads past manufacturing data into a database, and when a user inputs a question through a terminal interface, it uses a generative AI model to provide an answer in real time.

[0124] System configuration

[0125] 1. Database:

[0126] This database stores past manufacturing data and is constructed to enable efficient search and management. SQLite3 is used as a specific example of the database.

[0127] 2. User Interface (Interface):

[0128] This is the terminal interface for users to enter and submit questions. It consists of a web page with a question input field and a submit button.

[0129] 3. Server:

[0130] This server receives questions from users and sends requests to an AI model with natural language processing capabilities (generative AI model). The OpenAI API can be used as the AI ​​model.

[0131] 4. Intelligence (generative AI models):

[0132] It uses technology for understanding and processing human language to generate answers to user questions. This intelligence uses OpenAI's GPT-3 (registered trademark) as its generative AI model.

[0133] System processing flow

[0134] 1. Data Acquisition:

[0135] The server first searches the database based on the input query and retrieves relevant past manufacturing data.

[0136] 2. Answer generation from AI models:

[0137] If there is no relevant data in the database, the server will send the user's question to the generative AI model to generate an appropriate answer. The prompt for the question will be sent in the following format:

[0138] "Generate appropriate answers based on user questions: [question]"

[0139] 3. Returning the response:

[0140] The server receives the answer from the generative AI model and sends it back to the device.

[0141] 4. Show Answers:

[0142] The terminal displays the received answers to the user, allowing the user to obtain answers to their questions in real time.

[0143] Specific examples of hardware and software used

[0144] Database server: SQLite3

[0145] AI model: OpenAI GPT-3

[0146] Network communication: Ensure security by using encrypted communication (e.g., TLS / SSL).

[0147] Specific examples

[0148] For example, a user working on a production line might type into the interface, "What does error code 45 on this machine mean?" This question is sent to the server, which first searches the database. If no matching data is found, the server sends the question to a generative AI model, as follows:

[0149] "Generate an appropriate answer based on the user's question: What does error code 45 mean on this device?"

[0150] Upon receiving an answer from the generative AI model (e.g., "Error code 45 indicates a faulty temperature sensor"), the server sends it back to the device, which immediately displays the answer to the user.

[0151] This system makes it possible to grasp information on work progress and equipment status within the factory in real time, enabling efficient and rapid response to problems.

[0152] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0153] Step 1:

[0154] The user enters a question through the terminal interface and presses the send button. The input is a textual question, for example, "What does error code 45 on this device mean?" The output is the question data that is sent to the server.

[0155] Step 2:

[0156] The server receives questions sent by users. The input is the textual question data from the user, and the output is to store the received question data and pass it on to the next process.

[0157] Step 3:

[0158] The server searches the database based on the received question data. The server uses SQLite3 to search for data points related to the question. The input is the received question data, and the output is the data points as search results. If the corresponding data exists in the database, the data points are retrieved.

[0159] Step 4:

[0160] Validates the results of a database search. The input is the database search results, and the output is a determination of whether or not data exists. If relevant data is found, that information is used to generate an answer for the user.

[0161] Step 5:

[0162] If there is no relevant data in the database, the server sends the question to the generative AI model. The question is sent using the GPT-3 API, and the AI ​​model generates an answer. The input is a prompt containing the user's question: "Generate an appropriate answer based on the user's question: [question]", and the output is a text answer from the AI ​​model.

[0163] Step 6:

[0164] The server receives the answer obtained from the generative AI model. The input is the text-format answer data from the AI ​​model, and the output is to store the received answer data and pass it on to the next process.

[0165] Step 7:

[0166] The server returns the received response to the terminal. The input is the saved response data, and the output is the response data sent to the terminal.

[0167] Step 8:

[0168] The terminal displays the answer received from the server to the user. The input is the text-format answer data sent from the server, and the output is the answer displayed on the user interface. The user can check this and obtain information in real time.

[0169] Specific actions

[0170] For example, a user enters a question such as "What does error code 45 on this device indicate?" and presses the send button. The question is sent to the server, which searches the database. If the data does not exist, the server sends the question as a prompt to the generative AI model, and receives the answer from the AI: "Error code 45 indicates a faulty temperature sensor." This answer is sent back to the device, which displays it to the user.

[0171] 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.

[0172] The system of the present invention has the following features to efficiently utilize past survey data and provide real-time answers to user questions: It also incorporates an emotion engine to recognize user emotions, improving the user experience.

[0173] When the server starts up, it first establishes a database connection and loads past survey data into the database. The database is a storage device for saving and searching data, and stores past survey data.

[0174] Next, the terminal provides a user interface (UI) for the user to input a question. This UI is displayed on a web browser and is configured as a web form including a question input field and a submit button.

[0175] When a user inputs a question and presses the send button, the device sends the question to the server. The server receives the question and passes it to the emotion engine. The emotion engine analyzes the user's emotional state from the input and determines, for example, whether the user is confused or in a hurry.

[0176] The emotion engine suggests appropriate wording and tone for the question based on the user's emotional state and returns this information to the server. The server then uses this information to request a question from an artificial intelligence (AI) with chat generation capabilities. The AI ​​receives the question, generates an appropriate answer, and returns it to the server.

[0177] The server sends the AI's answers back to the device, which then displays them to the user, allowing the user to get answers to their questions in real time, with the answers delivered in a tone that matches the user's emotional state.

[0178] Specific examples

[0179] For example, if a user enters something in a hurry, such as "I don't understand the meaning of this data point!", the emotion engine will sense the user's impatience. Based on this information, the server will add additional information to the question it sends to the AI, such as "The user is in a hurry, so a concise, straightforward answer is required." The AI ​​will then take this additional information into account and generate a specific, quick answer, such as "That data point shows the growth rate." The server will then send this answer back to the device, which will display it to the user. As a result, the user can obtain appropriate information in real time and receive an answer that is in line with their emotional state.

[0180] In this way, the present invention provides a system that recognizes user emotions and utilizes past survey data to quickly and accurately answer user questions, improving the user experience and streamlining the survey process.

[0181] The processing flow will be explained below.

[0182] Step 1:

[0183] The server establishes a database connection on startup, which is a storage for past survey data, allowing it to be quickly referenced when needed.

[0184] Step 2:

[0185] The server opens the past survey data file and stores it line by line in the database. For example, it reads a CSV file, parses each line, and inserts it into the database. It also handles errors and verifies that the data was loaded correctly.

[0186] Step 3:

[0187] The terminal provides a user interface (UI) that allows the user to enter a question. This UI is displayed in a web browser and is structured as a web form that includes a question input field and a submit button.

[0188] Step 4:

[0189] The user enters a question into a web form on the device and presses the submit button, for example, "What does this data point mean?"

[0190] Step 5:

[0191] The device catches the form submission event, converts the question into JSON format using JavaScript, and then sends the question data to the server via asynchronous communication (AJAX).

[0192] Step 6:

[0193] The server receives the question sent by the user, saves the question in a temporary variable, and then passes the question to the emotion engine to analyze the user's emotional state.

[0194] Step 7:

[0195] The emotion engine analyzes the user's emotional state from the text they input. For example, it uses natural language processing to infer emotions such as "confusion" or "irritation."

[0196] Step 8:

[0197] The emotion engine sends the analysis results back to the server, including information suggesting appropriate expressions and tones based on the emotional state.

[0198] Step 9:

[0199] The server requests a question from an artificial intelligence (AI) system with chat generation capabilities based on the information obtained from the emotion engine. For example, the server includes information such as "The user is confused and needs a concise and clear answer."

[0200] Step 10:

[0201] The chat generation AI generates text based on the received question and additional information, generating an appropriate response, which is then sent back to the server in JSON format.

[0202] Step 11:

[0203] The server analyzes the response received from the AI, formats it as needed, and then converts it back into JSON format for sending back to the device.

[0204] Step 12:

[0205] The terminal receives the answer sent back from the server and displays it again in the UI on the web browser. The answer text is immediately visible to the user.

[0206] Step 13:

[0207] Users can view answers displayed on their devices to find solutions or explanations to their questions, and answers are delivered in a tone that reflects their emotional state, improving the user experience.

[0208] In this way, the present invention provides a system that efficiently utilizes past survey data to provide answers to users' questions in real time, and by combining it with an emotion engine, it provides appropriate answers that correspond to the user's emotional state.

[0209] Example 2

[0210] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0211] Conventional question-answering systems provide answers without considering the user's emotional state, resulting in a poor user experience. They also struggle to efficiently utilize past survey data and provide prompt and accurate responses. This often leaves users frustrated, and they sometimes fail to provide appropriate responses, especially for urgent questions.

[0212] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0213] In this invention, the server includes means for loading past survey data into a database, means for providing a user interface for a terminal that accepts questions from users, means for transmitting questions entered through the user interface to the server, means for the server to receive the questions and analyze the user's emotional state using an emotion analysis engine, means for generating a prompt sentence for a generative AI model based on the emotional state analyzed by the emotion analysis engine and transmitting the request, means for the server to receive an answer obtained from the generative AI model and return the answer to the terminal, and means for the terminal to display the answer, thereby enabling a quick and appropriate response that takes the user's emotional state into consideration.

[0214] A "server" is a computing device that receives requests from clients and processes them appropriately.

[0215] A "database" is a data storage system that stores past survey data and allows for efficient storage, retrieval, updating, and deletion of data.

[0216] A "user interface" is an interface through which a user interacts with a computer system, and is provided in the form of a web page that includes fields for entering questions and the like.

[0217] An "emotion analysis engine" is software or a system that analyzes text data entered by a user and detects the user's emotional state (e.g., frustration, confusion, joy).

[0218] A "generative AI model" is a model generated by artificial intelligence and used to generate appropriate answers to user questions.

[0219] A "prompt sentence" is a text sentence that provides appropriate background information to the generative AI model, and includes the user's emotional state and the question content.

[0220] A "question" refers to a specific inquiry that a user poses to a computer system, and is data that is processed through a server.

[0221] An "answer" is a response generated by a generative AI model in response to a user's question, and contains appropriate information.

[0222] MODE FOR CARRYING OUT THE INVENTION

[0223] The system according to the present invention has the following hardware and software configuration in order to efficiently utilize past survey data and provide answers to user questions in real time.

[0224] Hardware and software used

[0225] 1. Hardware

[0226] Server (a computer with a high-performance processor and sufficient memory)

[0227] User devices (PCs, tablets, smartphones, etc.)

[0228] 2. Software

[0229] Database Management System (DBMS): MySQL, PostgreSQL, etc.

[0230] Web browser: GOOGLE CHROME (registered trademark), Mozilla Firefox, etc.

[0231] Sentiment analysis engine: General natural language processing software (e.g., IBM Watson (registered trademark) Tone Analyzer, Google (registered trademark) Cloud Natural Language)

[0232] Generative AI model: OpenAI's GPT-3 or a similar model

[0233] Data processing flow

[0234] 1. Starting the server and connecting to the database

[0235] When the server starts, it establishes a connection with a database management system, which stores past survey data, and performs read operations on the database.

[0236] 2. Providing a user interface

[0237] The terminal provides a web browser interface for users to input questions. This interface is composed of HTML, CSS, and JavaScript, and includes a question input field and a submit button.

[0238] 3. User inputs and submits question

[0239] When a user enters a question and presses the submit button, the device parses the question into JSON format and sends it to the server using an HTTP POST request.

[0240] 4. Emotion analysis using an emotion analysis engine

[0241] The server passes the received question to a sentiment analysis engine, which analyzes the question and returns the user's emotional state in JSON format. For example, it can determine whether the user is impatient or confused.

[0242] 5. Prompt generation and request transmission to the generative AI model

[0243] Based on the emotional information obtained from the emotion analysis engine, the server generates a prompt sentence, which is used when sending a request to the generative AI model. An example of a prompt sentence is as follows:

[0244] User Question: "I don't understand what this data point means!"

[0245] Emotion engine analysis results: The user feels impatient

[0246] AI instructions:

[0247] "Users are in a hurry and need short, straightforward answers.

[0248] For example: That data point shows the growth rate.”

[0249] 6. Receiving and displaying generated answers

[0250] The generative AI model generates an appropriate answer based on the prompt sentence. The generated answer is sent back to the server, which then sends it to the user's device. The device receives the answer and displays it on the user interface.

[0251] Specific examples

[0252] For example, if a user types "I don't understand what this data point means!" and presses the submit button, the following process will occur:

[0253] The server passes the question to a sentiment analysis engine to analyze the user's sense of urgency.

[0254] Based on the results obtained from the sentiment analysis engine, the server generates prompt sentences for the generative AI model.

[0255] Based on the prompt sent to the generative AI model, an appropriate answer is generated, such as "That data point indicates a growth rate."

[0256] This answer is sent to the user's terminal via the server, and the user can check the answer on a web browser.

[0257] As described above, the present invention provides a system that recognizes the user's emotional state and utilizes past survey data to quickly and accurately answer the user's questions, thereby improving the user experience and streamlining the survey process.

[0258] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0259] Step 1: Starting the server and connecting to the database

[0260] When the server starts, it establishes a connection to the database management system (DBMS). Specifically, the server attempts to connect using the database connection string, and if successful, loads the past survey data from the database into memory.

[0261] Input: Database connection string

[0262] Output: Database connection established and data loaded successfully

[0263] What it does: The server loads the database connection settings on startup, and once the connection is established, it uses SQL queries to retrieve past survey data and stores it in memory.

[0264] Step 2: Providing a User Interface

[0265] The device provides a user interface (UI) on a web browser, specifically by using HTML, CSS, and JavaScript to display a page containing a question input field and a submit button.

[0266] Input: Access to the terminal

[0267] Output: UI displayed in a web browser

[0268] Specific operation: The device receives the UI file from the HTTP server, renders it, and displays it to the user. It then waits for the user to enter a question.

[0269] Step 3: User enters question and submits

[0270] The user enters a question through the device's UI and presses the submit button. The device parses the question into JSON format and sends it to the server using an HTTP POST request.

[0271] Input: User question

[0272] Output: The question parsed into JSON and sent to the server

[0273] Specific behavior: The user types "I don't understand the meaning of this data point!" and presses the send button. The device converts this to JSON format and sends it to the server as an HTTP POST request.

[0274] Step 4: Sentiment analysis using the sentiment analysis engine

[0275] The server passes the received question to a sentiment analysis engine to analyze the user's emotional state. The sentiment analysis engine analyzes the text data and returns the emotional state to the server in JSON format.

[0276] Input: JSON formatted question

[0277] Output: JSON format data indicating emotional state

[0278] Specific operation: The server sends the received question to the sentiment analysis engine and receives the analysis result (e.g., the user is impatient).

[0279] Step 5: Generate a prompt and send a request to the generative AI model

[0280] The server generates a prompt based on the results of the emotion analysis engine and sends a request to the generative AI model. The prompt includes the user's question and emotional state.

[0281] Input: JSON data representing emotional states

[0282] Output: Prompts and requests sent to the generative AI model

[0283] Specific operation: The server generates a prompt sentence containing the instruction "The user is impatient and needs a concise and straightforward answer" and sends it to the generative AI model.

[0284] Step 6: Receive and view the generated answers

[0285] The generative AI model generates an appropriate response based on the prompt and sends it back to the server, which then sends the response to the user's device, which displays it on the UI.

[0286] Input: Answer from a generative AI model

[0287] Output: Send and display responses to the terminal

[0288] Specific operation: The generative AI model generates an answer such as "That data point indicates a growth rate" and sends it back to the server. The server then sends this answer to the device, which displays it on the UI so that the user can confirm it.

[0289] (Application example 2)

[0290] 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."

[0291] While existing systems provide a way to answer users' questions in real time by utilizing past survey data, they lack a method for responding appropriately based on the user's emotional state. As a result, the user experience is often unsatisfactory, and it is difficult to provide appropriate support, especially when the user is confused or in a hurry. Therefore, there is a need for a system that can analyze the user's emotional state and adjust the tone of the questions accordingly.

[0292] 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.

[0293] In this invention, the server includes means for loading past survey data into a database, means for providing a user interface for a terminal that accepts questions from users, means for transmitting questions entered through the user interface to the server, means for the server to receive the questions and analyze the user's emotional state using an emotion analysis engine, means for the server to send a request to an artificial intelligence (AI) with a chat generation function based on the emotion analysis results, means for the server to receive answers obtained from the AI ​​and return the answers to the terminal, and means for the terminal to display the answers, thereby enabling real-time answers in an optimal tone according to the user's emotional state.

[0294] "Historical Survey Data" means information and statistics previously collected and stored.

[0295] A "database" is a system for organizing and storing information and data.

[0296] A "terminal" is a computer device that is operated by a user.

[0297] A "user interface" refers to the screen and input device that a user uses to operate a system.

[0298] A "server" is a computer system that provides services to other computers and terminals over a network.

[0299] An "emotion analysis engine" is software or algorithms for analyzing a user's emotional state from input data.

[0300] The "chat generation function" is a function that generates appropriate answers to input questions.

[0301] "Artificial intelligence" is a technology that uses computers to achieve human-like intelligence.

[0302] An "answer" is a response or answer to a question.

[0303] The system that realizes this application example is built using the following programs and hardware: The system leverages past survey data to quickly and accurately answer users' questions and can adjust its tone based on the user's emotional state.

[0304] The server first connects to a database (e.g., MySQL) to load past survey data. This database is a system for organizing and storing information and data. Then, a terminal built using React Native to provide the user interface accepts user questions.

[0305] When a user inputs a question and presses the send button, the terminal sends the question to the server. When the server receives the question, it analyzes the user's emotional state using an emotion analysis engine (e.g., the TENSORFLOW® model). This emotion analysis engine is software or an algorithm for analyzing the user's emotional state from the user's input data.

[0306] Next, the server sends a question to an AI (e.g., OpenAI GPT-4) based on the results of this emotion analysis. The AI, which has chat generation capabilities, has the ability to generate an appropriate answer to the question. The answer obtained from the AI ​​is sent back to the server, which then sends it back to the device.

[0307] The device then displays the received answers to the user through a user interface, allowing the user to receive answers to their questions in real time and in a tone appropriate to their emotional state.

[0308] For example, if a user types, "I'm not sure what to order!", the server uses a TensorFlow model to analyze the user's confusion. It then sends a prompt to OpenAI GPT-4: "The user is confused and needs quick and clear advice. If they're unsure what to order, what would you suggest?" The AI ​​responds by generating a response like, "I see you're having trouble. I recommend the chef's special pasta, which is a popular choice."

[0309] In this way, the system recognizes user emotions and leverages past survey data to provide quick and personalized answers to user questions, improving the user experience and enabling appropriate support for users in areas such as food delivery.

[0310] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0311] Step 1:

[0312] When the server starts, it establishes a database connection. At this time, the server loads past survey data from the database (MySQL). The input is the database connection information, and the output is the loaded past survey data. The information stored in the database is efficiently retrieved and prepared in a form that can be used by the end user.

[0313] Step 2:

[0314] The terminal provides a user interface for the user to input a question. React Native is used to display a question input field and a submit button on the screen. The input is the screen on the device that the user sees and operates, and the output is the question text entered by the user. The terminal screen displays an area for entering the question text and a submit button.

[0315] Step 3:

[0316] When a user enters a question and presses the send button, the terminal sends the question to the server. The input is the question text entered by the user, and the output is the question data to be sent to the server. The terminal sends the question to the server in the form of an HTTP request.

[0317] Step 4:

[0318] The server passes the received question data to a sentiment analysis engine (TensorFlow model). The sentiment analysis engine analyzes the question text and estimates the user's emotional state. The input is the question data, and the output is the user's emotional state (e.g., confused, urgent). The sentiment analysis engine extracts features from the text data and estimates the user's emotion.

[0319] Step 5:

[0320] Based on the emotional state returned by the emotion analysis engine, the server sends a prompt to an artificial intelligence (OpenAI GPT-4) with chat generation capabilities. The prompt contains information about the user's emotional state. The input is the user's emotional state and question data, and the output is a prompt to the AI. The server generates a specific prompt taking the emotional state into consideration and sends it to the AI.

[0321] Step 6:

[0322] The artificial intelligence (OpenAI GPT-4) generates an appropriate answer based on the received prompt. The input is the prompt, and the output is the generated answer text. The artificial intelligence processes the data according to the prompt and generates an appropriate answer.

[0323] Step 7:

[0324] The server receives the answer text obtained from the AI ​​and sends it back to the terminal. The input is the answer data from the AI, and the output is the answer data sent to the terminal. The server receives the AI's answer, converts the format as needed, and sends it to the terminal.

[0325] Step 8:

[0326] The terminal displays the received answer text on the user interface. The input is the answer data from the server, and the output is the display content on the user interface. The answer text is displayed on the terminal screen in a format that the user can easily understand.

[0327] This allows users to get answers to their questions in real time and receive support in a tone that matches their emotional state.

[0328] 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.

[0329] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0330] 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.

[0331] [Second embodiment]

[0332] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0333] 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.

[0334] 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).

[0335] 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.

[0336] 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.

[0337] 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).

[0338] 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.

[0339] 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.

[0340] 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.

[0341] 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.

[0342] 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.

[0343] 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."

[0344] The system provided by the present invention begins by loading past survey data into a database. This allows the data to be quickly referenced as needed, allowing users to efficiently use the survey data. Next, the terminal provides the user with a user interface (UI) for entering questions. This UI is configured as a web form including a question input field and a submit button.

[0345] When a user enters a question and presses the send button, the question is sent from the device to the server. The server receives the question and requests the question content from an artificial intelligence (AI) with chat generation functionality. Specifically, the server sends the question in text format to the AI ​​and receives a response from the AI. This AI has natural language processing capabilities and is equipped with a model for generating appropriate responses to user questions.

[0346] The server sends the answer received from the AI ​​back to the device, which then displays the answer to the user. This allows the user to get answers to their questions in real time. Data exchange between the server, device, and user is carried out via network communication, and encrypted communication is used as a security measure.

[0347] Specific examples

[0348] For example, if a user is working on a research project and has a question about a particular data point, such as "What does this data point mean?", they can enter "What does this data point mean?" into a web form on their device. They press submit, which sends the question to the server. The server then sends the question to an AI, which generates an answer to the question. The answer might be something like "This data point indicates a particular trend." The server receives this answer and sends it back to the device, which then displays it to the user, providing instant information.

[0349] This system allows users to refer to past survey data and obtain answers to questions in real time, greatly improving the efficiency of survey work. In addition, AI-based answers are fast and accurate, reducing the burden on users and improving the quality of surveys. This system can be applied to survey work in a variety of fields, providing great convenience.

[0350] The processing flow will be explained below.

[0351] Step 1:

[0352] When the server starts up, it establishes a database connection, which is a storage for past survey data.

[0353] Step 2:

[0354] The server opens the past survey data file and stores each line in the database. At this time, it performs error handling and checks the log to confirm that the data was loaded successfully.

[0355] Step 3:

[0356] The terminal provides a user interface (UI) on a web browser that allows the user to enter a question. The UI is configured as a web form that includes a question input field and a submit button.

[0357] Step 4:

[0358] The user enters a question into a web form on the device and presses the submit button, for example, entering a specific question such as "What does this data point mean?"

[0359] Step 5:

[0360] The device catches the form submission event, converts the question into JSON format using JavaScript, and then sends the question data to the server via asynchronous communication (AJAX).

[0361] Step 6:

[0362] The server receives the question sent by the user, saves the question in a temporary variable, and then constructs data to request the question from an artificial intelligence (AI) with chat generation capabilities.

[0363] Step 7:

[0364] The server issues an API request to send the question to the AI, which includes the question text as well as parameters such as the maximum number of tokens.

[0365] Step 8:

[0366] The AI ​​generates text based on the received question and creates an appropriate answer, which is then sent back to the server in JSON format.

[0367] Step 9:

[0368] The server analyzes the response received from the AI, formats it as needed, and then converts it back into JSON format for sending back to the device.

[0369] Step 10:

[0370] The terminal receives the answer sent back from the server and displays it again on the UI in the web browser, where the answer text is immediately visible to the user.

[0371] Step 11:

[0372] Users can check the answers displayed on their devices and get solutions and explanations for any questions they have, which allows them to carry out their research more efficiently.

[0373] Example 1

[0374] 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."

[0375] There is a need for a system that can efficiently use past survey data and provide quick and accurate answers to user questions. Furthermore, there is a need for a system that can obtain answers to user questions in real time while ensuring communication security. With conventional methods, referencing data and answering questions is time-consuming, resulting in a decrease in work efficiency.

[0376] 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.

[0377] In this invention, the server includes means for loading past survey data into a database, means for providing a user interface for a terminal that accepts questions from users, means for transmitting questions entered through the user interface to the server, means for transmitting requests to the generative AI model, means for receiving answers obtained from the generative AI model and returning the answers to the terminal, means for the terminal to display the answers, and means for ensuring security by encrypting communication between the server and the terminal. This allows users to efficiently use past survey data while obtaining accurate answers in real time.

[0378] A "database" is a specific structure or system for efficiently storing, managing, and retrieving data.

[0379] A "server" is a computer system that processes requests from clients via a network and provides services and data.

[0380] "Terminal" means a computer system or device capable of input and output used by a User.

[0381] A "user interface" is the means or screen configuration through which a user interacts with a system or application.

[0382] "Artificial intelligence" is a technology that allows machines to mimic human intelligent activity and have the ability to understand, learn, and respond.

[0383] A "generative AI model" is a type of artificial intelligence that uses pre-trained neural networks to generate and understand natural language at a near-human level.

[0384] A "web page" is a format for displaying documents and content on the Internet that can be accessed through a web browser.

[0385] "Encrypted communication" is a communication method that uses encryption technology to protect information from third parties when sending and receiving data.

[0386] The system provided by this invention starts by loading past survey data into a database. The server is configured to efficiently manage this data and enable quick reference as needed. It is preferable to use a database management system such as MySQL or PostgreSQL.

[0387] Next, the terminal provides the user with a user interface (UI) for entering questions. This UI is implemented as a web page built using HTML, CSS, and JavaScript. The UI includes a field for entering questions and a submit button, and is designed to be intuitive for users to use.

[0388] When a user inputs a question and presses the send button, the question is sent from the terminal to the server. This communication is performed asynchronously using the AJAX function, and the question is transmitted to the server via the HTTP / HTTPS protocol.

[0389] When the server receives a question, it sends a request to a generative AI model, such as OpenAI's GPT-4. The server sends the question to the AI ​​model in text format, and the AI ​​model generates an answer appropriate to the question.

[0390] The server then sends the answer received from the generative AI model back to the device, where it is displayed to the user in the device's browser using JavaScript, allowing the user to receive feedback in real time.

[0391] In addition, data exchange between the server and the device is performed using encrypted communication (SSL / TLS), ensuring security. This encryption reduces the risk of data eavesdropping or tampering.

[0392] Specific examples

[0393] For example, say a user has a question about a particular data point. They enter "What does this data point mean?" into a web form on their device. They press submit, sending the question to a server. The server sends the question to a generative AI model, which generates an answer such as "This data point indicates a particular trend." The server receives this answer and sends it back to the device, which displays it to the user, who receives the information instantly.

[0394] Prompt Sentence Examples

[0395] "What does this data point mean?"

[0396] By using this system, users can efficiently refer to past survey data and obtain accurate answers to questions in real time, significantly improving the efficiency of survey work and reducing the burden on users.

[0397] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0398] Step 1:

[0399] The server loads past survey data into a database. Specifically, the server uses a database management system (e.g., MySQL or PostgreSQL) to import data from a data source (e.g., a CSV file or Excel file). The input is a survey data file, which is then saved in the database and used as output. When storing the data in the database, the data is reshaped and formatted.

[0400] Step 2:

[0401] The terminal provides a user interface (UI). This UI is built using HTML, CSS, and JavaScript and displays a web page containing a question input field and a submit button. The user can enter a question in the input field and proceed to the next step by clicking the submit button. The input is the web browser accessed by the user, and the output is the displayed question input interface.

[0402] Step 3:

[0403] The user enters a question and presses the submit button. The input is the question the user enters in the input field, and the output is the question sent from the device to the server. The device uses JavaScript's AJAX function to asynchronously send the user's question to the server as a POST request.

[0404] Step 4:

[0405] The server receives the user's question and sends a request to the generative AI model. The input is the question received by the server, and the output is a prompt to be sent to the generative AI model. The server uses a Python library, for example, to send an API request to the AI ​​model (e.g., GPT-4).

[0406] Step 5:

[0407] The generative AI model receives a request from the server and generates an answer to the question. Here, the input is the prompt sent from the server, and the output is the text of the answer generated by the AI ​​model. The generative AI model uses a pre-trained neural network and performs natural language processing to generate an appropriate answer.

[0408] Step 6:

[0409] The server receives the answer from the generative AI model and sends it back to the device. The input is the text of the answer received from the generative AI model, and the output is an HTTP response to be sent to the device. The server sends this data back to the device using secure communication (e.g., HTTPS).

[0410] Step 7:

[0411] The terminal displays the answer received from the server in a user interface. The input is the text of the answer received from the server, and the output is the answer displayed to the user. The terminal uses JavaScript to display the received answer in a designated area on the web page.

[0412] Step 8:

[0413] Communication between the server and the terminal is always encrypted. The server sets up an SSL / TLS certificate and implements secure HTTPS communication. This encrypts the communication content and reduces the risk of data eavesdropping or tampering. The input is the entire data to be communicated, and the output is the encrypted data sent and received over the network.

[0414] (Application example 1)

[0415] 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."

[0416] In manufacturing processes within factories, it is difficult to grasp information about work progress and equipment status in real time, which often leads to delayed response to problems. Conventional methods require time to analyze manufacturing data and obtain results, which hinders efficient production activities. In addition, specialized engineers must always be on-site, which increases operational costs. This makes it difficult to improve overall productivity.

[0417] 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.

[0418] In this invention, the server includes means for loading past data into a database, means for providing a terminal interface for accepting questions from users, means for transmitting questions entered through the interface to the server, means for the server to receive the questions and send requests to an intelligence with natural language processing capabilities, means for the server to receive answers from the intelligence and return the answers to the terminal, means for the terminal to display the answers, and industrial equipment with a function for accepting questions about the status of equipment and work progress in manufacturing operations and providing answers in real time. This makes it possible to immediately grasp information such as work progress and equipment failures within the factory, enabling efficient and prompt troubleshooting.

[0419] "Historical data" refers to previously collected information and records related to the manufacturing process.

[0420] A "database" refers to a structured collection of data for efficient management and retrieval of data.

[0421] "Interface" refers to the component of a system that provides the means for users to enter questions and obtain information.

[0422] "Accepting a question" refers to the system recognizing and processing an inquiry from a user.

[0423] A "server" refers to a computer that provides services to users on a network.

[0424] "Natural language processing" refers to technology for understanding and processing human language.

[0425] "Intelligence" refers to systems that use generative AI models and machine learning algorithms to generate answers to user questions.

[0426] "Sending a request" means sending a request to another system for a particular action or piece of information.

[0427] "Industrial equipment" refers to the entire machinery, equipment, and systems used in manufacturing.

[0428] "Equipment status" refers to the current operating status and operating conditions of each piece of equipment and machine in the factory.

[0429] "Work progress" refers to the current progress in the manufacturing process.

[0430] "Storage" refers to hardware or devices for storing digital data.

[0431] "Web page" means a document containing information or an interface that can be accessed over the Internet.

[0432] To implement the present invention, the following system configuration and program are required: This system loads past manufacturing data into a database, and when a user inputs a question through a terminal interface, it uses a generative AI model to provide an answer in real time.

[0433] System configuration

[0434] 1. Database:

[0435] This database stores past manufacturing data and is constructed to enable efficient search and management. SQLite3 is used as a specific example of the database.

[0436] 2. User Interface (Interface):

[0437] This is the terminal interface for users to enter and submit questions. It consists of a web page with a question input field and a submit button.

[0438] 3. Server:

[0439] This server receives questions from users and sends requests to an AI model with natural language processing capabilities (generative AI model). The OpenAI API can be used as the AI ​​model.

[0440] 4. Intelligence (generative AI models):

[0441] It uses technology for understanding and processing human language to generate answers to user questions. This intelligence uses OpenAI's GPT-3 as its generative AI model.

[0442] System processing flow

[0443] 1. Data Acquisition:

[0444] The server first searches the database based on the input query and retrieves relevant past manufacturing data.

[0445] 2. Answer generation from AI models:

[0446] If there is no relevant data in the database, the server will send the user's question to the generative AI model to generate an appropriate answer. The prompt for the question will be sent in the following format:

[0447] "Generate appropriate answers based on user questions: [question]"

[0448] 3. Returning the response:

[0449] The server receives the answer from the generative AI model and sends it back to the device.

[0450] 4. Show Answers:

[0451] The terminal displays the received answers to the user, allowing the user to obtain answers to their questions in real time.

[0452] Specific examples of hardware and software used

[0453] Database server: SQLite3

[0454] AI model: OpenAI GPT-3

[0455] Network communication: Ensure security by using encrypted communication (e.g., TLS / SSL).

[0456] Specific examples

[0457] For example, a user working on a production line might type into the interface, "What does error code 45 on this machine mean?" This question is sent to the server, which first searches the database. If no matching data is found, the server sends the question to a generative AI model, as follows:

[0458] "Generate an appropriate answer based on the user's question: What does error code 45 mean on this device?"

[0459] Upon receiving an answer from the generative AI model (e.g., "Error code 45 indicates a faulty temperature sensor"), the server sends it back to the device, which immediately displays the answer to the user.

[0460] This system makes it possible to grasp information on work progress and equipment status within the factory in real time, enabling efficient and rapid response to problems.

[0461] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0462] Step 1:

[0463] The user enters a question through the terminal interface and presses the send button. The input is a textual question, for example, "What does error code 45 on this device mean?" The output is the question data that is sent to the server.

[0464] Step 2:

[0465] The server receives questions sent by users. The input is the textual question data from the user, and the output is to store the received question data and pass it on to the next process.

[0466] Step 3:

[0467] The server searches the database based on the received question data. The server uses SQLite3 to search for data points related to the question. The input is the received question data, and the output is the data points as search results. If the corresponding data exists in the database, the data points are retrieved.

[0468] Step 4:

[0469] Validates the results of a database search. The input is the database search results, and the output is a determination of whether or not data exists. If relevant data is found, that information is used to generate an answer for the user.

[0470] Step 5:

[0471] If there is no relevant data in the database, the server sends the question to the generative AI model. The question is sent using the GPT-3 API, and the AI ​​model generates an answer. The input is a prompt containing the user's question: "Generate an appropriate answer based on the user's question: [question]", and the output is a text answer from the AI ​​model.

[0472] Step 6:

[0473] The server receives the answer obtained from the generative AI model. The input is the text-format answer data from the AI ​​model, and the output is to store the received answer data and pass it on to the next process.

[0474] Step 7:

[0475] The server returns the received response to the terminal. The input is the saved response data, and the output is the response data sent to the terminal.

[0476] Step 8:

[0477] The terminal displays the answer received from the server to the user. The input is the text-format answer data sent from the server, and the output is the answer displayed on the user interface. The user can check this and obtain information in real time.

[0478] Specific actions

[0479] For example, a user enters a question such as "What does error code 45 on this device indicate?" and presses the send button. The question is sent to the server, which searches the database. If the data does not exist, the server sends the question as a prompt to the generative AI model, and receives the answer from the AI: "Error code 45 indicates a faulty temperature sensor." This answer is sent back to the device, which displays it to the user.

[0480] 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.

[0481] The system of the present invention has the following features to efficiently utilize past survey data and provide real-time answers to user questions: It also incorporates an emotion engine to recognize user emotions, improving the user experience.

[0482] When the server starts up, it first establishes a database connection and loads past survey data into the database. The database is a storage device for saving and searching data, and stores past survey data.

[0483] Next, the terminal provides a user interface (UI) for the user to input a question. This UI is displayed on a web browser and is configured as a web form including a question input field and a submit button.

[0484] When a user inputs a question and presses the send button, the device sends the question to the server. The server receives the question and passes it to the emotion engine. The emotion engine analyzes the user's emotional state from the input and determines, for example, whether the user is confused or in a hurry.

[0485] The emotion engine suggests appropriate wording and tone for the question based on the user's emotional state and returns this information to the server. The server then uses this information to request a question from an artificial intelligence (AI) with chat generation capabilities. The AI ​​receives the question, generates an appropriate answer, and returns it to the server.

[0486] The server sends the AI's answers back to the device, which then displays them to the user, allowing the user to get answers to their questions in real time, with the answers delivered in a tone that matches the user's emotional state.

[0487] Specific examples

[0488] For example, if a user enters something in a hurry, such as "I don't understand the meaning of this data point!", the emotion engine will sense the user's impatience. Based on this information, the server will add additional information to the question it sends to the AI, such as "The user is in a hurry, so a concise, straightforward answer is required." The AI ​​will then take this additional information into account and generate a specific, quick answer, such as "That data point shows the growth rate." The server will then send this answer back to the device, which will display it to the user. As a result, the user can obtain appropriate information in real time and receive an answer that is in line with their emotional state.

[0489] In this way, the present invention provides a system that recognizes user emotions and utilizes past survey data to quickly and accurately answer user questions, improving the user experience and streamlining the survey process.

[0490] The processing flow will be explained below.

[0491] Step 1:

[0492] The server establishes a database connection on startup, which is a storage for past survey data, allowing it to be quickly referenced when needed.

[0493] Step 2:

[0494] The server opens the past survey data file and stores it line by line in the database. For example, it reads a CSV file, parses each line, and inserts it into the database. It also handles errors and verifies that the data was loaded correctly.

[0495] Step 3:

[0496] The terminal provides a user interface (UI) that allows the user to enter a question. This UI is displayed in a web browser and is structured as a web form that includes a question input field and a submit button.

[0497] Step 4:

[0498] The user enters a question into a web form on the device and presses the submit button, for example, "What does this data point mean?"

[0499] Step 5:

[0500] The device catches the form submission event, converts the question into JSON format using JavaScript, and then sends the question data to the server via asynchronous communication (AJAX).

[0501] Step 6:

[0502] The server receives the question sent by the user, saves the question in a temporary variable, and then passes the question to the emotion engine to analyze the user's emotional state.

[0503] Step 7:

[0504] The emotion engine analyzes the user's emotional state from the text they input. For example, it uses natural language processing to infer emotions such as "confusion" or "irritation."

[0505] Step 8:

[0506] The emotion engine sends the analysis results back to the server, including information suggesting appropriate expressions and tones based on the emotional state.

[0507] Step 9:

[0508] The server requests a question from an artificial intelligence (AI) system with chat generation capabilities based on the information obtained from the emotion engine. For example, the server includes information such as "The user is confused and needs a concise and clear answer."

[0509] Step 10:

[0510] The chat generation AI generates text based on the received question and additional information, generating an appropriate response, which is then sent back to the server in JSON format.

[0511] Step 11:

[0512] The server analyzes the response received from the AI, formats it as needed, and then converts it back into JSON format for sending back to the device.

[0513] Step 12:

[0514] The terminal receives the answer sent back from the server and displays it again in the UI on the web browser. The answer text is immediately visible to the user.

[0515] Step 13:

[0516] Users can view answers displayed on their devices to find solutions or explanations to their questions, and answers are delivered in a tone that reflects their emotional state, improving the user experience.

[0517] In this way, the present invention provides a system that efficiently utilizes past survey data to provide answers to users' questions in real time, and by combining it with an emotion engine, it provides appropriate answers that correspond to the user's emotional state.

[0518] Example 2

[0519] 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."

[0520] Conventional question-answering systems provide answers without considering the user's emotional state, resulting in a poor user experience. They also struggle to efficiently utilize past survey data and provide prompt and accurate responses. This often leaves users frustrated, and they sometimes fail to provide appropriate responses, especially for urgent questions.

[0521] 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.

[0522] In this invention, the server includes means for loading past survey data into a database, means for providing a user interface for a terminal that accepts questions from users, means for transmitting questions entered through the user interface to the server, means for the server to receive the questions and analyze the user's emotional state using an emotion analysis engine, means for generating a prompt sentence for a generative AI model based on the emotional state analyzed by the emotion analysis engine and transmitting the request, means for the server to receive an answer obtained from the generative AI model and return the answer to the terminal, and means for the terminal to display the answer, thereby enabling a quick and appropriate response that takes the user's emotional state into consideration.

[0523] A "server" is a computing device that receives requests from clients and processes them appropriately.

[0524] A "database" is a data storage system that stores past survey data and allows for efficient storage, retrieval, updating, and deletion of data.

[0525] A "user interface" is an interface through which a user interacts with a computer system, and is provided in the form of a web page that includes fields for entering questions and the like.

[0526] An "emotion analysis engine" is software or a system that analyzes text data entered by a user and detects the user's emotional state (e.g., frustration, confusion, joy).

[0527] A "generative AI model" is a model generated by artificial intelligence and used to generate appropriate answers to user questions.

[0528] A "prompt sentence" is a text sentence that provides appropriate background information to the generative AI model, and includes the user's emotional state and the question content.

[0529] A "question" refers to a specific inquiry that a user poses to a computer system, and is data that is processed through a server.

[0530] An "answer" is a response generated by a generative AI model in response to a user's question, and contains appropriate information.

[0531] MODE FOR CARRYING OUT THE INVENTION

[0532] The system according to the present invention has the following hardware and software configuration in order to efficiently utilize past survey data and provide answers to user questions in real time.

[0533] Hardware and software used

[0534] 1. Hardware

[0535] Server (a computer with a high-performance processor and sufficient memory)

[0536] User devices (PCs, tablets, smartphones, etc.)

[0537] 2. Software

[0538] Database Management System (DBMS): MySQL, PostgreSQL, etc.

[0539] Web browser: Google Chrome, Mozilla Firefox, etc.

[0540] Sentiment analysis engine: General natural language processing software (e.g., IBM Watson Tone Analyzer, Google Cloud Natural Language)

[0541] Generative AI model: OpenAI's GPT-3 or a similar model

[0542] Data processing flow

[0543] 1. Starting the server and connecting to the database

[0544] When the server starts, it establishes a connection with a database management system, which stores past survey data, and performs read operations on the database.

[0545] 2. Providing a user interface

[0546] The terminal provides a web browser interface for users to input questions. This interface is composed of HTML, CSS, and JavaScript, and includes a question input field and a submit button.

[0547] 3. User inputs and submits question

[0548] When a user enters a question and presses the submit button, the device parses the question into JSON format and sends it to the server using an HTTP POST request.

[0549] 4. Emotion analysis using an emotion analysis engine

[0550] The server passes the received question to a sentiment analysis engine, which analyzes the question and returns the user's emotional state in JSON format. For example, it can determine whether the user is impatient or confused.

[0551] 5. Prompt generation and request transmission to the generative AI model

[0552] Based on the emotional information obtained from the emotion analysis engine, the server generates a prompt sentence, which is used when sending a request to the generative AI model. An example of a prompt sentence is as follows:

[0553] User Question: "I don't understand what this data point means!"

[0554] Emotion engine analysis results: The user feels impatient

[0555] AI instructions:

[0556] "Users are in a hurry and need short, straightforward answers.

[0557] For example: That data point shows the growth rate.”

[0558] 6. Receiving and displaying generated answers

[0559] The generative AI model generates an appropriate answer based on the prompt sentence. The generated answer is sent back to the server, which then sends it to the user's device. The device receives the answer and displays it on the user interface.

[0560] Specific examples

[0561] For example, if a user types "I don't understand what this data point means!" and presses the submit button, the following process will occur:

[0562] The server passes the question to a sentiment analysis engine to analyze the user's sense of urgency.

[0563] Based on the results obtained from the sentiment analysis engine, the server generates prompt sentences for the generative AI model.

[0564] Based on the prompt sent to the generative AI model, an appropriate answer is generated, such as "That data point indicates a growth rate."

[0565] This answer is sent to the user's terminal via the server, and the user can check the answer on a web browser.

[0566] As described above, the present invention provides a system that recognizes the user's emotional state and utilizes past survey data to quickly and accurately answer the user's questions, thereby improving the user experience and streamlining the survey process.

[0567] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0568] Step 1: Starting the server and connecting to the database

[0569] When the server starts, it establishes a connection to the database management system (DBMS). Specifically, the server attempts to connect using the database connection string, and if successful, loads the past survey data from the database into memory.

[0570] Input: Database connection string

[0571] Output: Database connection established and data loaded successfully

[0572] What it does: The server loads the database connection settings on startup, and once the connection is established, it uses SQL queries to retrieve past survey data and stores it in memory.

[0573] Step 2: Providing a User Interface

[0574] The device provides a user interface (UI) on a web browser, specifically by using HTML, CSS, and JavaScript to display a page containing a question input field and a submit button.

[0575] Input: Access to the terminal

[0576] Output: UI displayed in a web browser

[0577] Specific operation: The device receives the UI file from the HTTP server, renders it, and displays it to the user. It then waits for the user to enter a question.

[0578] Step 3: User enters question and submits

[0579] The user enters a question through the device's UI and presses the submit button. The device parses the question into JSON format and sends it to the server using an HTTP POST request.

[0580] Input: User question

[0581] Output: The question parsed into JSON and sent to the server

[0582] Specific behavior: The user types "I don't understand the meaning of this data point!" and presses the send button. The device converts this to JSON format and sends it to the server as an HTTP POST request.

[0583] Step 4: Sentiment analysis using the sentiment analysis engine

[0584] The server passes the received question to a sentiment analysis engine to analyze the user's emotional state. The sentiment analysis engine analyzes the text data and returns the emotional state to the server in JSON format.

[0585] Input: JSON formatted question

[0586] Output: JSON format data indicating emotional state

[0587] Specific operation: The server sends the received question to the sentiment analysis engine and receives the analysis result (e.g., the user is impatient).

[0588] Step 5: Generate a prompt and send a request to the generative AI model

[0589] The server generates a prompt based on the results of the emotion analysis engine and sends a request to the generative AI model. The prompt includes the user's question and emotional state.

[0590] Input: JSON data representing emotional states

[0591] Output: Prompts and requests sent to the generative AI model

[0592] Specific operation: The server generates a prompt sentence containing the instruction "The user is impatient and needs a concise and straightforward answer" and sends it to the generative AI model.

[0593] Step 6: Receive and view the generated answers

[0594] The generative AI model generates an appropriate response based on the prompt and sends it back to the server, which then sends the response to the user's device, which displays it on the UI.

[0595] Input: Answer from a generative AI model

[0596] Output: Send and display responses to the terminal

[0597] Specific operation: The generative AI model generates an answer such as "That data point indicates a growth rate" and sends it back to the server. The server then sends this answer to the device, which displays it on the UI so that the user can confirm it.

[0598] (Application example 2)

[0599] 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."

[0600] While existing systems provide a way to answer users' questions in real time by utilizing past survey data, they lack a method for responding appropriately based on the user's emotional state. As a result, the user experience is often unsatisfactory, and it is difficult to provide appropriate support, especially when the user is confused or in a hurry. Therefore, there is a need for a system that can analyze the user's emotional state and adjust the tone of the questions accordingly.

[0601] 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.

[0602] In this invention, the server includes means for loading past survey data into a database, means for providing a user interface for a terminal that accepts questions from users, means for transmitting questions entered through the user interface to the server, means for the server to receive the questions and analyze the user's emotional state using an emotion analysis engine, means for the server to send a request to an artificial intelligence (AI) with a chat generation function based on the emotion analysis results, means for the server to receive answers obtained from the AI ​​and return the answers to the terminal, and means for the terminal to display the answers, thereby enabling real-time answers in an optimal tone according to the user's emotional state.

[0603] "Historical Survey Data" means information and statistics previously collected and stored.

[0604] A "database" is a system for organizing and storing information and data.

[0605] A "terminal" is a computer device that is operated by a user.

[0606] A "user interface" refers to the screen and input device that a user uses to operate a system.

[0607] A "server" is a computer system that provides services to other computers and terminals over a network.

[0608] An "emotion analysis engine" is software or algorithms for analyzing a user's emotional state from input data.

[0609] The "chat generation function" is a function that generates appropriate answers to input questions.

[0610] "Artificial intelligence" is a technology that uses computers to achieve human-like intelligence.

[0611] An "answer" is a response or answer to a question.

[0612] The system that realizes this application example is built using the following programs and hardware: The system leverages past survey data to quickly and accurately answer users' questions and can adjust its tone based on the user's emotional state.

[0613] The server first connects to a database (e.g., MySQL) to load past survey data. This database is a system for organizing and storing information and data. Then, a terminal built using React Native to provide the user interface accepts user questions.

[0614] When a user inputs a question and presses the send button, the device sends the question to the server. When the server receives the question, it analyzes the user's emotional state using an emotion analysis engine (e.g., a TensorFlow model). This emotion analysis engine is software or an algorithm for analyzing the user's emotional state from the user's input data.

[0615] Next, the server sends a question to an AI (e.g., OpenAI GPT-4) based on the results of this emotion analysis. The AI, which has chat generation capabilities, has the ability to generate an appropriate answer to the question. The answer obtained from the AI ​​is sent back to the server, which then sends it back to the device.

[0616] The device then displays the received answers to the user through a user interface, allowing the user to receive answers to their questions in real time and in a tone appropriate to their emotional state.

[0617] For example, if a user types, "I'm not sure what to order!", the server uses a TensorFlow model to analyze the user's confusion. It then sends a prompt to OpenAI GPT-4: "The user is confused and needs quick and clear advice. If they're unsure what to order, what would you suggest?" The AI ​​responds by generating a response like, "I see you're having trouble. I recommend the chef's special pasta, which is a popular choice."

[0618] In this way, the system recognizes user emotions and leverages past survey data to provide quick and personalized answers to user questions, improving the user experience and enabling appropriate support for users in areas such as food delivery.

[0619] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0620] Step 1:

[0621] When the server starts, it establishes a database connection. At this time, the server loads past survey data from the database (MySQL). The input is the database connection information, and the output is the loaded past survey data. The information stored in the database is efficiently retrieved and prepared in a form that can be used by the end user.

[0622] Step 2:

[0623] The terminal provides a user interface for the user to input a question. React Native is used to display a question input field and a submit button on the screen. The input is the screen on the device that the user sees and operates, and the output is the question text entered by the user. The terminal screen displays an area for entering the question text and a submit button.

[0624] Step 3:

[0625] When a user enters a question and presses the send button, the terminal sends the question to the server. The input is the question text entered by the user, and the output is the question data to be sent to the server. The terminal sends the question to the server in the form of an HTTP request.

[0626] Step 4:

[0627] The server passes the received question data to a sentiment analysis engine (TensorFlow model). The sentiment analysis engine analyzes the question text and estimates the user's emotional state. The input is the question data, and the output is the user's emotional state (e.g., confused, urgent). The sentiment analysis engine extracts features from the text data and estimates the user's emotion.

[0628] Step 5:

[0629] Based on the emotional state returned by the emotion analysis engine, the server sends a prompt to an artificial intelligence (OpenAI GPT-4) with chat generation capabilities. The prompt contains information about the user's emotional state. The input is the user's emotional state and question data, and the output is a prompt to the AI. The server generates a specific prompt taking the emotional state into consideration and sends it to the AI.

[0630] Step 6:

[0631] The artificial intelligence (OpenAI GPT-4) generates an appropriate answer based on the received prompt. The input is the prompt, and the output is the generated answer text. The artificial intelligence processes the data according to the prompt and generates an appropriate answer.

[0632] Step 7:

[0633] The server receives the answer text obtained from the AI ​​and sends it back to the terminal. The input is the answer data from the AI, and the output is the answer data sent to the terminal. The server receives the AI's answer, converts the format as needed, and sends it to the terminal.

[0634] Step 8:

[0635] The terminal displays the received answer text on the user interface. The input is the answer data from the server, and the output is the display content on the user interface. The answer text is displayed on the terminal screen in a format that the user can easily understand.

[0636] This allows users to get answers to their questions in real time and receive support in a tone that matches their emotional state.

[0637] 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.

[0638] 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.

[0639] 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.

[0640] [Third embodiment]

[0641] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0642] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0643] 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).

[0644] 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.

[0645] 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.

[0646] 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).

[0647] 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.

[0648] 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.

[0649] 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.

[0650] 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.

[0651] 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.

[0652] 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."

[0653] The system provided by the present invention begins by loading past survey data into a database. This allows the data to be quickly referenced as needed, allowing users to efficiently use the survey data. Next, the terminal provides the user with a user interface (UI) for entering questions. This UI is configured as a web form including a question input field and a submit button.

[0654] When a user enters a question and presses the send button, the question is sent from the device to the server. The server receives the question and requests the question content from an artificial intelligence (AI) with chat generation functionality. Specifically, the server sends the question in text format to the AI ​​and receives a response from the AI. This AI has natural language processing capabilities and is equipped with a model for generating appropriate responses to user questions.

[0655] The server sends the answer received from the AI ​​back to the device, which then displays the answer to the user. This allows the user to get answers to their questions in real time. Data exchange between the server, device, and user is carried out via network communication, and encrypted communication is used as a security measure.

[0656] Specific examples

[0657] For example, if a user is working on a research project and has a question about a particular data point, such as "What does this data point mean?", they can enter "What does this data point mean?" into a web form on their device. They press submit, which sends the question to the server. The server then sends the question to an AI, which generates an answer to the question. The answer might be something like "This data point indicates a particular trend." The server receives this answer and sends it back to the device, which then displays it to the user, providing instant information.

[0658] This system allows users to refer to past survey data and obtain answers to questions in real time, greatly improving the efficiency of survey work. In addition, AI-based answers are fast and accurate, reducing the burden on users and improving the quality of surveys. This system can be applied to survey work in a variety of fields, providing great convenience.

[0659] The processing flow will be explained below.

[0660] Step 1:

[0661] When the server starts up, it establishes a database connection, which is a storage for past survey data.

[0662] Step 2:

[0663] The server opens the past survey data file and stores each line in the database. At this time, it performs error handling and checks the log to confirm that the data was loaded successfully.

[0664] Step 3:

[0665] The terminal provides a user interface (UI) on a web browser that allows the user to enter a question. The UI is configured as a web form that includes a question input field and a submit button.

[0666] Step 4:

[0667] The user enters a question into a web form on the device and presses the submit button, for example, entering a specific question such as "What does this data point mean?"

[0668] Step 5:

[0669] The device catches the form submission event, converts the question into JSON format using JavaScript, and then sends the question data to the server via asynchronous communication (AJAX).

[0670] Step 6:

[0671] The server receives the question sent by the user, saves the question in a temporary variable, and then constructs data to request the question from an artificial intelligence (AI) with chat generation capabilities.

[0672] Step 7:

[0673] The server issues an API request to send the question to the AI, which includes the question text as well as parameters such as the maximum number of tokens.

[0674] Step 8:

[0675] The AI ​​generates text based on the received question and creates an appropriate answer, which is then sent back to the server in JSON format.

[0676] Step 9:

[0677] The server analyzes the response received from the AI, formats it as needed, and then converts it back into JSON format for sending back to the device.

[0678] Step 10:

[0679] The terminal receives the answer sent back from the server and displays it again on the UI in the web browser, where the answer text is immediately visible to the user.

[0680] Step 11:

[0681] Users can check the answers displayed on their devices and get solutions and explanations for any questions they have, which allows them to carry out their research more efficiently.

[0682] Example 1

[0683] 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."

[0684] There is a need for a system that can efficiently use past survey data and provide quick and accurate answers to user questions. Furthermore, there is a need for a system that can obtain answers to user questions in real time while ensuring communication security. With conventional methods, referencing data and answering questions is time-consuming, resulting in a decrease in work efficiency.

[0685] 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.

[0686] In this invention, the server includes means for loading past survey data into a database, means for providing a user interface for a terminal that accepts questions from users, means for transmitting questions entered through the user interface to the server, means for transmitting requests to the generative AI model, means for receiving answers obtained from the generative AI model and returning the answers to the terminal, means for the terminal to display the answers, and means for ensuring security by encrypting communication between the server and the terminal. This allows users to efficiently use past survey data while obtaining accurate answers in real time.

[0687] A "database" is a specific structure or system for efficiently storing, managing, and retrieving data.

[0688] A "server" is a computer system that processes requests from clients via a network and provides services and data.

[0689] "Terminal" means a computer system or device capable of input and output used by a User.

[0690] A "user interface" is the means or screen configuration through which a user interacts with a system or application.

[0691] "Artificial intelligence" is a technology that allows machines to mimic human intelligent activity and have the ability to understand, learn, and respond.

[0692] A "generative AI model" is a type of artificial intelligence that uses pre-trained neural networks to generate and understand natural language at a near-human level.

[0693] A "web page" is a format for displaying documents and content on the Internet that can be accessed through a web browser.

[0694] "Encrypted communication" is a communication method that uses encryption technology to protect information from third parties when sending and receiving data.

[0695] The system provided by this invention starts by loading past survey data into a database. The server is configured to efficiently manage this data and enable quick reference as needed. It is preferable to use a database management system such as MySQL or PostgreSQL.

[0696] Next, the terminal provides the user with a user interface (UI) for entering questions. This UI is implemented as a web page built using HTML, CSS, and JavaScript. The UI includes a field for entering questions and a submit button, and is designed to be intuitive for users to use.

[0697] When a user inputs a question and presses the send button, the question is sent from the terminal to the server. This communication is performed asynchronously using the AJAX function, and the question is transmitted to the server via the HTTP / HTTPS protocol.

[0698] When the server receives a question, it sends a request to a generative AI model, such as OpenAI's GPT-4. The server sends the question to the AI ​​model in text format, and the AI ​​model generates an answer appropriate to the question.

[0699] The server then sends the answer received from the generative AI model back to the device, where it is displayed to the user in the device's browser using JavaScript, allowing the user to receive feedback in real time.

[0700] In addition, data exchange between the server and the device is performed using encrypted communication (SSL / TLS), ensuring security. This encryption reduces the risk of data eavesdropping or tampering.

[0701] Specific examples

[0702] For example, say a user has a question about a particular data point. They enter "What does this data point mean?" into a web form on their device. They press submit, sending the question to a server. The server sends the question to a generative AI model, which generates an answer such as "This data point indicates a particular trend." The server receives this answer and sends it back to the device, which displays it to the user, who receives the information instantly.

[0703] Prompt Sentence Examples

[0704] "What does this data point mean?"

[0705] By using this system, users can efficiently refer to past survey data and obtain accurate answers to questions in real time, significantly improving the efficiency of survey work and reducing the burden on users.

[0706] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0707] Step 1:

[0708] The server loads past survey data into a database. Specifically, the server uses a database management system (e.g., MySQL or PostgreSQL) to import data from a data source (e.g., a CSV file or Excel file). The input is a survey data file, which is then saved in the database and used as output. When storing the data in the database, the data is reshaped and formatted.

[0709] Step 2:

[0710] The terminal provides a user interface (UI). This UI is built using HTML, CSS, and JavaScript and displays a web page containing a question input field and a submit button. The user can enter a question in the input field and proceed to the next step by clicking the submit button. The input is the web browser accessed by the user, and the output is the displayed question input interface.

[0711] Step 3:

[0712] The user enters a question and presses the submit button. The input is the question the user enters in the input field, and the output is the question sent from the device to the server. The device uses JavaScript's AJAX function to asynchronously send the user's question to the server as a POST request.

[0713] Step 4:

[0714] The server receives the user's question and sends a request to the generative AI model. The input is the question received by the server, and the output is a prompt to be sent to the generative AI model. The server uses a Python library, for example, to send an API request to the AI ​​model (e.g., GPT-4).

[0715] Step 5:

[0716] The generative AI model receives a request from the server and generates an answer to the question. Here, the input is the prompt sent from the server, and the output is the text of the answer generated by the AI ​​model. The generative AI model uses a pre-trained neural network and performs natural language processing to generate an appropriate answer.

[0717] Step 6:

[0718] The server receives the answer from the generative AI model and sends it back to the device. The input is the text of the answer received from the generative AI model, and the output is an HTTP response to be sent to the device. The server sends this data back to the device using secure communication (e.g., HTTPS).

[0719] Step 7:

[0720] The terminal displays the answer received from the server in a user interface. The input is the text of the answer received from the server, and the output is the answer displayed to the user. The terminal uses JavaScript to display the received answer in a designated area on the web page.

[0721] Step 8:

[0722] Communication between the server and the terminal is always encrypted. The server sets up an SSL / TLS certificate and implements secure HTTPS communication. This encrypts the communication content and reduces the risk of data eavesdropping or tampering. The input is the entire data to be communicated, and the output is the encrypted data sent and received over the network.

[0723] (Application example 1)

[0724] 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."

[0725] In manufacturing processes within factories, it is difficult to grasp information about work progress and equipment status in real time, which often leads to delayed response to problems. Conventional methods require time to analyze manufacturing data and obtain results, which hinders efficient production activities. In addition, specialized engineers must always be on-site, which increases operational costs. This makes it difficult to improve overall productivity.

[0726] 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.

[0727] In this invention, the server includes means for loading past data into a database, means for providing a terminal interface for accepting questions from users, means for transmitting questions entered through the interface to the server, means for the server to receive the questions and send requests to an intelligence with natural language processing capabilities, means for the server to receive answers from the intelligence and return the answers to the terminal, means for the terminal to display the answers, and industrial equipment with a function for accepting questions about the status of equipment and work progress in manufacturing operations and providing answers in real time. This makes it possible to immediately grasp information such as work progress and equipment failures within the factory, enabling efficient and prompt troubleshooting.

[0728] "Historical data" refers to previously collected information and records related to the manufacturing process.

[0729] A "database" refers to a structured collection of data for efficient management and retrieval of data.

[0730] "Interface" refers to the component of a system that provides the means for users to enter questions and obtain information.

[0731] "Accepting a question" refers to the system recognizing and processing an inquiry from a user.

[0732] A "server" refers to a computer that provides services to users on a network.

[0733] "Natural language processing" refers to technology for understanding and processing human language.

[0734] "Intelligence" refers to systems that use generative AI models and machine learning algorithms to generate answers to user questions.

[0735] "Sending a request" means sending a request to another system for a particular action or piece of information.

[0736] "Industrial equipment" refers to the entire machinery, equipment, and systems used in manufacturing.

[0737] "Equipment status" refers to the current operating status and operating conditions of each piece of equipment and machine in the factory.

[0738] "Work progress" refers to the current progress in the manufacturing process.

[0739] "Storage" refers to hardware or devices for storing digital data.

[0740] "Web page" means a document containing information or an interface that can be accessed over the Internet.

[0741] To implement the present invention, the following system configuration and program are required: This system loads past manufacturing data into a database, and when a user inputs a question through a terminal interface, it uses a generative AI model to provide an answer in real time.

[0742] System configuration

[0743] 1. Database:

[0744] This database stores past manufacturing data and is constructed to enable efficient search and management. SQLite3 is used as a specific example of the database.

[0745] 2. User Interface (Interface):

[0746] This is the terminal interface for users to enter and submit questions. It consists of a web page with a question input field and a submit button.

[0747] 3. Server:

[0748] This server receives questions from users and sends requests to an AI model with natural language processing capabilities (generative AI model). The OpenAI API can be used as the AI ​​model.

[0749] 4. Intelligence (generative AI models):

[0750] It uses technology for understanding and processing human language to generate answers to user questions. This intelligence uses OpenAI's GPT-3 as its generative AI model.

[0751] System processing flow

[0752] 1. Data Acquisition:

[0753] The server first searches the database based on the input query and retrieves relevant past manufacturing data.

[0754] 2. Answer generation from AI models:

[0755] If there is no relevant data in the database, the server will send the user's question to the generative AI model to generate an appropriate answer. The prompt for the question will be sent in the following format:

[0756] "Generate appropriate answers based on user questions: [question]"

[0757] 3. Returning the response:

[0758] The server receives the answer from the generative AI model and sends it back to the device.

[0759] 4. Show Answers:

[0760] The terminal displays the received answers to the user, allowing the user to obtain answers to their questions in real time.

[0761] Specific examples of hardware and software used

[0762] Database server: SQLite3

[0763] AI model: OpenAI GPT-3

[0764] Network communication: Ensure security by using encrypted communication (e.g., TLS / SSL).

[0765] Specific examples

[0766] For example, a user working on a production line might type into the interface, "What does error code 45 on this machine mean?" This question is sent to the server, which first searches the database. If no matching data is found, the server sends the question to a generative AI model, as follows:

[0767] "Generate an appropriate answer based on the user's question: What does error code 45 mean on this device?"

[0768] Upon receiving an answer from the generative AI model (e.g., "Error code 45 indicates a faulty temperature sensor"), the server sends it back to the device, which immediately displays the answer to the user.

[0769] This system makes it possible to grasp information on work progress and equipment status within the factory in real time, enabling efficient and rapid response to problems.

[0770] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0771] Step 1:

[0772] The user enters a question through the terminal interface and presses the send button. The input is a textual question, for example, "What does error code 45 on this device mean?" The output is the question data that is sent to the server.

[0773] Step 2:

[0774] The server receives questions sent by users. The input is the textual question data from the user, and the output is to store the received question data and pass it on to the next process.

[0775] Step 3:

[0776] The server searches the database based on the received question data. The server uses SQLite3 to search for data points related to the question. The input is the received question data, and the output is the data points as search results. If the corresponding data exists in the database, the data points are retrieved.

[0777] Step 4:

[0778] Validates the results of a database search. The input is the database search results, and the output is a determination of whether or not data exists. If relevant data is found, that information is used to generate an answer for the user.

[0779] Step 5:

[0780] If there is no relevant data in the database, the server sends the question to the generative AI model. The question is sent using the GPT-3 API, and the AI ​​model generates an answer. The input is a prompt containing the user's question: "Generate an appropriate answer based on the user's question: [question]", and the output is a text answer from the AI ​​model.

[0781] Step 6:

[0782] The server receives the answer obtained from the generative AI model. The input is the text-format answer data from the AI ​​model, and the output is to store the received answer data and pass it on to the next process.

[0783] Step 7:

[0784] The server returns the received response to the terminal. The input is the saved response data, and the output is the response data sent to the terminal.

[0785] Step 8:

[0786] The terminal displays the answer received from the server to the user. The input is the text-format answer data sent from the server, and the output is the answer displayed on the user interface. The user can check this and obtain information in real time.

[0787] Specific actions

[0788] For example, a user enters a question such as "What does error code 45 on this device indicate?" and presses the send button. The question is sent to the server, which searches the database. If the data does not exist, the server sends the question as a prompt to the generative AI model, and receives the answer from the AI: "Error code 45 indicates a faulty temperature sensor." This answer is sent back to the device, which displays it to the user.

[0789] 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.

[0790] The system of the present invention has the following features to efficiently utilize past survey data and provide real-time answers to user questions: It also incorporates an emotion engine to recognize user emotions, improving the user experience.

[0791] When the server starts up, it first establishes a database connection and loads past survey data into the database. The database is a storage device for saving and searching data, and stores past survey data.

[0792] Next, the terminal provides a user interface (UI) for the user to input a question. This UI is displayed on a web browser and is configured as a web form including a question input field and a submit button.

[0793] When a user inputs a question and presses the send button, the device sends the question to the server. The server receives the question and passes it to the emotion engine. The emotion engine analyzes the user's emotional state from the input and determines, for example, whether the user is confused or in a hurry.

[0794] The emotion engine suggests appropriate wording and tone for the question based on the user's emotional state and returns this information to the server. The server then uses this information to request a question from an artificial intelligence (AI) with chat generation capabilities. The AI ​​receives the question, generates an appropriate answer, and returns it to the server.

[0795] The server sends the AI's answers back to the device, which then displays them to the user, allowing the user to get answers to their questions in real time, with the answers delivered in a tone that matches the user's emotional state.

[0796] Specific examples

[0797] For example, if a user enters something in a hurry, such as "I don't understand the meaning of this data point!", the emotion engine will sense the user's impatience. Based on this information, the server will add additional information to the question it sends to the AI, such as "The user is in a hurry, so a concise, straightforward answer is required." The AI ​​will then take this additional information into account and generate a specific, quick answer, such as "That data point shows the growth rate." The server will then send this answer back to the device, which will display it to the user. As a result, the user can obtain appropriate information in real time and receive an answer that is in line with their emotional state.

[0798] In this way, the present invention provides a system that recognizes user emotions and utilizes past survey data to quickly and accurately answer user questions, improving the user experience and streamlining the survey process.

[0799] The processing flow will be explained below.

[0800] Step 1:

[0801] The server establishes a database connection on startup, which is a storage for past survey data, allowing it to be quickly referenced when needed.

[0802] Step 2:

[0803] The server opens the past survey data file and stores it line by line in the database. For example, it reads a CSV file, parses each line, and inserts it into the database. It also handles errors and verifies that the data was loaded correctly.

[0804] Step 3:

[0805] The terminal provides a user interface (UI) that allows the user to enter a question. This UI is displayed in a web browser and is structured as a web form that includes a question input field and a submit button.

[0806] Step 4:

[0807] The user enters a question into a web form on the device and presses the submit button, for example, "What does this data point mean?"

[0808] Step 5:

[0809] The device catches the form submission event, converts the question into JSON format using JavaScript, and then sends the question data to the server via asynchronous communication (AJAX).

[0810] Step 6:

[0811] The server receives the question sent by the user, saves the question in a temporary variable, and then passes the question to the emotion engine to analyze the user's emotional state.

[0812] Step 7:

[0813] The emotion engine analyzes the user's emotional state from the text they input. For example, it uses natural language processing to infer emotions such as "confusion" or "irritation."

[0814] Step 8:

[0815] The emotion engine sends the analysis results back to the server, including information suggesting appropriate expressions and tones based on the emotional state.

[0816] Step 9:

[0817] The server requests a question from an artificial intelligence (AI) system with chat generation capabilities based on the information obtained from the emotion engine. For example, the server includes information such as "The user is confused and needs a concise and clear answer."

[0818] Step 10:

[0819] The chat generation AI generates text based on the received question and additional information, generating an appropriate response, which is then sent back to the server in JSON format.

[0820] Step 11:

[0821] The server analyzes the response received from the AI, formats it as needed, and then converts it back into JSON format for sending back to the device.

[0822] Step 12:

[0823] The terminal receives the answer sent back from the server and displays it again in the UI on the web browser. The answer text is immediately visible to the user.

[0824] Step 13:

[0825] Users can view answers displayed on their devices to find solutions or explanations to their questions, and answers are delivered in a tone that reflects their emotional state, improving the user experience.

[0826] In this way, the present invention provides a system that efficiently utilizes past survey data to provide answers to users' questions in real time, and by combining it with an emotion engine, it provides appropriate answers that correspond to the user's emotional state.

[0827] Example 2

[0828] 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."

[0829] Conventional question-answering systems provide answers without considering the user's emotional state, resulting in a poor user experience. They also struggle to efficiently utilize past survey data and provide prompt and accurate responses. This often leaves users frustrated, and they sometimes fail to provide appropriate responses, especially for urgent questions.

[0830] 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.

[0831] In this invention, the server includes means for loading past survey data into a database, means for providing a user interface for a terminal that accepts questions from users, means for transmitting questions entered through the user interface to the server, means for the server to receive the questions and analyze the user's emotional state using an emotion analysis engine, means for generating a prompt sentence for a generative AI model based on the emotional state analyzed by the emotion analysis engine and transmitting the request, means for the server to receive an answer obtained from the generative AI model and return the answer to the terminal, and means for the terminal to display the answer, thereby enabling a quick and appropriate response that takes the user's emotional state into consideration.

[0832] A "server" is a computing device that receives requests from clients and processes them appropriately.

[0833] A "database" is a data storage system that stores past survey data and allows for efficient storage, retrieval, updating, and deletion of data.

[0834] A "user interface" is an interface through which a user interacts with a computer system, and is provided in the form of a web page that includes fields for entering questions and the like.

[0835] An "emotion analysis engine" is software or a system that analyzes text data entered by a user and detects the user's emotional state (e.g., frustration, confusion, joy).

[0836] A "generative AI model" is a model generated by artificial intelligence and used to generate appropriate answers to user questions.

[0837] A "prompt sentence" is a text sentence that provides appropriate background information to the generative AI model, and includes the user's emotional state and the question content.

[0838] A "question" refers to a specific inquiry that a user poses to a computer system, and is data that is processed through a server.

[0839] An "answer" is a response generated by a generative AI model in response to a user's question, and contains appropriate information.

[0840] MODE FOR CARRYING OUT THE INVENTION

[0841] The system according to the present invention has the following hardware and software configuration in order to efficiently utilize past survey data and provide answers to user questions in real time.

[0842] Hardware and software used

[0843] 1. Hardware

[0844] Server (a computer with a high-performance processor and sufficient memory)

[0845] User devices (PCs, tablets, smartphones, etc.)

[0846] 2. Software

[0847] Database Management System (DBMS): MySQL, PostgreSQL, etc.

[0848] Web browser: Google Chrome, Mozilla Firefox, etc.

[0849] Sentiment analysis engine: General natural language processing software (e.g., IBM Watson Tone Analyzer, Google Cloud Natural Language)

[0850] Generative AI model: OpenAI's GPT-3 or a similar model

[0851] Data processing flow

[0852] 1. Starting the server and connecting to the database

[0853] When the server starts, it establishes a connection with a database management system, which stores past survey data, and performs read operations on the database.

[0854] 2. Providing a user interface

[0855] The terminal provides a web browser interface for users to input questions. This interface is composed of HTML, CSS, and JavaScript, and includes a question input field and a submit button.

[0856] 3. User inputs and submits question

[0857] When a user enters a question and presses the submit button, the device parses the question into JSON format and sends it to the server using an HTTP POST request.

[0858] 4. Emotion analysis using an emotion analysis engine

[0859] The server passes the received question to a sentiment analysis engine, which analyzes the question and returns the user's emotional state in JSON format. For example, it can determine whether the user is impatient or confused.

[0860] 5. Prompt generation and request transmission to the generative AI model

[0861] Based on the emotional information obtained from the emotion analysis engine, the server generates a prompt sentence, which is used when sending a request to the generative AI model. An example of a prompt sentence is as follows:

[0862] User Question: "I don't understand what this data point means!"

[0863] Emotion engine analysis results: The user feels impatient

[0864] AI instructions:

[0865] "Users are in a hurry and need short, straightforward answers.

[0866] For example: That data point shows the growth rate.”

[0867] 6. Receiving and displaying generated answers

[0868] The generative AI model generates an appropriate answer based on the prompt sentence. The generated answer is sent back to the server, which then sends it to the user's device. The device receives the answer and displays it on the user interface.

[0869] Specific examples

[0870] For example, if a user types "I don't understand what this data point means!" and presses the submit button, the following process will occur:

[0871] The server passes the question to a sentiment analysis engine to analyze the user's sense of urgency.

[0872] Based on the results obtained from the sentiment analysis engine, the server generates prompt sentences for the generative AI model.

[0873] Based on the prompt sent to the generative AI model, an appropriate answer is generated, such as "That data point indicates a growth rate."

[0874] This answer is sent to the user's terminal via the server, and the user can check the answer on a web browser.

[0875] As described above, the present invention provides a system that recognizes the user's emotional state and utilizes past survey data to quickly and accurately answer the user's questions, thereby improving the user experience and streamlining the survey process.

[0876] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0877] Step 1: Starting the server and connecting to the database

[0878] When the server starts, it establishes a connection to the database management system (DBMS). Specifically, the server attempts to connect using the database connection string, and if successful, loads the past survey data from the database into memory.

[0879] Input: Database connection string

[0880] Output: Database connection established and data loaded successfully

[0881] What it does: The server loads the database connection settings on startup, and once the connection is established, it uses SQL queries to retrieve past survey data and stores it in memory.

[0882] Step 2: Providing a User Interface

[0883] The device provides a user interface (UI) on a web browser, specifically by using HTML, CSS, and JavaScript to display a page containing a question input field and a submit button.

[0884] Input: Access to the terminal

[0885] Output: UI displayed in a web browser

[0886] Specific operation: The device receives the UI file from the HTTP server, renders it, and displays it to the user. It then waits for the user to enter a question.

[0887] Step 3: User enters question and submits

[0888] The user enters a question through the device's UI and presses the submit button. The device parses the question into JSON format and sends it to the server using an HTTP POST request.

[0889] Input: User question

[0890] Output: The question parsed into JSON and sent to the server

[0891] Specific behavior: The user types "I don't understand the meaning of this data point!" and presses the send button. The device converts this to JSON format and sends it to the server as an HTTP POST request.

[0892] Step 4: Sentiment analysis using the sentiment analysis engine

[0893] The server passes the received question to a sentiment analysis engine to analyze the user's emotional state. The sentiment analysis engine analyzes the text data and returns the emotional state to the server in JSON format.

[0894] Input: JSON formatted question

[0895] Output: JSON format data indicating emotional state

[0896] Specific operation: The server sends the received question to the sentiment analysis engine and receives the analysis result (e.g., the user is impatient).

[0897] Step 5: Generate a prompt and send a request to the generative AI model

[0898] The server generates a prompt based on the results of the emotion analysis engine and sends a request to the generative AI model. The prompt includes the user's question and emotional state.

[0899] Input: JSON data representing emotional states

[0900] Output: Prompts and requests sent to the generative AI model

[0901] Specific operation: The server generates a prompt sentence containing the instruction "The user is impatient and needs a concise and straightforward answer" and sends it to the generative AI model.

[0902] Step 6: Receive and view the generated answers

[0903] The generative AI model generates an appropriate response based on the prompt and sends it back to the server, which then sends the response to the user's device, which displays it on the UI.

[0904] Input: Answer from a generative AI model

[0905] Output: Send and display responses to the terminal

[0906] Specific operation: The generative AI model generates an answer such as "That data point indicates a growth rate" and sends it back to the server. The server then sends this answer to the device, which displays it on the UI so that the user can confirm it.

[0907] (Application example 2)

[0908] 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."

[0909] While existing systems provide a way to answer users' questions in real time by utilizing past survey data, they lack a method for responding appropriately based on the user's emotional state. As a result, the user experience is often unsatisfactory, and it is difficult to provide appropriate support, especially when the user is confused or in a hurry. Therefore, there is a need for a system that can analyze the user's emotional state and adjust the tone of the questions accordingly.

[0910] 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.

[0911] In this invention, the server includes means for loading past survey data into a database, means for providing a user interface for a terminal that accepts questions from users, means for transmitting questions entered through the user interface to the server, means for the server to receive the questions and analyze the user's emotional state using an emotion analysis engine, means for the server to send a request to an artificial intelligence (AI) with a chat generation function based on the emotion analysis results, means for the server to receive answers obtained from the AI ​​and return the answers to the terminal, and means for the terminal to display the answers, thereby enabling real-time answers in an optimal tone according to the user's emotional state.

[0912] "Historical Survey Data" means information and statistics previously collected and stored.

[0913] A "database" is a system for organizing and storing information and data.

[0914] A "terminal" is a computer device that is operated by a user.

[0915] A "user interface" refers to the screen and input device that a user uses to operate a system.

[0916] A "server" is a computer system that provides services to other computers and terminals over a network.

[0917] An "emotion analysis engine" is software or algorithms for analyzing a user's emotional state from input data.

[0918] The "chat generation function" is a function that generates appropriate answers to input questions.

[0919] "Artificial intelligence" is a technology that uses computers to achieve human-like intelligence.

[0920] An "answer" is a response or answer to a question.

[0921] The system that realizes this application example is built using the following programs and hardware: The system leverages past survey data to quickly and accurately answer users' questions and can adjust its tone based on the user's emotional state.

[0922] The server first connects to a database (e.g., MySQL) to load past survey data. This database is a system for organizing and storing information and data. Then, a terminal built using React Native to provide the user interface accepts user questions.

[0923] When a user inputs a question and presses the send button, the device sends the question to the server. When the server receives the question, it analyzes the user's emotional state using an emotion analysis engine (e.g., a TensorFlow model). This emotion analysis engine is software or an algorithm for analyzing the user's emotional state from the user's input data.

[0924] Next, the server sends a question to an AI (e.g., OpenAI GPT-4) based on the results of this emotion analysis. The AI, which has chat generation capabilities, has the ability to generate an appropriate answer to the question. The answer obtained from the AI ​​is sent back to the server, which then sends it back to the device.

[0925] The device then displays the received answers to the user through a user interface, allowing the user to receive answers to their questions in real time and in a tone appropriate to their emotional state.

[0926] For example, if a user types, "I'm not sure what to order!", the server uses a TensorFlow model to analyze the user's confusion. It then sends a prompt to OpenAI GPT-4: "The user is confused and needs quick and clear advice. If they're unsure what to order, what would you suggest?" The AI ​​responds by generating a response like, "I see you're having trouble. I recommend the chef's special pasta, which is a popular choice."

[0927] In this way, the system recognizes user emotions and leverages past survey data to provide quick and personalized answers to user questions, improving the user experience and enabling appropriate support for users in areas such as food delivery.

[0928] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0929] Step 1:

[0930] When the server starts, it establishes a database connection. At this time, the server loads past survey data from the database (MySQL). The input is the database connection information, and the output is the loaded past survey data. The information stored in the database is efficiently retrieved and prepared in a form that can be used by the end user.

[0931] Step 2:

[0932] The terminal provides a user interface for the user to input a question. React Native is used to display a question input field and a submit button on the screen. The input is the screen on the device that the user sees and operates, and the output is the question text entered by the user. The terminal screen displays an area for entering the question text and a submit button.

[0933] Step 3:

[0934] When a user enters a question and presses the send button, the terminal sends the question to the server. The input is the question text entered by the user, and the output is the question data to be sent to the server. The terminal sends the question to the server in the form of an HTTP request.

[0935] Step 4:

[0936] The server passes the received question data to a sentiment analysis engine (TensorFlow model). The sentiment analysis engine analyzes the question text and estimates the user's emotional state. The input is the question data, and the output is the user's emotional state (e.g., confused, urgent). The sentiment analysis engine extracts features from the text data and estimates the user's emotion.

[0937] Step 5:

[0938] Based on the emotional state returned by the emotion analysis engine, the server sends a prompt to an artificial intelligence (OpenAI GPT-4) with chat generation capabilities. The prompt contains information about the user's emotional state. The input is the user's emotional state and question data, and the output is a prompt to the AI. The server generates a specific prompt taking the emotional state into consideration and sends it to the AI.

[0939] Step 6:

[0940] The artificial intelligence (OpenAI GPT-4) generates an appropriate answer based on the received prompt. The input is the prompt, and the output is the generated answer text. The artificial intelligence processes the data according to the prompt and generates an appropriate answer.

[0941] Step 7:

[0942] The server receives the answer text obtained from the AI ​​and sends it back to the terminal. The input is the answer data from the AI, and the output is the answer data sent to the terminal. The server receives the AI's answer, converts the format as needed, and sends it to the terminal.

[0943] Step 8:

[0944] The terminal displays the received answer text on the user interface. The input is the answer data from the server, and the output is the display content on the user interface. The answer text is displayed on the terminal screen in a format that the user can easily understand.

[0945] This allows users to get answers to their questions in real time and receive support in a tone that matches their emotional state.

[0946] 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.

[0947] 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.

[0948] 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.

[0949] [Fourth embodiment]

[0950] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0951] 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.

[0952] 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).

[0953] 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.

[0954] 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.

[0955] 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).

[0956] 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.

[0957] 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.

[0958] 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.

[0959] 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.

[0960] 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.

[0961] 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.

[0962] 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."

[0963] The system provided by the present invention begins by loading past survey data into a database. This allows the data to be quickly referenced as needed, allowing users to efficiently use the survey data. Next, the terminal provides the user with a user interface (UI) for entering questions. This UI is configured as a web form including a question input field and a submit button.

[0964] When a user enters a question and presses the send button, the question is sent from the device to the server. The server receives the question and requests the question content from an artificial intelligence (AI) with chat generation functionality. Specifically, the server sends the question in text format to the AI ​​and receives a response from the AI. This AI has natural language processing capabilities and is equipped with a model for generating appropriate responses to user questions.

[0965] The server sends the answer received from the AI ​​back to the device, which then displays the answer to the user. This allows the user to get answers to their questions in real time. Data exchange between the server, device, and user is carried out via network communication, and encrypted communication is used as a security measure.

[0966] Specific examples

[0967] For example, if a user is working on a research project and has a question about a particular data point, such as "What does this data point mean?", they can enter "What does this data point mean?" into a web form on their device. They press submit, which sends the question to the server. The server then sends the question to an AI, which generates an answer to the question. The answer might be something like "This data point indicates a particular trend." The server receives this answer and sends it back to the device, which then displays it to the user, providing instant information.

[0968] This system allows users to refer to past survey data and obtain answers to questions in real time, greatly improving the efficiency of survey work. In addition, AI-based answers are fast and accurate, reducing the burden on users and improving the quality of surveys. This system can be applied to survey work in a variety of fields, providing great convenience.

[0969] The processing flow will be explained below.

[0970] Step 1:

[0971] When the server starts up, it establishes a database connection, which is a storage for past survey data.

[0972] Step 2:

[0973] The server opens the past survey data file and stores each line in the database. At this time, it performs error handling and checks the log to confirm that the data was loaded successfully.

[0974] Step 3:

[0975] The terminal provides a user interface (UI) on a web browser that allows the user to enter a question. The UI is configured as a web form that includes a question input field and a submit button.

[0976] Step 4:

[0977] The user enters a question into a web form on the device and presses the submit button, for example, entering a specific question such as "What does this data point mean?"

[0978] Step 5:

[0979] The device catches the form submission event, converts the question into JSON format using JavaScript, and then sends the question data to the server via asynchronous communication (AJAX).

[0980] Step 6:

[0981] The server receives the question sent by the user, saves the question in a temporary variable, and then constructs data to request the question from an artificial intelligence (AI) with chat generation capabilities.

[0982] Step 7:

[0983] The server issues an API request to send the question to the AI, which includes the question text as well as parameters such as the maximum number of tokens.

[0984] Step 8:

[0985] The AI ​​generates text based on the received question and creates an appropriate answer, which is then sent back to the server in JSON format.

[0986] Step 9:

[0987] The server analyzes the response received from the AI, formats it as needed, and then converts it back into JSON format for sending back to the device.

[0988] Step 10:

[0989] The terminal receives the answer sent back from the server and displays it again on the UI in the web browser, where the answer text is immediately visible to the user.

[0990] Step 11:

[0991] Users can check the answers displayed on their devices and get solutions and explanations for any questions they have, which allows them to carry out their research more efficiently.

[0992] Example 1

[0993] 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."

[0994] There is a need for a system that can efficiently use past survey data and provide quick and accurate answers to user questions. Furthermore, there is a need for a system that can obtain answers to user questions in real time while ensuring communication security. With conventional methods, referencing data and answering questions is time-consuming, resulting in a decrease in work efficiency.

[0995] 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.

[0996] In this invention, the server includes means for loading past survey data into a database, means for providing a user interface for a terminal that accepts questions from users, means for transmitting questions entered through the user interface to the server, means for transmitting requests to the generative AI model, means for receiving answers obtained from the generative AI model and returning the answers to the terminal, means for the terminal to display the answers, and means for ensuring security by encrypting communication between the server and the terminal. This allows users to efficiently use past survey data while obtaining accurate answers in real time.

[0997] A "database" is a specific structure or system for efficiently storing, managing, and retrieving data.

[0998] A "server" is a computer system that processes requests from clients via a network and provides services and data.

[0999] "Terminal" means a computer system or device capable of input and output used by a User.

[1000] A "user interface" is the means or screen configuration through which a user interacts with a system or application.

[1001] "Artificial intelligence" is a technology that allows machines to mimic human intelligent activity and have the ability to understand, learn, and respond.

[1002] A "generative AI model" is a type of artificial intelligence that uses pre-trained neural networks to generate and understand natural language at a near-human level.

[1003] A "web page" is a format for displaying documents and content on the Internet that can be accessed through a web browser.

[1004] "Encrypted communication" is a communication method that uses encryption technology to protect information from third parties when sending and receiving data.

[1005] The system provided by this invention starts by loading past survey data into a database. The server is configured to efficiently manage this data and enable quick reference as needed. It is preferable to use a database management system such as MySQL or PostgreSQL.

[1006] Next, the terminal provides the user with a user interface (UI) for entering questions. This UI is implemented as a web page built using HTML, CSS, and JavaScript. The UI includes a field for entering questions and a submit button, and is designed to be intuitive for users to use.

[1007] When a user inputs a question and presses the send button, the question is sent from the terminal to the server. This communication is performed asynchronously using the AJAX function, and the question is transmitted to the server via the HTTP / HTTPS protocol.

[1008] When the server receives a question, it sends a request to a generative AI model, such as OpenAI's GPT-4. The server sends the question to the AI ​​model in text format, and the AI ​​model generates an answer appropriate to the question.

[1009] The server then sends the answer received from the generative AI model back to the device, where it is displayed to the user in the device's browser using JavaScript, allowing the user to receive feedback in real time.

[1010] In addition, data exchange between the server and the device is performed using encrypted communication (SSL / TLS), ensuring security. This encryption reduces the risk of data eavesdropping or tampering.

[1011] Specific examples

[1012] For example, say a user has a question about a particular data point. They enter "What does this data point mean?" into a web form on their device. They press submit, sending the question to a server. The server sends the question to a generative AI model, which generates an answer such as "This data point indicates a particular trend." The server receives this answer and sends it back to the device, which displays it to the user, who receives the information instantly.

[1013] Prompt Sentence Examples

[1014] "What does this data point mean?"

[1015] By using this system, users can efficiently refer to past survey data and obtain accurate answers to questions in real time, significantly improving the efficiency of survey work and reducing the burden on users.

[1016] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1017] Step 1:

[1018] The server loads past survey data into a database. Specifically, the server uses a database management system (e.g., MySQL or PostgreSQL) to import data from a data source (e.g., a CSV file or Excel file). The input is a survey data file, which is then saved in the database and used as output. When storing the data in the database, the data is reshaped and formatted.

[1019] Step 2:

[1020] The terminal provides a user interface (UI). This UI is built using HTML, CSS, and JavaScript and displays a web page containing a question input field and a submit button. The user can enter a question in the input field and proceed to the next step by clicking the submit button. The input is the web browser accessed by the user, and the output is the displayed question input interface.

[1021] Step 3:

[1022] The user enters a question and presses the submit button. The input is the question the user enters in the input field, and the output is the question sent from the device to the server. The device uses JavaScript's AJAX function to asynchronously send the user's question to the server as a POST request.

[1023] Step 4:

[1024] The server receives the user's question and sends a request to the generative AI model. The input is the question received by the server, and the output is a prompt to be sent to the generative AI model. The server uses a Python library, for example, to send an API request to the AI ​​model (e.g., GPT-4).

[1025] Step 5:

[1026] The generative AI model receives a request from the server and generates an answer to the question. Here, the input is the prompt sent from the server, and the output is the text of the answer generated by the AI ​​model. The generative AI model uses a pre-trained neural network and performs natural language processing to generate an appropriate answer.

[1027] Step 6:

[1028] The server receives the answer from the generative AI model and sends it back to the device. The input is the text of the answer received from the generative AI model, and the output is an HTTP response to be sent to the device. The server sends this data back to the device using secure communication (e.g., HTTPS).

[1029] Step 7:

[1030] The terminal displays the answer received from the server in a user interface. The input is the text of the answer received from the server, and the output is the answer displayed to the user. The terminal uses JavaScript to display the received answer in a designated area on the web page.

[1031] Step 8:

[1032] Communication between the server and the terminal is always encrypted. The server sets up an SSL / TLS certificate and implements secure HTTPS communication. This encrypts the communication content and reduces the risk of data eavesdropping or tampering. The input is the entire data to be communicated, and the output is the encrypted data sent and received over the network.

[1033] (Application example 1)

[1034] 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."

[1035] In manufacturing processes within factories, it is difficult to grasp information about work progress and equipment status in real time, which often leads to delayed response to problems. Conventional methods require time to analyze manufacturing data and obtain results, which hinders efficient production activities. In addition, specialized engineers must always be on-site, which increases operational costs. This makes it difficult to improve overall productivity.

[1036] 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.

[1037] In this invention, the server includes means for loading past data into a database, means for providing a terminal interface for accepting questions from users, means for transmitting questions entered through the interface to the server, means for the server to receive the questions and send requests to an intelligence with natural language processing capabilities, means for the server to receive answers from the intelligence and return the answers to the terminal, means for the terminal to display the answers, and industrial equipment with a function for accepting questions about the status of equipment and work progress in manufacturing operations and providing answers in real time. This makes it possible to immediately grasp information such as work progress and equipment failures within the factory, enabling efficient and prompt troubleshooting.

[1038] "Historical data" refers to previously collected information and records related to the manufacturing process.

[1039] A "database" refers to a structured collection of data for efficient management and retrieval of data.

[1040] "Interface" refers to the component of a system that provides the means for users to enter questions and obtain information.

[1041] "Accepting a question" refers to the system recognizing and processing an inquiry from a user.

[1042] A "server" refers to a computer that provides services to users on a network.

[1043] "Natural language processing" refers to technology for understanding and processing human language.

[1044] "Intelligence" refers to systems that use generative AI models and machine learning algorithms to generate answers to user questions.

[1045] "Sending a request" means sending a request to another system for a particular action or piece of information.

[1046] "Industrial equipment" refers to the entire machinery, equipment, and systems used in manufacturing.

[1047] "Equipment status" refers to the current operating status and operating conditions of each piece of equipment and machine in the factory.

[1048] "Work progress" refers to the current progress in the manufacturing process.

[1049] "Storage" refers to hardware or devices for storing digital data.

[1050] "Web page" means a document containing information or an interface that can be accessed over the Internet.

[1051] To implement the present invention, the following system configuration and program are required: This system loads past manufacturing data into a database, and when a user inputs a question through a terminal interface, it uses a generative AI model to provide an answer in real time.

[1052] System configuration

[1053] 1. Database:

[1054] This database stores past manufacturing data and is constructed to enable efficient search and management. SQLite3 is used as a specific example of the database.

[1055] 2. User Interface (Interface):

[1056] This is the terminal interface for users to enter and submit questions. It consists of a web page with a question input field and a submit button.

[1057] 3. Server:

[1058] This server receives questions from users and sends requests to an AI model with natural language processing capabilities (generative AI model). The OpenAI API can be used as the AI ​​model.

[1059] 4. Intelligence (generative AI models):

[1060] It uses technology for understanding and processing human language to generate answers to user questions. This intelligence uses OpenAI's GPT-3 as its generative AI model.

[1061] System processing flow

[1062] 1. Data Acquisition:

[1063] The server first searches the database based on the input query and retrieves relevant past manufacturing data.

[1064] 2. Answer generation from AI models:

[1065] If there is no relevant data in the database, the server will send the user's question to the generative AI model to generate an appropriate answer. The prompt for the question will be sent in the following format:

[1066] "Generate appropriate answers based on user questions: [question]"

[1067] 3. Returning the response:

[1068] The server receives the answer from the generative AI model and sends it back to the device.

[1069] 4. Show Answers:

[1070] The terminal displays the received answers to the user, allowing the user to obtain answers to their questions in real time.

[1071] Specific examples of hardware and software used

[1072] Database server: SQLite3

[1073] AI model: OpenAI GPT-3

[1074] Network communication: Ensure security by using encrypted communication (e.g., TLS / SSL).

[1075] Specific examples

[1076] For example, a user working on a production line might type into the interface, "What does error code 45 on this machine mean?" This question is sent to the server, which first searches the database. If no matching data is found, the server sends the question to a generative AI model, as follows:

[1077] "Generate an appropriate answer based on the user's question: What does error code 45 mean on this device?"

[1078] Upon receiving an answer from the generative AI model (e.g., "Error code 45 indicates a faulty temperature sensor"), the server sends it back to the device, which immediately displays the answer to the user.

[1079] This system makes it possible to grasp information on work progress and equipment status within the factory in real time, enabling efficient and rapid response to problems.

[1080] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1081] Step 1:

[1082] The user enters a question through the terminal interface and presses the send button. The input is a textual question, for example, "What does error code 45 on this device mean?" The output is the question data that is sent to the server.

[1083] Step 2:

[1084] The server receives questions sent by users. The input is the textual question data from the user, and the output is to store the received question data and pass it on to the next process.

[1085] Step 3:

[1086] The server searches the database based on the received question data. The server uses SQLite3 to search for data points related to the question. The input is the received question data, and the output is the data points as search results. If the corresponding data exists in the database, the data points are retrieved.

[1087] Step 4:

[1088] Validates the results of a database search. The input is the database search results, and the output is a determination of whether or not data exists. If relevant data is found, that information is used to generate an answer for the user.

[1089] Step 5:

[1090] If there is no relevant data in the database, the server sends the question to the generative AI model. The question is sent using the GPT-3 API, and the AI ​​model generates an answer. The input is a prompt containing the user's question: "Generate an appropriate answer based on the user's question: [question]", and the output is a text answer from the AI ​​model.

[1091] Step 6:

[1092] The server receives the answer obtained from the generative AI model. The input is the text-format answer data from the AI ​​model, and the output is to store the received answer data and pass it on to the next process.

[1093] Step 7:

[1094] The server returns the received response to the terminal. The input is the saved response data, and the output is the response data sent to the terminal.

[1095] Step 8:

[1096] The terminal displays the answer received from the server to the user. The input is the text-format answer data sent from the server, and the output is the answer displayed on the user interface. The user can check this and obtain information in real time.

[1097] Specific actions

[1098] For example, a user enters a question such as "What does error code 45 on this device indicate?" and presses the send button. The question is sent to the server, which searches the database. If the data does not exist, the server sends the question as a prompt to the generative AI model, and receives the answer from the AI: "Error code 45 indicates a faulty temperature sensor." This answer is sent back to the device, which displays it to the user.

[1099] 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.

[1100] The system of the present invention has the following features to efficiently utilize past survey data and provide real-time answers to user questions: It also incorporates an emotion engine to recognize user emotions, improving the user experience.

[1101] When the server starts up, it first establishes a database connection and loads past survey data into the database. The database is a storage device for saving and searching data, and stores past survey data.

[1102] Next, the terminal provides a user interface (UI) for the user to input a question. This UI is displayed on a web browser and is configured as a web form including a question input field and a submit button.

[1103] When a user inputs a question and presses the send button, the device sends the question to the server. The server receives the question and passes it to the emotion engine. The emotion engine analyzes the user's emotional state from the input and determines, for example, whether the user is confused or in a hurry.

[1104] The emotion engine suggests appropriate wording and tone for the question based on the user's emotional state and returns this information to the server. The server then uses this information to request a question from an artificial intelligence (AI) with chat generation capabilities. The AI ​​receives the question, generates an appropriate answer, and returns it to the server.

[1105] The server sends the AI's answers back to the device, which then displays them to the user, allowing the user to get answers to their questions in real time, with the answers delivered in a tone that matches the user's emotional state.

[1106] Specific examples

[1107] For example, if a user enters something in a hurry, such as "I don't understand the meaning of this data point!", the emotion engine will sense the user's impatience. Based on this information, the server will add additional information to the question it sends to the AI, such as "The user is in a hurry, so a concise, straightforward answer is required." The AI ​​will then take this additional information into account and generate a specific, quick answer, such as "That data point shows the growth rate." The server will then send this answer back to the device, which will display it to the user. As a result, the user can obtain appropriate information in real time and receive an answer that is in line with their emotional state.

[1108] In this way, the present invention provides a system that recognizes user emotions and utilizes past survey data to quickly and accurately answer user questions, improving the user experience and streamlining the survey process.

[1109] The processing flow will be explained below.

[1110] Step 1:

[1111] The server establishes a database connection on startup, which is a storage for past survey data, allowing it to be quickly referenced when needed.

[1112] Step 2:

[1113] The server opens the past survey data file and stores it line by line in the database. For example, it reads a CSV file, parses each line, and inserts it into the database. It also handles errors and verifies that the data was loaded correctly.

[1114] Step 3:

[1115] The terminal provides a user interface (UI) that allows the user to enter a question. This UI is displayed in a web browser and is structured as a web form that includes a question input field and a submit button.

[1116] Step 4:

[1117] The user enters a question into a web form on the device and presses the submit button, for example, "What does this data point mean?"

[1118] Step 5:

[1119] The device catches the form submission event, converts the question into JSON format using JavaScript, and then sends the question data to the server via asynchronous communication (AJAX).

[1120] Step 6:

[1121] The server receives the question sent by the user, saves the question in a temporary variable, and then passes the question to the emotion engine to analyze the user's emotional state.

[1122] Step 7:

[1123] The emotion engine analyzes the user's emotional state from the text they input. For example, it uses natural language processing to infer emotions such as "confusion" or "irritation."

[1124] Step 8:

[1125] The emotion engine sends the analysis results back to the server, including information suggesting appropriate expressions and tones based on the emotional state.

[1126] Step 9:

[1127] The server requests a question from an artificial intelligence (AI) system with chat generation capabilities based on the information obtained from the emotion engine. For example, the server includes information such as "The user is confused and needs a concise and clear answer."

[1128] Step 10:

[1129] The chat generation AI generates text based on the received question and additional information, generating an appropriate response, which is then sent back to the server in JSON format.

[1130] Step 11:

[1131] The server analyzes the response received from the AI, formats it as needed, and then converts it back into JSON format for sending back to the device.

[1132] Step 12:

[1133] The terminal receives the answer sent back from the server and displays it again in the UI on the web browser. The answer text is immediately visible to the user.

[1134] Step 13:

[1135] Users can view answers displayed on their devices to find solutions or explanations to their questions, and answers are delivered in a tone that reflects their emotional state, improving the user experience.

[1136] In this way, the present invention provides a system that efficiently utilizes past survey data to provide answers to users' questions in real time, and by combining it with an emotion engine, it provides appropriate answers that correspond to the user's emotional state.

[1137] Example 2

[1138] 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."

[1139] Conventional question-answering systems provide answers without considering the user's emotional state, resulting in a poor user experience. They also struggle to efficiently utilize past survey data and provide prompt and accurate responses. This often leaves users frustrated, and they sometimes fail to provide appropriate responses, especially for urgent questions.

[1140] 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.

[1141] In this invention, the server includes means for loading past survey data into a database, means for providing a user interface for a terminal that accepts questions from users, means for transmitting questions entered through the user interface to the server, means for the server to receive the questions and analyze the user's emotional state using an emotion analysis engine, means for generating a prompt sentence for a generative AI model based on the emotional state analyzed by the emotion analysis engine and transmitting the request, means for the server to receive an answer obtained from the generative AI model and return the answer to the terminal, and means for the terminal to display the answer, thereby enabling a quick and appropriate response that takes the user's emotional state into consideration.

[1142] A "server" is a computing device that receives requests from clients and processes them appropriately.

[1143] A "database" is a data storage system that stores past survey data and allows for efficient storage, retrieval, updating, and deletion of data.

[1144] A "user interface" is an interface through which a user interacts with a computer system, and is provided in the form of a web page that includes fields for entering questions and the like.

[1145] An "emotion analysis engine" is software or a system that analyzes text data entered by a user and detects the user's emotional state (e.g., frustration, confusion, joy).

[1146] A "generative AI model" is a model generated by artificial intelligence and used to generate appropriate answers to user questions.

[1147] A "prompt sentence" is a text sentence that provides appropriate background information to the generative AI model, and includes the user's emotional state and the question content.

[1148] A "question" refers to a specific inquiry that a user poses to a computer system, and is data that is processed through a server.

[1149] An "answer" is a response generated by a generative AI model in response to a user's question, and contains appropriate information.

[1150] MODE FOR CARRYING OUT THE INVENTION

[1151] The system according to the present invention has the following hardware and software configuration in order to efficiently utilize past survey data and provide answers to user questions in real time.

[1152] Hardware and software used

[1153] 1. Hardware

[1154] Server (a computer with a high-performance processor and sufficient memory)

[1155] User devices (PCs, tablets, smartphones, etc.)

[1156] 2. Software

[1157] Database Management System (DBMS): MySQL, PostgreSQL, etc.

[1158] Web browser: Google Chrome, Mozilla Firefox, etc.

[1159] Sentiment analysis engine: General natural language processing software (e.g., IBM Watson Tone Analyzer, Google Cloud Natural Language)

[1160] Generative AI model: OpenAI's GPT-3 or a similar model

[1161] Data processing flow

[1162] 1. Starting the server and connecting to the database

[1163] When the server starts, it establishes a connection with a database management system, which stores past survey data, and performs read operations on the database.

[1164] 2. Providing a user interface

[1165] The terminal provides a web browser interface for users to input questions. This interface is composed of HTML, CSS, and JavaScript, and includes a question input field and a submit button.

[1166] 3. User inputs and submits question

[1167] When a user enters a question and presses the submit button, the device parses the question into JSON format and sends it to the server using an HTTP POST request.

[1168] 4. Emotion analysis using an emotion analysis engine

[1169] The server passes the received question to a sentiment analysis engine, which analyzes the question and returns the user's emotional state in JSON format. For example, it can determine whether the user is impatient or confused.

[1170] 5. Prompt generation and request transmission to the generative AI model

[1171] Based on the emotional information obtained from the emotion analysis engine, the server generates a prompt sentence, which is used when sending a request to the generative AI model. An example of a prompt sentence is as follows:

[1172] User Question: "I don't understand what this data point means!"

[1173] Emotion engine analysis results: The user feels impatient

[1174] AI instructions:

[1175] "Users are in a hurry and need short, straightforward answers.

[1176] For example: That data point shows the growth rate.”

[1177] 6. Receiving and displaying generated answers

[1178] The generative AI model generates an appropriate answer based on the prompt sentence. The generated answer is sent back to the server, which then sends it to the user's device. The device receives the answer and displays it on the user interface.

[1179] Specific examples

[1180] For example, if a user types "I don't understand what this data point means!" and presses the submit button, the following process will occur:

[1181] The server passes the question to a sentiment analysis engine to analyze the user's sense of urgency.

[1182] Based on the results obtained from the sentiment analysis engine, the server generates prompt sentences for the generative AI model.

[1183] Based on the prompt sent to the generative AI model, an appropriate answer is generated, such as "That data point indicates a growth rate."

[1184] This answer is sent to the user's terminal via the server, and the user can check the answer on a web browser.

[1185] As described above, the present invention provides a system that recognizes the user's emotional state and utilizes past survey data to quickly and accurately answer the user's questions, thereby improving the user experience and streamlining the survey process.

[1186] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1187] Step 1: Starting the server and connecting to the database

[1188] When the server starts, it establishes a connection to the database management system (DBMS). Specifically, the server attempts to connect using the database connection string, and if successful, loads the past survey data from the database into memory.

[1189] Input: Database connection string

[1190] Output: Database connection established and data loaded successfully

[1191] What it does: The server loads the database connection settings on startup, and once the connection is established, it uses SQL queries to retrieve past survey data and stores it in memory.

[1192] Step 2: Providing a User Interface

[1193] The device provides a user interface (UI) on a web browser, specifically by using HTML, CSS, and JavaScript to display a page containing a question input field and a submit button.

[1194] Input: Access to the terminal

[1195] Output: UI displayed in a web browser

[1196] Specific operation: The device receives the UI file from the HTTP server, renders it, and displays it to the user. It then waits for the user to enter a question.

[1197] Step 3: User enters question and submits

[1198] The user enters a question through the device's UI and presses the submit button. The device parses the question into JSON format and sends it to the server using an HTTP POST request.

[1199] Input: User question

[1200] Output: The question parsed into JSON and sent to the server

[1201] Specific behavior: The user types "I don't understand the meaning of this data point!" and presses the send button. The device converts this to JSON format and sends it to the server as an HTTP POST request.

[1202] Step 4: Sentiment analysis using the sentiment analysis engine

[1203] The server passes the received question to a sentiment analysis engine to analyze the user's emotional state. The sentiment analysis engine analyzes the text data and returns the emotional state to the server in JSON format.

[1204] Input: JSON formatted question

[1205] Output: JSON format data indicating emotional state

[1206] Specific operation: The server sends the received question to the sentiment analysis engine and receives the analysis result (e.g., the user is impatient).

[1207] Step 5: Generate a prompt and send a request to the generative AI model

[1208] The server generates a prompt based on the results of the emotion analysis engine and sends a request to the generative AI model. The prompt includes the user's question and emotional state.

[1209] Input: JSON data representing emotional states

[1210] Output: Prompts and requests sent to the generative AI model

[1211] Specific operation: The server generates a prompt sentence containing the instruction "The user is impatient and needs a concise and straightforward answer" and sends it to the generative AI model.

[1212] Step 6: Receive and view the generated answers

[1213] The generative AI model generates an appropriate response based on the prompt and sends it back to the server, which then sends the response to the user's device, which displays it on the UI.

[1214] Input: Answer from a generative AI model

[1215] Output: Send and display responses to the terminal

[1216] Specific operation: The generative AI model generates an answer such as "That data point indicates a growth rate" and sends it back to the server. The server then sends this answer to the device, which displays it on the UI so that the user can confirm it.

[1217] (Application example 2)

[1218] 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."

[1219] While existing systems provide a way to answer users' questions in real time by utilizing past survey data, they lack a method for responding appropriately based on the user's emotional state. As a result, the user experience is often unsatisfactory, and it is difficult to provide appropriate support, especially when the user is confused or in a hurry. Therefore, there is a need for a system that can analyze the user's emotional state and adjust the tone of the questions accordingly.

[1220] 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.

[1221] In this invention, the server includes means for loading past survey data into a database, means for providing a user interface for a terminal that accepts questions from users, means for transmitting questions entered through the user interface to the server, means for the server to receive the questions and analyze the user's emotional state using an emotion analysis engine, means for the server to send a request to an artificial intelligence (AI) with a chat generation function based on the emotion analysis results, means for the server to receive answers obtained from the AI ​​and return the answers to the terminal, and means for the terminal to display the answers, thereby enabling real-time answers in an optimal tone according to the user's emotional state.

[1222] "Historical Survey Data" means information and statistics previously collected and stored.

[1223] A "database" is a system for organizing and storing information and data.

[1224] A "terminal" is a computer device that is operated by a user.

[1225] A "user interface" refers to the screen and input device that a user uses to operate a system.

[1226] A "server" is a computer system that provides services to other computers and terminals over a network.

[1227] An "emotion analysis engine" is software or algorithms for analyzing a user's emotional state from input data.

[1228] The "chat generation function" is a function that generates appropriate answers to input questions.

[1229] "Artificial intelligence" is a technology that uses computers to achieve human-like intelligence.

[1230] An "answer" is a response or answer to a question.

[1231] The system that realizes this application example is built using the following programs and hardware: The system leverages past survey data to quickly and accurately answer users' questions and can adjust its tone based on the user's emotional state.

[1232] The server first connects to a database (e.g., MySQL) to load past survey data. This database is a system for organizing and storing information and data. Then, a terminal built using React Native to provide the user interface accepts user questions.

[1233] When a user inputs a question and presses the send button, the device sends the question to the server. When the server receives the question, it analyzes the user's emotional state using an emotion analysis engine (e.g., a TensorFlow model). This emotion analysis engine is software or an algorithm for analyzing the user's emotional state from the user's input data.

[1234] Next, the server sends a question to an AI (e.g., OpenAI GPT-4) based on the results of this emotion analysis. The AI, which has chat generation capabilities, has the ability to generate an appropriate answer to the question. The answer obtained from the AI ​​is sent back to the server, which then sends it back to the device.

[1235] The device then displays the received answers to the user through a user interface, allowing the user to receive answers to their questions in real time and in a tone appropriate to their emotional state.

[1236] For example, if a user types, "I'm not sure what to order!", the server uses a TensorFlow model to analyze the user's confusion. It then sends a prompt to OpenAI GPT-4: "The user is confused and needs quick and clear advice. If they're unsure what to order, what would you suggest?" The AI ​​responds by generating a response like, "I see you're having trouble. I recommend the chef's special pasta, which is a popular choice."

[1237] In this way, the system recognizes user emotions and leverages past survey data to provide quick and personalized answers to user questions, improving the user experience and enabling appropriate support for users in areas such as food delivery.

[1238] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1239] Step 1:

[1240] When the server starts, it establishes a database connection. At this time, the server loads past survey data from the database (MySQL). The input is the database connection information, and the output is the loaded past survey data. The information stored in the database is efficiently retrieved and prepared in a form that can be used by the end user.

[1241] Step 2:

[1242] The terminal provides a user interface for the user to input a question. React Native is used to display a question input field and a submit button on the screen. The input is the screen on the device that the user sees and operates, and the output is the question text entered by the user. The terminal screen displays an area for entering the question text and a submit button.

[1243] Step 3:

[1244] When a user enters a question and presses the send button, the terminal sends the question to the server. The input is the question text entered by the user, and the output is the question data to be sent to the server. The terminal sends the question to the server in the form of an HTTP request.

[1245] Step 4:

[1246] The server passes the received question data to a sentiment analysis engine (TensorFlow model). The sentiment analysis engine analyzes the question text and estimates the user's emotional state. The input is the question data, and the output is the user's emotional state (e.g., confused, urgent). The sentiment analysis engine extracts features from the text data and estimates the user's emotion.

[1247] Step 5:

[1248] Based on the emotional state returned by the emotion analysis engine, the server sends a prompt to an artificial intelligence (OpenAI GPT-4) with chat generation capabilities. The prompt contains information about the user's emotional state. The input is the user's emotional state and question data, and the output is a prompt to the AI. The server generates a specific prompt taking the emotional state into consideration and sends it to the AI.

[1249] Step 6:

[1250] The artificial intelligence (OpenAI GPT-4) generates an appropriate answer based on the received prompt. The input is the prompt, and the output is the generated answer text. The artificial intelligence processes the data according to the prompt and generates an appropriate answer.

[1251] Step 7:

[1252] The server receives the answer text obtained from the AI ​​and sends it back to the terminal. The input is the answer data from the AI, and the output is the answer data sent to the terminal. The server receives the AI's answer, converts the format as needed, and sends it to the terminal.

[1253] Step 8:

[1254] The terminal displays the received answer text on the user interface. The input is the answer data from the server, and the output is the display content on the user interface. The answer text is displayed on the terminal screen in a format that the user can easily understand.

[1255] This allows users to get answers to their questions in real time and receive support in a tone that matches their emotional state.

[1256] 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.

[1257] 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.

[1258] 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.

[1259] 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.

[1260] 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.

[1261] 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.

[1262] 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).

[1263] 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.

[1264] 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."

[1265] 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.

[1266] 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).

[1267] 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.

[1268] 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.

[1269] 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.

[1270] 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.

[1271] 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.

[1272] 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.

[1273] 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.

[1274] 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.

[1275] 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.

[1276] 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.

[1277] The following is further disclosed regarding the above embodiment.

[1278] (Claim 1)

[1279] A means of loading past survey data into the database;

[1280] means for providing a user interface for the terminal for accepting questions from a user;

[1281] means for transmitting questions entered through the user interface to a server;

[1282] A means for the server to receive the question and send a request to an artificial intelligence having a chat generation function;

[1283] A means for the server to receive a response obtained from the artificial intelligence and return the response to the terminal;

[1284] The system includes a terminal including means for displaying the answer.

[1285] (Claim 2)

[1286] 10. The system of claim 1, wherein the database comprises data storage for storing past survey data.

[1287] (Claim 3)

[1288] 10. The system of claim 1, wherein the user interface comprises a web page including a question input field and a submit button.

[1289] "Example 1"

[1290] (Claim 1)

[1291] A means of loading past survey data into the database;

[1292] means for providing a user interface for the terminal for accepting questions from a user;

[1293] means for transmitting questions entered through the user interface to a server;

[1294] means for the server to receive the question and send a request to a generative AI model;

[1295] A means for the server to receive an answer obtained from the generative AI model and return the answer to the terminal;

[1296] means for displaying the answer on the terminal;

[1297] The system includes a means for ensuring security by encrypting communication between the server and the terminal.

[1298] (Claim 2)

[1299] 10. The system of claim 1, wherein the database comprises data storage for storing past survey data.

[1300] (Claim 3)

[1301] 10. The system of claim 1, wherein the user interface comprises a web page including a question input field and a submit button.

[1302] "Application Example 1"

[1303] (Claim 1)

[1304] A means of loading historical data into the database;

[1305] means for providing a terminal interface for accepting queries from a user;

[1306] means for transmitting a question input through said interface to a server;

[1307] a means for the server to receive the question and send a request to an intelligence having a natural language processing function;

[1308] means for the server to receive a response from the intelligence and return the response to the terminal;

[1309] means for displaying the answer on the terminal;

[1310] A system that includes industrial equipment with the ability to accept questions about the status of equipment and work progress in manufacturing operations and provide answers in real time.

[1311] (Claim 2)

[1312] 10. The system of claim 1, wherein the database comprises storage for storing historical manufacturing data.

[1313] (Claim 3)

[1314] 10. The system of claim 1, wherein the interface comprises a web page including a question input field and a submit button.

[1315] "Example 2: Combining Emotion Engines"

[1316] (Claim 1)

[1317] A means of loading past survey data into the database;

[1318] means for providing a user interface for the terminal for accepting questions from a user;

[1319] means for transmitting questions entered through the user interface to a server;

[1320] means for receiving the question from the server and analyzing the user's emotional state using an emotion analysis engine;

[1321] A means for generating a prompt sentence for a generative AI model based on the emotional state analyzed by the emotion analysis engine and sending a request;

[1322] A means for the server to receive an answer obtained from the generative AI model and return the answer to the terminal;

[1323] The system includes a terminal including means for displaying the answer.

[1324] (Claim 2)

[1325] 10. The system of claim 1, wherein the database comprises data storage for storing past survey data.

[1326] (Claim 3)

[1327] 10. The system of claim 1, wherein the user interface comprises a web page including a question input field and a submit button.

[1328] "Application example 2 when combining emotion engines"

[1329] (Claim 1)

[1330] A means of loading past survey data into the database;

[1331] means for providing a user interface for the terminal for accepting questions from a user;

[1332] means for transmitting questions entered through the user interface to a server;

[1333] means for receiving the question from the server and analyzing the user's emotional state using an emotion analysis engine;

[1334] A means for the server to send a request to an artificial intelligence having a chat generation function based on the emotion analysis result;

[1335] A means for the server to receive a response obtained from the artificial intelligence and return the response to the terminal;

[1336] The system includes a terminal including means for displaying the answer.

[1337] (Claim 2)

[1338] 10. The system of claim 1, wherein the database comprises data storage for storing past survey data.

[1339] (Claim 3)

[1340] 10. The system of claim 1, wherein the user interface comprises a web page including a question input field and a submit button. [Explanation of symbols]

[1341] 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 loading past survey data into the database; means for providing a user interface for the terminal for accepting questions from a user; means for transmitting questions entered through the user interface to a server; A means for the server to receive the question and send a request to an artificial intelligence having a chat generation function; A means for the server to receive a response obtained from the artificial intelligence and return the response to the terminal; The system includes a terminal including means for displaying the answer.

2. 10. The system of claim 1, wherein the database comprises data storage for storing past survey data.

3. 2. The system of claim 1, wherein the user interface comprises a web page including a question input field and a submit button.

Images