system

Abstract

Provide a system. 【Solution means】 Input means for the user to input questions, Transmission means for transmitting the questions from the input means to the server, Analysis means for analyzing the questions received by the server and identifying necessary data, Acquisition means for acquiring the data identified by the server from the database, Generation means for generating an appropriate answer based on the data obtained by the acquisition means, Answer transmission means for transmitting the answer generated by the generation means to the user's terminal, Display means for displaying on the user's terminal, A system including the above.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In conventional operations, when employees asked about the data storage location for numerical analysis or the usage method of a specific system, they had to ask the persons in charge of other departments or conduct investigations on their own for a long time. This labor and time had become a problem of significantly reducing work efficiency. Furthermore, when the persons in charge with specific knowledge were absent, it became even more difficult to obtain appropriate information, hindering prompt problem-solving.

Means for Solving the Problems

[0005] The present invention solves the above problems by providing a system that analyzes questions input by users and automatically acquires and provides appropriate data. Specifically, it includes the following means.

[0006] The system includes an input means for the user to enter a question, a transmission means for sending the entered question to a server, an analysis means for the server to analyze the received question and identify the necessary data, an acquisition means for obtaining the identified data from a database, a generation means for generating an appropriate answer based on the acquired data, an answer transmission means for sending the generated answer to the user's terminal, and a display means for displaying the answer on the user's terminal. This allows the user to quickly obtain the necessary information and improves work efficiency.

[0007] A "user" is an entity that uses a system to input questions and obtain answers.

[0008] "Input means" refers to a device or interface for a user to input a question.

[0009] "Transmission means" refers to a function or device for sending the question from the input means to the server.

[0010] A "server" is a computer device that analyzes questions received from users, retrieves necessary data, generates answers, and transmits them.

[0011] "Analysis means" refers to a function or device used by a server to analyze a received question and identify the necessary data.

[0012] "Acquisition means" refers to a function or device for acquiring data identified by the analysis means from a database.

[0013] "Generation means" refers to a function or device for generating an appropriate response based on data obtained from acquisition means.

[0014] "Response transmission means" refers to a function or device for sending the generated response to the user's terminal.

[0015] "Display means" refers to a function or device for displaying the answer on the user's terminal.

[0016] "Generative artificial intelligence" is an artificial intelligence technology that extracts the intent and keywords of a question and performs analysis.

[0017] "Natural language generation technology" is a technology for generating answers in natural language based on the acquired data.

Brief Explanation of Drawings

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

Mode for Carrying Out the Invention

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

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

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

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

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

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

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

[0026] [First Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0039] This invention is a system that automatically retrieves necessary data based on questions entered by the user and provides appropriate answers. To properly operate this system, the following basic procedures should be followed.

[0040] User inquiries

[0041] First, the user enters a question into their device. For example, they might enter the question, "Where can I find the latest sales data?" The device is equipped with input methods such as a keyboard or touchscreen, and the user uses these to enter the question.

[0042] Submit a question

[0043] Next, the terminal sends the user's entered question to the server over the network. This is done using a common internet communication protocol (e.g., HTTP). The server then receives the user's question.

[0044] Question analysis

[0045] The server analyzes the received question. The server has a built-in generative artificial intelligence (AI) that analyzes the question content and extracts the intent of the question and necessary keywords. In this step, keywords such as "sales data" and "latest" are extracted.

[0046] Retrieving the relevant information

[0047] Based on the analysis results, the server searches the database to retrieve the relevant information. For example, the server might identify the database folder containing the "latest sales data" and retrieve that information from the database. This database contains the company's sales data and other related data.

[0048] AI-generated answer generation

[0049] Based on the acquired data, the server's AI generates an appropriate response. For example, it might generate a response such as, "The latest sales data is stored in the Q4 2023 folder in SalesDB."

[0050] Submit your response

[0051] The generated response is sent from the server to the user's terminal. This is also done using internet communication protocols.

[0052] Displaying responses to users

[0053] Finally, the terminal receives the response from the server and displays it to the user. This allows the user to quickly obtain the answer, "The latest sales data is stored in the Q4 2023 folder in SalesDB."

[0054] This configuration allows users to efficiently acquire the necessary data and improve the efficiency of their work. Because this system responds quickly and accurately to inquiries such as data storage locations, it is extremely useful in a business environment.

[0055] The following describes the processing flow.

[0056] Step 1:

[0057] The user enters a question into their device. For example, they might type, "Where can I find the latest sales data?" The user enters this question using their device's keyboard or touchscreen.

[0058] Step 2:

[0059] The device stores the user's question as text data and sends this data to the server. Specifically, it uses an HTTP POST request, including the question content in the request body.

[0060] Step 3:

[0061] The server parses the received request and extracts the text data of the question from the request body. The server preprocesses the text data as needed (e.g., tokenization and stop word removal).

[0062] Step 4:

[0063] Generative artificial intelligence (AI) embedded in the server analyzes the question. This AI understands the intent of the question and extracts important keywords. For example, it identifies keywords such as "latest" and "sales data."

[0064] Step 5:

[0065] The server searches the database based on the extracted keywords. For example, it identifies the "latest data" from the database where sales data is stored and retrieves it.

[0066] Step 6:

[0067] Based on the data acquired by the server's AI, natural language generation technology is used to generate appropriate answers. For example, it can generate a specific answer such as, "The latest sales data is stored in the Q4 2023 folder in SalesDB."

[0068] Step 7:

[0069] The server formats the generated response data into an HTTP response format and sends it to the user's terminal. Specifically, it sends the response body containing the response content along with an HTTP status code of 200 OK.

[0070] Step 8:

[0071] The terminal analyzes the response received from the server and extracts the answer data from the response body. The terminal then displays this answer data to the user. For example, the terminal screen might display "The latest sales data is stored in the Q4 2023 folder in SalesDB."

[0072] These steps enable users to quickly obtain the information they need, leading to increased efficiency in their work.

[0073] (Example 1)

[0074] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0075] The objective is to provide an efficient method for users to quickly and accurately obtain specific information. Conventional information retrieval methods require users to spend a lot of time and effort searching for the information they need, resulting in decreased work efficiency. This invention aims to solve these problems by automatically generating the optimal answer to the user's question.

[0076] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0077] In this invention, the server includes an input means for a user to input a question, a transmission means for transmitting the question from the input means to an information processing device, an analysis means for analyzing the question received by the information processing device and identifying the necessary information, an acquisition means for obtaining the information identified by the information processing device from a data storage device, a generation means for generating an appropriate answer based on the information obtained from the acquisition means, an answer transmission means for transmitting the answer generated by the generation means to the user's terminal, and a display means for displaying the answer on the user's terminal. This enables the user to efficiently obtain the necessary information and improve the efficiency of their work.

[0078] "Input means" refers to a device or method for a user to input a question.

[0079] "Transmission means" refers to a device or method for transmitting a question entered by a user to an information processing device.

[0080] An "information processing device" is a computing device or system used to analyze a question, identify necessary information, and generate an answer.

[0081] "Analysis means" refers to a device or method for an information processing device to analyze a question received and identify the necessary information.

[0082] A "data storage device" is a device or system in which necessary information is stored.

[0083] "Acquisition means" refers to the devices or methods by which an information processing device acquires necessary information from a data storage device.

[0084] "Generation means" refers to a device or method for generating an appropriate response based on information obtained from acquisition means.

[0085] "Response transmission means" refers to a device or method for transmitting the response generated by the generation means to the user's terminal.

[0086] "Display means" refers to a device or method for displaying the generated response on the user's terminal.

[0087] This invention relates to a system that automatically acquires necessary data based on questions entered by a user and provides appropriate answers. To properly operate this system, the following configuration and operation are implemented.

[0088] Hardware and Software Overview

[0089] 1. User's terminal

[0090] Hardware used: Personal computers, smartphones, tablets, and other standard input devices (keyboards, touchscreens, etc.)

[0091] Software used: Web browser, mobile application

[0092] 2. Server

[0093] Hardware required: High-performance computing equipment (e.g., server-class machines)

[0094] Software used:

[0095] Question analysis and answer generation: TENSORFLOW®, PyTorch, OpenAI®, GPT-3®, Google®, BERT

[0096] Database management: MySQL (registered trademark), PostgreSQL

[0097] Processing flow and detailed explanation

[0098] 1. The user enters the question.

[0099] The user enters the question using their own device. For example, they might type "Where can I find the latest sales data?" into the search bar of their web browser.

[0100] 2. Submit your question

[0101] The terminal sends the entered question to the server over the network. The question is sent to the server in JSON format using the HTTP protocol.

[0102] 3. Analysis of the Question

[0103] The server parses the received JSON data and extracts the question. At this stage, generative artificial intelligence (AI) such as TensorFlow or PyTorch is used to identify the intent of the question and keywords. For example, the keywords "sales data" and "latest" might be extracted.

[0104] 4. Obtaining the relevant information

[0105] Based on the analysis results, the server searches for relevant data in databases such as MySQL or PostgreSQL. For example, the server might identify the folder where the "latest sales data" is stored and retrieve the corresponding data.

[0106] 5. Answer generation using AI

[0107] The server's AI generation system (e.g., OpenAI GPT-3, Google BERT) generates appropriate answers based on the acquired data. For example, it might generate an answer such as, "The latest sales data is stored in the Q4 2023 folder in SalesDB."

[0108] 6. Submit your response

[0109] The server converts the generated response back into JSON format and sends it to the user's terminal as an HTTP response.

[0110] 7. Displaying responses to users

[0111] The user's device parses the received JSON data and displays the generated response in a web browser or mobile application. This allows the user to obtain the answer quickly.

[0112] Examples of specific cases and prompt statements

[0113] When a user enters "Where can I find the latest sales data?" into their device, the process proceeds as follows:

[0114] Example of a prompt:

[0115] "The user is asking, 'Where can I find the latest sales data?' Please generate an appropriate answer to this question. The data is in SalesDB, and the most recent data is in the Q4 2023 folder."

[0116] Example of a generated response:

[0117] "The latest sales data is stored in the Q4 2023 folder in SalesDB."

[0118] This system allows users to efficiently obtain the information they need, thereby improving the efficiency of their work.

[0119] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0120] Step 1:

[0121] The user enters the question using their own device. For example, the user might type "Where can I find the latest sales data?" into the search bar of their web browser. This generates the text data for the question.

[0122] Input: A question entered by the user (e.g., "Where can I find the latest sales data?")

[0123] Output: Text data of the question

[0124] Specific actions:

[0125] The user opens a web browser.

[0126] Enter your question in the search bar.

[0127] Click the "Send" button.

[0128] Step 2:

[0129] The terminal sends the user's entered questions to the server via the network. During this process, the text data of the questions is converted to JSON format and sent as an HTTP request.

[0130] Input: Text data of the question on the user's device

[0131] Output: JSON formatted data sent to the server

[0132] Specific actions:

[0133] The terminal converts the question's text data into JSON format.

[0134] Send JSON data to the server as a POST request using the HTTP protocol.

[0135] Step 3:

[0136] The server analyzes the received question. First, the server parses the received JSON data and extracts the question content. Next, it uses generative artificial intelligence (AI) to analyze the intent and keywords of the question.

[0137] Input: Question data in JSON format received by the server

[0138] Output: Analyzed keywords and question intent

[0139] Specific actions:

[0140] The server parses the received JSON data.

[0141] Extract the question content.

[0142] The extracted content is input into a generative AI, which analyzes keywords such as "sales data" and "latest" along with their intent.

[0143] Step 4:

[0144] The server searches the database based on the analysis results. The server queries database management systems such as MySQL and PostgreSQL to retrieve the relevant data.

[0145] Input: Analyzed keywords and question intent

[0146] Output: Retrieved relevant data (e.g., latest sales data)

[0147] Specific actions:

[0148] The server connects to the database.

[0149] Generate and execute SQL queries based on keywords.

[0150] Retrieve the latest sales data from the database.

[0151] Step 5:

[0152] The server's AI generates appropriate answers based on the acquired data. The generated answers are expressed in natural language.

[0153] Input: Retrieved related data

[0154] Output: Generated response (Example: "The latest sales data is stored in the Q4 2023 folder in SalesDB.")

[0155] Specific actions:

[0156] The server generates prompt messages based on the acquired data and inputs them into the AI ​​model.

[0157] The generative AI model generates the answer text.

[0158] Step 6:

[0159] The server converts the generated response back into JSON format and sends it to the user's terminal as an HTTP response.

[0160] Input: Generated answer

[0161] Output: Response data in JSON format sent to the user's device.

[0162] Specific actions:

[0163] The server converts the generated response into JSON format.

[0164] Send JSON data to the user's device using the HTTP protocol.

[0165] Step 7:

[0166] The user's device parses the received JSON data and extracts the response content to display the answer. This allows the response to be displayed on the user's screen.

[0167] Input: Response data in JSON format sent from the server.

[0168] Output: The response displayed on the user's screen (e.g., "The latest sales data is stored in the Q4 2023 folder in SalesDB").

[0169] Specific actions:

[0170] The terminal parses the received JSON data.

[0171] Display the extracted responses in a web browser or application.

[0172] The user checks the answer.

[0173] (Application Example 1)

[0174] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0175] In manufacturing environments, it is crucial for managers to quickly and appropriately obtain production-related information in order to improve production efficiency and solve problems. However, existing systems have limitations in terms of the effort required to manually search for necessary data and the ability to obtain information in real time, making efficient data acquisition and response difficult. Therefore, there is a need for a system that streamlines management operations in the production environment and provides information quickly.

[0176] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0177] In this invention, the server includes an input means for a user to input a question, a transmission means for sending the question from the input means to the server, an analysis means for analyzing the question received by the server and identifying the necessary data, an acquisition means for obtaining the data identified by the server from a database, a generation means for generating an appropriate answer based on the data obtained from the acquisition means, an answer transmission means for sending the answer generated by the generation means to the user's terminal, a display means for displaying the answer on the user's terminal, and a means installed in a work execution device at the manufacturing site for acquiring production-related information in response to inquiries from an administrator and displaying the answer. As a result, administrators can quickly and automatically acquire production-related information at the manufacturing site, and an improvement in work efficiency can be expected.

[0178] A "user" refers to a person who works in the manufacturing or management field and uses the system to obtain information.

[0179] "Input method" refers to interfaces such as keyboards, touchscreens, and voice input devices that users use to input questions.

[0180] "Transmission means" refers to the network communication means used to send the entered question to the server.

[0181] A "server" refers to a central processing unit that analyzes received questions, identifies necessary data, and generates answers using a generative AI.

[0182] "Analysis method" refers to a generative artificial intelligence that extracts the intent and keywords of a question on a server.

[0183] "Acquisition means" refers to the software and hardware used to retrieve data identified by the analysis means from a database.

[0184] "Generation method" refers to software that uses natural language generation technology to generate appropriate responses based on acquired data.

[0185] "Response transmission means" refers to network communication means for transmitting the response generated by the generation means to the user's terminal.

[0186] "Display means" refers to an interface such as a display used to visually display the received response on the user's terminal.

[0187] "Work execution device" refers to robots and automated machinery operating in a manufacturing environment.

[0188] A "manager" refers to a person responsible for acquiring information about the manufacturing site and manufacturing process, and for performing management duties.

[0189] This invention is a system for streamlining information acquisition in manufacturing sites. It automatically acquires necessary data based on user-inputted questions and provides appropriate answers. This system is implemented in the following manner.

[0190] System Configuration

[0191] The system includes a means for user input, a means for sending questions to the server, an analysis and acquisition means by the server, a generation means, an answer transmission means, a display means on the user's terminal, and an information acquisition means installed on the work execution device.

[0192] Program processing

[0193] When a user enters a question into a terminal, the input means receives the question and sends it to the server via the transmission means. The server uses an analysis means (generative artificial intelligence) to analyze the question and extract necessary keywords and intent. Next, the acquisition means retrieves the necessary data from the database based on this extracted information. Subsequently, the generation means generates an appropriate answer using natural language generation technology based on the acquired data.

[0194] The generated answers are sent to the user's terminal via the answer transmission means and displayed to the user by the display means. Furthermore, if a user, acting as an administrator, inputs a question using the information acquisition means installed on the work execution device at the manufacturing site, the data can be automatically acquired and answers provided quickly.

[0195] Hardware and software to be used

[0196] Server: A central processing unit for data analysis and response generation. (Example: High-performance computing server)

[0197] Input methods: Keyboard, touchscreen, voice input device. (Example: Peripherals connected to a Raspberry Pi)

[0198] Generative artificial intelligence: AI models such as Hugging Face Transformers.

[0199] Database: MySQL or other SQL databases.

[0200] Device display method: Display. (Example: Tablet screen)

[0201] Specific example

[0202] For example, suppose a factory manager asks, "What is the current operating status of the production line?" The user enters this question into a terminal via an input device. This question is sent to a server via a transmission device and analyzed by an analysis device (generative artificial intelligence) on the server. The server's acquisition device retrieves information about the current production line from a database, and the generation device generates an answer based on that data. The generated answer will be "The current operating rate is 80%", which is sent to the user's terminal via an answer transmission device and displayed to the manager via a display device.

[0203] Next, here are some examples of prompts for the generated AI model.

[0204] Question: 'Could you tell me the current operating status of your production line?'

[0205] Context: 'Production line A: 80% operational. Production line B: 60% operational. Production line C: 90% operational.'

[0206] This allows users to quickly and accurately obtain the necessary information using an automated system, thereby improving the efficiency of management tasks on the production floor.

[0207] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0208] Step 1:

[0209] The user enters a question into the terminal. Using an input device (keyboard, touchscreen, voice input device), the user enters a question about the production site (e.g., "What is the current operating status of the production line?"). Once the input is complete, the question is sent to the transmission device.

[0210] Input: User's question

[0211] Output: Question data for submission

[0212] Step 2:

[0213] The terminal sends a question to the server via a transmission method. The entered question is sent to the server using a network communication protocol (e.g., HTTP). This process transfers the question data to the server.

[0214] Input: Question data for submission

[0215] Output: Question data received by the server

[0216] Step 3:

[0217] The server analyzes the received question using generative artificial intelligence. The analysis extracts the intent and necessary keywords from the question. For example, the keywords "production line operating status" and "currently" are analyzed.

[0218] Input: Question data received by the server

[0219] Output: Extracted intent and keywords

[0220] Step 4:

[0221] The server searches the database based on the analysis results and retrieves the relevant production information. Using the retrieval method, it searches the database for records that match the specified keywords and obtains the latest operating status of the production line.

[0222] Input: Extracted intent and keywords

[0223] Output: Applicable production information

[0224] Step 5:

[0225] The server generates responses using a generative AI model based on the acquired data. The generation method takes the acquired production information as a prompt and generates an appropriate response using natural language generation technology. For example, a response such as "The current operating rate is 80%" might be generated.

[0226] Input: Relevant production information

[0227] Output: Generated answer

[0228] Step 6:

[0229] The server sends the generated response to the user's terminal via a transmission method. It uses a network communication protocol to send the generated response to the terminal.

[0230] Input: Generated answer

[0231] Output: Response received on the user's device

[0232] Step 7:

[0233] The terminal receives the response and displays it to the user through a display device. The received response is displayed on the screen so that the user can easily confirm it. For example, the terminal's display might show "Current uptime is 80%."

[0234] Input: Response received on the user's device

[0235] Output: The answer displayed on the screen

[0236] This allows users to quickly and automatically obtain critical information from the manufacturing site through the system.

[0237] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0238] This invention combines a system that analyzes user-inputted questions, automatically retrieves necessary data, and provides appropriate answers with a function that recognizes user emotions and adjusts the content of the answers accordingly. To properly operate this system, the following basic procedures should be followed.

[0239] User question reception and sentiment recognition

[0240] First, the user enters a question into their device. For example, they might enter the question, "Where can I find the latest sales data?" Meanwhile, the device is equipped with a camera and microphone, and an emotion recognition system operates to recognize emotions through the user's facial expressions and voice. As the user enters the question, this emotion recognition system analyzes the user's voice and facial expressions and extracts emotion data.

[0241] Sending questions and sentiment data

[0242] Next, the terminal sends the user's entered questions and sentiment data to the server over the network. This procedure allows the server to receive the user's questions and sentiment data.

[0243] Analysis of Questions and Sentiment Data

[0244] The server analyzes the received questions and sentiment data. The server incorporates generative artificial intelligence (AI), which analyzes the question content and sentiment data. The AI ​​understands the intent of the question, extracts important keywords, and adjusts the tone and content of the response based on the sentiment data.

[0245] Retrieving the relevant information

[0246] Based on the analysis results, the server searches the database to retrieve the relevant information. For example, the server identifies the database folder where the "latest sales data" is stored and retrieves that information from the database.

[0247] AI-generated response generation and emotion adjustment

[0248] Based on the acquired data, the server's AI generates an appropriate response. After analyzing the emotional data, the AI ​​adjusts the tone and content of the response. For example, if the user expresses anxiety, the response may include reassuring phrases such as, "Please rest assured."

[0249] Submit your response

[0250] The generated response is sent from the server to the user's terminal.

[0251] Displaying responses to users

[0252] Finally, the terminal receives the response from the server and displays it to the user. This allows the user to quickly receive a response that reflects their own feelings, such as, "The latest sales data is stored in the Q4 2023 folder in SalesDB. Please rest assured."

[0253] This format allows users to obtain information quickly and appropriately, while simultaneously improving the user experience by providing emotionally responsive answers. This system not only contributes to a significant improvement in operational efficiency but also delivers a better user experience by enabling responses that consider the user's emotions.

[0254] The following describes the processing flow.

[0255] Step 1:

[0256] The user enters a question into their device. For example, they might type, "Where can I find the latest sales data?" The user enters the question using the device's keyboard or touchscreen.

[0257] Step 2:

[0258] As soon as the device receives a user's question, it uses its camera and microphone to collect the user's facial expressions and voice data. This data is then analyzed by emotion recognition tools to identify the user's emotions.

[0259] Step 3:

[0260] The device sends the user's question text and analyzed sentiment data to the server. It uses an HTTP POST request, including the question content and sentiment data in the request body.

[0261] Step 4:

[0262] The server analyzes the received request and extracts the question content and sentiment data from the request body. Based on this, the server uses generative artificial intelligence (AI) to identify the intent of the question and keywords.

[0263] Step 5:

[0264] The server searches the database based on the analysis and retrieves the necessary data. For example, it might execute a database query to extract "latest sales data" and identify the relevant data folder (e.g., the Q4 2023 folder).

[0265] Step 6:

[0266] The server generates responses based on the data acquired by the AI. Furthermore, it adjusts the tone and expression of the responses by taking emotional data into consideration. For example, if the user is feeling anxious, the response may include phrases such as, "Please rest assured."

[0267] Step 7:

[0268] The server formats the generated response and sends it to the user's terminal. This is also done in the form of an HTTP response, with the response content included in the response body along with a status code 200 OK.

[0269] Step 8:

[0270] The terminal analyzes the response received from the server and extracts the answer data from the response body. Based on this data, the terminal displays the answer to the user. For example, the terminal screen might display, "The latest sales data is stored in the Q4 2023 folder in SalesDB. Please rest assured."

[0271] This allows users to quickly obtain the information they need and receive emotionally sensitive responses. Through this entire process, the user experience is improved and operations become more efficient.

[0272] (Example 2)

[0273] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0274] Traditional question-answering systems provide appropriate data in response to user questions, but they lack the ability to respond while considering the user's emotions. As a result, they fail to alleviate user anxiety and stress, limiting the user experience. Furthermore, the lack of emotionally balanced responses leads to decreased user satisfaction and impacts the reliability of the system.

[0275] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0276] In this invention, the server includes an input means for the user to input a question, a transmission means for sending the question and emotion data from the input means to the server, an analysis means for analyzing the question and emotion data received by the server and identifying necessary data, an acquisition means for obtaining the data identified by the server from a database, a generation means for generating an appropriate answer including emotion data based on the data obtained from the acquisition means, an answer transmission means for sending the answer generated by the generation means to the user's terminal, and a display means for displaying the answer on the user's terminal. This enables responses that take the user's emotions into consideration, reduces user anxiety and stress, and improves the user experience.

[0277] "Input means" refers to a device or interface for a user to input a question.

[0278] A "transmission method" refers to a device or protocol that has the functionality to send questions and sentiment data to a server over a network.

[0279] The "analysis means" refers to software or algorithms for analyzing the questions and sentiment data received by the server to identify the necessary data and answers.

[0280] The "acquisition means" refers to a program or interface having a function for acquiring the necessary data identified by the analysis means from a database.

[0281] The "generation means" refers to natural language generation techniques or algorithms for generating appropriate answers in consideration of sentiment data based on the data obtained from the acquisition means.

[0282] The "answer transmission means" refers to a network interface or protocol for transmitting the generated answer to the user's terminal.

[0283] The "display means" refers to a display or UI component for displaying the received answer on the user's terminal.

[0284] The present invention is a system that analyzes the questions input by a user, automatically acquires the necessary data, provides appropriate answers, and further combines a function of recognizing the user's sentiment and adjusting the answer content. To properly operate this system, the following basic hardware and software are used.

[0285] Hardware to be used

[0286] User terminal: A personal computer, smartphone, tablet, etc., equipped with a camera and a microphone.

[0287] Server: A high-performance computer system that holds a database and a generation AI model.

[0288] Software to be used

[0289] Emotion recognition software: This software analyzes input from the device's camera and microphone to extract emotional data from the user's facial expressions and voice. For this purpose, the use of Python's OpenCV and biometric software is recommended.

[0290] Generative AI Model: A generative artificial intelligence (such as GPT-4®) that runs on a server, extracts the intent and keywords of a question, and generates an answer.

[0291] Database system: A database management system such as Microsoft SQL Server stores the necessary data.

[0292] Specific examples of the system

[0293] 1. The user enters the question "Where can I find the latest sales data?" into the device. At this time, the device's camera and microphone record the user's facial expressions and voice, and emotion recognition software extracts emotion data.

[0294] 2. The terminal sends the entered question and sentiment data to the server in JSON format. An example of the content sent will be in the following format:

[0295] {

[0296] "Question": "Where can I find the latest sales data?"

[0297] "Emotion": "Anxiety"

[0298] }

[0299] 3. The server analyzes the received question and sentiment data. A generative AI model is used to extract the intent of the question and key keywords (e.g., "latest," "sales data," "where," etc.). The analysis results can also be viewed in the form of a prompt message like the following:

[0300] Question entered by the user: "Where is the latest sales data?"

[0301] User's emotion: "Anxiety"

[0302] 4. The server searches the database, executes a specific SQL query to obtain the corresponding data. For example, execute the following query:

[0303] SELECT FROM SalesDB.QuarterlySales WHERE Quarter='2023Q4';

[0304] 5. The server uses the obtained data to generate an AI model to generate an appropriate answer, and adjusts the tone of the answer based on the emotion data. For example, add the phrase "Please rest assured." to the answer.

[0305] 6. The server sends the generated answer to the terminal in JSON format, and the terminal receives and displays it.

[0306] Example of a specific response:

[0307] The latest sales data is stored in the 2023 Q4 folder of SalesDB. Please rest assured.

[0308] Through the above process, the user can quickly obtain an appropriate answer corresponding to their question and emotion. Also, this system can reduce the user's anxiety and stress and improve the user experience.

[0309] The flow of the specific process in Example 2 will be described using FIG. 13.

[0310] Step 1:

[0311] The user enters a question into the terminal. For example, "Where can I find the latest sales data?" The terminal receives the input as text data and simultaneously records the user's facial expressions and voice using the camera and microphone. The input data includes the question text and audio / video data. The output consists of text data and multimedia data.

[0312] Step 2:

[0313] The device performs emotion recognition using the input question text, recorded audio, and video data. Here, we use Python's OpenCV and an emotion recognition algorithm to extract emotion data from the user's facial expressions and voice. Inputs include the user's question text, facial image, and audio data. Output is emotion data (e.g., "anxiety").

[0314] Step 3:

[0315] The terminal packages the question text and sentiment data in JSON format and sends it to the server over the network. The input consists of the question text and sentiment data, and the output is the data converted to JSON format (e.g., {"Question": "Where can I find the latest sales data?", "Sentiment": "Anxious"}).

[0316] Step 4:

[0317] The server parses the received JSON data. First, it separates the question text from the sentiment data and uses a generative AI model (e.g., GPT-4) to extract the intent of the question and keywords. The input is JSON data, and the output is the parsed intent of the question and keywords (e.g., "latest", "sales data", "where").

[0318] Step 5:

[0319] The server searches the database based on the retrieved intent and keywords. Here, it uses an SQL query to retrieve the relevant data from the "SalesDB" database in Microsoft SQL Server. The input is the parsed keywords, and the output is sales data retrieved from the database (e.g., "Sales data for Q4 2023").

[0320] Step 6:

[0321] The server generates responses using a generative AI model based on acquired data and sentiment data. The tone of the response is adjusted based on the sentiment data. For example, for the sentiment data of "anxiety," the phrase "Please rest assured" is added. The input consists of data and sentiment data acquired from a database, and the output is the adjusted response text (e.g., "The latest sales data is stored in the Q4 2023 folder in SalesDB. Please rest assured.").

[0322] Step 7:

[0323] The server sends the generated response text to the user's terminal in JSON format. The input is the generated response text, and the output is data sent to the terminal in JSON format.

[0324] Step 8:

[0325] The terminal parses the JSON data received from the server and extracts the answer text. Next, the extracted text is displayed on the terminal's screen. The input is the JSON data received from the server, and the output is the answer text displayed in a format visible to the user. The user can then verify this and obtain the answer to the question.

[0326] (Application Example 2)

[0327] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0328] Traditional question-answering systems have struggled to respond appropriately to user emotions, making it difficult to improve the user experience. Furthermore, especially in security-related emergencies, users often exhibit anxiety and impatience, which can hinder appropriate responses. Such situations increase the risk of decreased user satisfaction. Therefore, there is a need for a system that can respond appropriately to user emotions and improve the user experience.

[0329] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0330] In this invention, the server includes emotion recognition means for recognizing the user's emotions, emotion transmission means for transmitting the emotions recognized by the emotion recognition means to the server, and emotion analysis means for analyzing the emotions and adjusting the tone and content of the response. This makes it possible to provide a response that corresponds to the user's emotions.

[0331] An "input method" refers to a device or interface used by a user to input a question.

[0332] "Transmission means" refers to a communication mechanism for sending the entered question to the server.

[0333] "Analysis means" refers to a function that analyzes the questions received by the server and identifies the necessary data and answers.

[0334] "Acquisition method" refers to the mechanism by which the server retrieves the identified data from the database.

[0335] "Generation means" refers to a function that generates appropriate answers based on acquired data.

[0336] "Response transmission means" refers to a communication mechanism for sending the generated response to the user's terminal.

[0337] "Display means" refers to a display device that allows the user to check their answers on their terminal.

[0338] "Emotion recognition means" refers to a function that recognizes emotions through the user's facial expressions and voice.

[0339] An "emotion transmission means" is a communication mechanism for sending recognized emotion data to a server.

[0340] "Emotional analysis means" refers to a function that analyzes the emotional data received by the server and adjusts the tone and content of the response.

[0341] This invention provides a system that recognizes the user's emotions and adjusts the tone and content of the response accordingly. This system consists of the following elements:

[0342] Hardware configuration

[0343] 1. Input method: A device or interface through which the user enters a question (e.g., smartphone, tablet, computer).

[0344] 2. Camera and microphone: Devices for capturing the user's facial expressions and voice.

[0345] 3. Communication equipment: Network communication equipment for transmitting question data and sentiment data to the server.

[0346] Software Configuration

[0347] 1. Question Analysis Software: Using a generative AI model installed on the server, the intent of the question and important keywords are analyzed.

[0348] 2. Database access software: Retrieves necessary data from the database on the server.

[0349] 3. Answer generation software: A function that uses a generative AI model to generate answers to questions based on acquired data.

[0350] 4. Emotion Recognition Software: A function that analyzes data acquired from the camera and microphone to recognize the user's emotions (e.g., OpenCV, speech_recognition).

[0351] 5. Sentiment analysis software: A function that analyzes recognized sentiment data and adjusts the tone and content of responses (e.g., sentiment analysis algorithm).

[0352] Processing flow

[0353] 1. Users input questions using a smartphone or computer. At the same time, the camera and microphone capture the user's facial expressions and voice.

[0354] 2. The entered questions and captured sentiment data are sent to the server via the terminal's transmission method.

[0355] 3. The server analyzes the received question and extracts its intent and key keywords (e.g., using generative artificial intelligence).

[0356] 4. Based on the analysis results, the server searches the database and retrieves the necessary data.

[0357] 5. Based on the acquired data, the server's AI generates an appropriate response.

[0358] 6. Simultaneously, the server's sentiment analysis system analyzes the user's sentiment data and adjusts the tone and content of the response.

[0359] 7. The generated response is sent to the user's terminal by the response transmission means, and the user can confirm it through the display means.

[0360] Examples of specific cases and prompt statements

[0361] To illustrate the use of the system, let's consider a scenario where a user has forgotten their password and shows signs of anxiety or frustration:

[0362] Example of a prompt

[0363] The user is expressing anxiety. Please generate appropriate answers to the following questions: I forgot my password.

[0364] In this way, the system can provide quick and appropriate answers while taking the user's feelings into consideration. This improves the user experience and provides a sense of security, especially in emergencies.

[0365] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0366] Step 1:

[0367] The user enters a question.

[0368] Users input questions using their smartphones or computers. During this process, the camera and microphone capture the user's facial expressions and voice. The input device acquires the user's question text and sentiment data, which is then passed on to the next step.

[0369] Step 2:

[0370] The device sends the question and sentiment data to the server.

[0371] The terminal transmits the acquired user question text and sentiment data to the server via internet communication. The input data (question text, sentiment data) is packetized and transferred to the server via the network.

[0372] Step 3:

[0373] The server analyzes the question data and identifies the necessary data.

[0374] The server analyzes the user's question received and extracts its intent and key keywords using generative artificial intelligence. It analyzes the input data (question text), identifies the intent and keywords, and passes them on to the next database search step.

[0375] Step 4:

[0376] The server searches the database and retrieves the necessary data.

[0377] Based on the analysis results, the server searches relevant databases and retrieves the necessary data. Using the input data (intent, keywords), it generates database search queries, extracts appropriate data, and passes it on to the next step.

[0378] Step 5:

[0379] The server analyzes the emotional data.

[0380] The server analyzes the emotional data acquired by the emotion recognition system to identify the user's emotions. It analyzes the input data (emotional data) to identify the user's emotional state (e.g., anxious, excited, calm).

[0381] Step 6:

[0382] The server generates the response data.

[0383] The server generates appropriate responses using a generative AI model based on acquired data and analyzed sentiment data. It generates prompt sentences based on input data (acquired data, sentiment state) and then generates response text based on those prompts.

[0384] Step 7:

[0385] The server sends the generated response to the user's device.

[0386] The server sends the generated response to the terminal. The input data (response text) is packetized and transferred to the user's terminal via the network.

[0387] Step 8:

[0388] The device displays the answer to the user.

[0389] The user's device displays the received responses to the user via a display device. The input data (response text) is analyzed and displayed in a user-friendly format, which the user can then review.

[0390] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0391] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0392] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0393] [Second Embodiment]

[0394] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0395] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0396] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0397] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0398] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0399] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0400] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0401] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0402] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0403] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0404] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0405] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0406] This invention is a system that automatically retrieves necessary data based on questions entered by the user and provides appropriate answers. To properly operate this system, the following basic procedures should be followed.

[0407] User inquiries

[0408] First, the user enters a question into their device. For example, they might enter the question, "Where can I find the latest sales data?" The device is equipped with input methods such as a keyboard or touchscreen, and the user uses these to enter the question.

[0409] Submit a question

[0410] Next, the terminal sends the user's entered question to the server over the network. This is done using a common internet communication protocol (e.g., HTTP). The server then receives the user's question.

[0411] Question analysis

[0412] The server analyzes the received question. The server has a built-in generative artificial intelligence (AI) that analyzes the question content and extracts the intent of the question and necessary keywords. In this step, keywords such as "sales data" and "latest" are extracted.

[0413] Retrieving the relevant information

[0414] Based on the analysis results, the server searches the database to retrieve the relevant information. For example, the server might identify the database folder containing the "latest sales data" and retrieve that information from the database. This database contains the company's sales data and other related data.

[0415] AI-generated answer generation

[0416] Based on the acquired data, the server's AI generates an appropriate response. For example, it might generate a response such as, "The latest sales data is stored in the Q4 2023 folder in SalesDB."

[0417] Submit your response

[0418] The generated response is sent from the server to the user's terminal. This is also done using internet communication protocols.

[0419] Displaying responses to users

[0420] Finally, the terminal receives the response from the server and displays it to the user. This allows the user to quickly obtain the answer, "The latest sales data is stored in the Q4 2023 folder in SalesDB."

[0421] This configuration allows users to efficiently acquire the necessary data and improve the efficiency of their work. Because this system responds quickly and accurately to inquiries such as data storage locations, it is extremely useful in a business environment.

[0422] The following describes the processing flow.

[0423] Step 1:

[0424] The user enters a question into their device. For example, they might type, "Where can I find the latest sales data?" The user enters this question using their device's keyboard or touchscreen.

[0425] Step 2:

[0426] The device stores the user's question as text data and sends this data to the server. Specifically, it uses an HTTP POST request, including the question content in the request body.

[0427] Step 3:

[0428] The server parses the received request and extracts the text data of the question from the request body. The server preprocesses the text data as needed (e.g., tokenization and stop word removal).

[0429] Step 4:

[0430] Generative artificial intelligence (AI) embedded in the server analyzes the question. This AI understands the intent of the question and extracts important keywords. For example, it identifies keywords such as "latest" and "sales data."

[0431] Step 5:

[0432] The server searches the database based on the extracted keywords. For example, it identifies the "latest data" from the database where sales data is stored and retrieves it.

[0433] Step 6:

[0434] Based on the data acquired by the server's AI, natural language generation technology is used to generate appropriate answers. For example, it can generate a specific answer such as, "The latest sales data is stored in the Q4 2023 folder in SalesDB."

[0435] Step 7:

[0436] The server formats the generated response data into an HTTP response format and sends it to the user's terminal. Specifically, it sends the response body containing the response content along with an HTTP status code of 200 OK.

[0437] Step 8:

[0438] The terminal analyzes the response received from the server and extracts the answer data from the response body. The terminal then displays this answer data to the user. For example, the terminal screen might display "The latest sales data is stored in the Q4 2023 folder in SalesDB."

[0439] These steps enable users to quickly obtain the information they need, leading to increased efficiency in their work.

[0440] (Example 1)

[0441] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0442] The objective is to provide an efficient method for users to quickly and accurately obtain specific information. Conventional information retrieval methods require users to spend a lot of time and effort searching for the information they need, resulting in decreased work efficiency. This invention aims to solve these problems by automatically generating the optimal answer to the user's question.

[0443] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0444] In this invention, the server includes an input means for a user to input a question, a transmission means for transmitting the question from the input means to an information processing device, an analysis means for analyzing the question received by the information processing device and identifying the necessary information, an acquisition means for obtaining the information identified by the information processing device from a data storage device, a generation means for generating an appropriate answer based on the information obtained from the acquisition means, an answer transmission means for transmitting the answer generated by the generation means to the user's terminal, and a display means for displaying the answer on the user's terminal. This enables the user to efficiently obtain the necessary information and improve the efficiency of their work.

[0445] "Input means" refers to a device or method for a user to input a question.

[0446] "Transmission means" refers to a device or method for transmitting a question entered by a user to an information processing device.

[0447] An "information processing device" is a computing device or system used to analyze a question, identify necessary information, and generate an answer.

[0448] "Analysis means" refers to a device or method for an information processing device to analyze a question received and identify the necessary information.

[0449] A "data storage device" is a device or system in which necessary information is stored.

[0450] "Acquisition means" refers to the devices or methods by which an information processing device acquires necessary information from a data storage device.

[0451] "Generation means" refers to a device or method for generating an appropriate response based on information obtained from acquisition means.

[0452] "Response transmission means" refers to a device or method for transmitting the response generated by the generation means to the user's terminal.

[0453] "Display means" refers to a device or method for displaying the generated response on the user's terminal.

[0454] This invention relates to a system that automatically acquires necessary data based on questions entered by a user and provides appropriate answers. To properly operate this system, the following configuration and operation are implemented.

[0455] Hardware and Software Overview

[0456] 1. User's terminal

[0457] Hardware used: Personal computers, smartphones, tablets, and other standard input devices (keyboards, touchscreens, etc.)

[0458] Software used: Web browser, mobile application

[0459] 2. Server

[0460] Hardware required: High-performance computing equipment (e.g., server-class machines)

[0461] Software used:

[0462] Question analysis and answer generation: TensorFlow, PyTorch, OpenAI GPT-3, Google BERT

[0463] Database management: MySQL, PostgreSQL

[0464] Processing flow and detailed explanation

[0465] 1. The user enters the question.

[0466] The user enters the question using their own device. For example, they might type "Where can I find the latest sales data?" into the search bar of their web browser.

[0467] 2. Submit your question

[0468] The terminal sends the entered question to the server over the network. The question is sent to the server in JSON format using the HTTP protocol.

[0469] 3. Analysis of the Question

[0470] The server parses the received JSON data and extracts the question. At this stage, generative artificial intelligence (AI) such as TensorFlow or PyTorch is used to identify the intent of the question and keywords. For example, the keywords "sales data" and "latest" might be extracted.

[0471] 4. Obtaining the relevant information

[0472] Based on the analysis results, the server searches for relevant data in databases such as MySQL or PostgreSQL. For example, the server might identify the folder where the "latest sales data" is stored and retrieve the corresponding data.

[0473] 5. Answer generation using AI

[0474] The server's AI generation system (e.g., OpenAI GPT-3, Google BERT) generates appropriate answers based on the acquired data. For example, it might generate an answer such as, "The latest sales data is stored in the Q4 2023 folder in SalesDB."

[0475] 6. Submit your response

[0476] The server converts the generated response back into JSON format and sends it to the user's terminal as an HTTP response.

[0477] 7. Displaying responses to users

[0478] The user's device parses the received JSON data and displays the generated response in a web browser or mobile application. This allows the user to obtain the answer quickly.

[0479] Examples of specific cases and prompt statements

[0480] When a user enters "Where can I find the latest sales data?" into their device, the process proceeds as follows:

[0481] Example of a prompt:

[0482] "The user is asking, 'Where can I find the latest sales data?' Please generate an appropriate answer to this question. The data is in SalesDB, and the most recent data is in the Q4 2023 folder."

[0483] Example of a generated response:

[0484] "The latest sales data is stored in the Q4 2023 folder in SalesDB."

[0485] This system allows users to efficiently obtain the information they need, thereby improving the efficiency of their work.

[0486] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0487] Step 1:

[0488] The user enters the question using their own device. For example, the user might type "Where can I find the latest sales data?" into the search bar of their web browser. This generates the text data for the question.

[0489] Input: A question entered by the user (e.g., "Where can I find the latest sales data?")

[0490] Output: Text data of the question

[0491] Specific actions:

[0492] The user opens a web browser.

[0493] Enter your question in the search bar.

[0494] Click the "Send" button.

[0495] Step 2:

[0496] The terminal sends the user's entered questions to the server via the network. During this process, the text data of the questions is converted to JSON format and sent as an HTTP request.

[0497] Input: Text data of the question on the user's device

[0498] Output: JSON formatted data sent to the server

[0499] Specific actions:

[0500] The terminal converts the question's text data into JSON format.

[0501] Send JSON data to the server as a POST request using the HTTP protocol.

[0502] Step 3:

[0503] The server analyzes the received question. First, the server parses the received JSON data and extracts the question content. Next, it uses generative artificial intelligence (AI) to analyze the intent and keywords of the question.

[0504] Input: Question data in JSON format received by the server

[0505] Output: Analyzed keywords and question intent

[0506] Specific actions:

[0507] The server parses the received JSON data.

[0508] Extract the question content.

[0509] The extracted content is input into a generative AI, which analyzes keywords such as "sales data" and "latest" along with their intent.

[0510] Step 4:

[0511] The server searches the database based on the analysis results. The server queries database management systems such as MySQL and PostgreSQL to retrieve the relevant data.

[0512] Input: Analyzed keywords and question intent

[0513] Output: Retrieved relevant data (e.g., latest sales data)

[0514] Specific actions:

[0515] The server connects to the database.

[0516] Generate and execute SQL queries based on keywords.

[0517] Retrieve the latest sales data from the database.

[0518] Step 5:

[0519] The server's AI generates appropriate answers based on the acquired data. The generated answers are expressed in natural language.

[0520] Input: Retrieved related data

[0521] Output: Generated response (Example: "The latest sales data is stored in the Q4 2023 folder in SalesDB.")

[0522] Specific actions:

[0523] The server generates prompt messages based on the acquired data and inputs them into the AI ​​model.

[0524] The generative AI model generates the answer text.

[0525] Step 6:

[0526] The server converts the generated response back into JSON format and sends it to the user's terminal as an HTTP response.

[0527] Input: Generated answer

[0528] Output: Response data in JSON format sent to the user's device.

[0529] Specific actions:

[0530] The server converts the generated response into JSON format.

[0531] Send JSON data to the user's device using the HTTP protocol.

[0532] Step 7:

[0533] The user's device parses the received JSON data and extracts the response content to display the answer. This allows the response to be displayed on the user's screen.

[0534] Input: Response data in JSON format sent from the server.

[0535] Output: The response displayed on the user's screen (e.g., "The latest sales data is stored in the Q4 2023 folder in SalesDB").

[0536] Specific actions:

[0537] The terminal parses the received JSON data.

[0538] Display the extracted responses in a web browser or application.

[0539] The user checks the answer.

[0540] (Application Example 1)

[0541] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0542] In manufacturing environments, it is crucial for managers to quickly and appropriately obtain production-related information in order to improve production efficiency and solve problems. However, existing systems have limitations in terms of the effort required to manually search for necessary data and the ability to obtain information in real time, making efficient data acquisition and response difficult. Therefore, there is a need for a system that streamlines management operations in the production environment and provides information quickly.

[0543] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0544] In this invention, the server includes an input means for a user to input a question, a transmission means for sending the question from the input means to the server, an analysis means for analyzing the question received by the server and identifying the necessary data, an acquisition means for obtaining the data identified by the server from a database, a generation means for generating an appropriate answer based on the data obtained from the acquisition means, an answer transmission means for sending the answer generated by the generation means to the user's terminal, a display means for displaying the answer on the user's terminal, and a means installed in a work execution device at the manufacturing site for acquiring production-related information in response to inquiries from an administrator and displaying the answer. As a result, administrators can quickly and automatically acquire production-related information at the manufacturing site, and an improvement in work efficiency can be expected.

[0545] A "user" refers to a person who works in the manufacturing or management field and uses the system to obtain information.

[0546] "Input method" refers to interfaces such as keyboards, touchscreens, and voice input devices that users use to input questions.

[0547] "Transmission means" refers to the network communication means used to send the entered question to the server.

[0548] A "server" refers to a central processing unit that analyzes received questions, identifies necessary data, and generates answers using a generative AI.

[0549] "Analysis method" refers to a generative artificial intelligence that extracts the intent and keywords of a question on a server.

[0550] "Acquisition means" refers to the software and hardware used to retrieve data identified by the analysis means from a database.

[0551] "Generation method" refers to software that uses natural language generation technology to generate appropriate responses based on acquired data.

[0552] "Response transmission means" refers to network communication means for transmitting the response generated by the generation means to the user's terminal.

[0553] "Display means" refers to an interface such as a display used to visually display the received response on the user's terminal.

[0554] "Work execution device" refers to robots and automated machinery operating in a manufacturing environment.

[0555] A "manager" refers to a person responsible for acquiring information about the manufacturing site and manufacturing process, and for performing management duties.

[0556] This invention is a system for streamlining information acquisition in manufacturing sites. It automatically acquires necessary data based on user-inputted questions and provides appropriate answers. This system is implemented in the following manner.

[0557] System Configuration

[0558] The system includes a means for user input, a means for sending questions to the server, an analysis and acquisition means by the server, a generation means, an answer transmission means, a display means on the user's terminal, and an information acquisition means installed on the work execution device.

[0559] Program processing

[0560] When a user enters a question into a terminal, the input means receives the question and sends it to the server via the transmission means. The server uses an analysis means (generative artificial intelligence) to analyze the question and extract necessary keywords and intent. Next, the acquisition means retrieves the necessary data from the database based on this extracted information. Subsequently, the generation means generates an appropriate answer using natural language generation technology based on the acquired data.

[0561] The generated answers are sent to the user's terminal via the answer transmission means and displayed to the user by the display means. Furthermore, if a user, acting as an administrator, inputs a question using the information acquisition means installed on the work execution device at the manufacturing site, the data can be automatically acquired and answers provided quickly.

[0562] Hardware and software to be used

[0563] Server: A central processing unit for data analysis and response generation. (Example: High-performance computing server)

[0564] Input methods: Keyboard, touchscreen, voice input device. (Example: Peripherals connected to a Raspberry Pi)

[0565] Generative artificial intelligence: AI models such as Hugging Face Transformers.

[0566] Database: MySQL or other SQL databases.

[0567] Device display method: Display. (Example: Tablet screen)

[0568] Specific example

[0569] For example, suppose a factory manager asks, "What is the current operating status of the production line?" The user enters this question into a terminal via an input device. This question is sent to a server via a transmission device and analyzed by an analysis device (generative artificial intelligence) on the server. The server's acquisition device retrieves information about the current production line from a database, and the generation device generates an answer based on that data. The generated answer will be "The current operating rate is 80%", which is sent to the user's terminal via an answer transmission device and displayed to the manager via a display device.

[0570] Next, here are some examples of prompts for the generated AI model.

[0571] Question: 'Could you tell me the current operating status of your production line?'

[0572] Context: 'Production line A: 80% operational. Production line B: 60% operational. Production line C: 90% operational.'

[0573] This allows users to quickly and accurately obtain the necessary information using an automated system, thereby improving the efficiency of management tasks on the production floor.

[0574] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0575] Step 1:

[0576] The user enters a question into the terminal. Using an input device (keyboard, touchscreen, voice input device), the user enters a question about the production site (e.g., "What is the current operating status of the production line?"). Once the input is complete, the question is sent to the transmission device.

[0577] Input: User's question

[0578] Output: Question data for submission

[0579] Step 2:

[0580] The terminal sends a question to the server via a transmission method. The entered question is sent to the server using a network communication protocol (e.g., HTTP). This process transfers the question data to the server.

[0581] Input: Question data for submission

[0582] Output: Question data received by the server

[0583] Step 3:

[0584] The server analyzes the received question using generative artificial intelligence. The analysis extracts the intent and necessary keywords from the question. For example, the keywords "production line operating status" and "currently" are analyzed.

[0585] Input: Question data received by the server

[0586] Output: Extracted intent and keywords

[0587] Step 4:

[0588] The server searches the database based on the analysis results and retrieves the relevant production information. Using the retrieval method, it searches the database for records that match the specified keywords and obtains the latest operating status of the production line.

[0589] Input: Extracted intent and keywords

[0590] Output: Applicable production information

[0591] Step 5:

[0592] The server generates responses using a generative AI model based on the acquired data. The generation method takes the acquired production information as a prompt and generates an appropriate response using natural language generation technology. For example, a response such as "The current operating rate is 80%" might be generated.

[0593] Input: Relevant production information

[0594] Output: Generated answer

[0595] Step 6:

[0596] The server sends the generated response to the user's terminal via a transmission method. It uses a network communication protocol to send the generated response to the terminal.

[0597] Input: Generated answer

[0598] Output: Response received on the user's device

[0599] Step 7:

[0600] The terminal receives the response and displays it to the user through a display device. The received response is displayed on the screen so that the user can easily confirm it. For example, the terminal's display might show "Current uptime is 80%."

[0601] Input: Response received on the user's device

[0602] Output: The answer displayed on the screen

[0603] This allows users to quickly and automatically obtain critical information from the manufacturing site through the system.

[0604] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0605] This invention combines a system that analyzes user-inputted questions, automatically retrieves necessary data, and provides appropriate answers with a function that recognizes user emotions and adjusts the content of the answers accordingly. To properly operate this system, the following basic procedures should be followed.

[0606] User question reception and sentiment recognition

[0607] First, the user enters a question into their device. For example, they might enter the question, "Where can I find the latest sales data?" Meanwhile, the device is equipped with a camera and microphone, and an emotion recognition system operates to recognize emotions through the user's facial expressions and voice. As the user enters the question, this emotion recognition system analyzes the user's voice and facial expressions and extracts emotion data.

[0608] Sending questions and sentiment data

[0609] Next, the terminal sends the user's entered questions and sentiment data to the server over the network. This procedure allows the server to receive the user's questions and sentiment data.

[0610] Analysis of Questions and Sentiment Data

[0611] The server analyzes the received questions and sentiment data. The server incorporates generative artificial intelligence (AI), which analyzes the question content and sentiment data. The AI ​​understands the intent of the question, extracts important keywords, and adjusts the tone and content of the response based on the sentiment data.

[0612] Retrieving the relevant information

[0613] Based on the analysis results, the server searches the database to retrieve the relevant information. For example, the server identifies the database folder where the "latest sales data" is stored and retrieves that information from the database.

[0614] AI-generated response generation and emotion adjustment

[0615] Based on the acquired data, the server's AI generates an appropriate response. After analyzing the emotional data, the AI ​​adjusts the tone and content of the response. For example, if the user expresses anxiety, the response may include reassuring phrases such as, "Please rest assured."

[0616] Submit your response

[0617] The generated response is sent from the server to the user's terminal.

[0618] Displaying responses to users

[0619] Finally, the terminal receives the response from the server and displays it to the user. This allows the user to quickly receive a response that reflects their own feelings, such as, "The latest sales data is stored in the Q4 2023 folder in SalesDB. Please rest assured."

[0620] This format allows users to obtain information quickly and appropriately, while simultaneously improving the user experience by providing emotionally responsive answers. This system not only contributes to a significant improvement in operational efficiency but also delivers a better user experience by enabling responses that consider the user's emotions.

[0621] The following describes the processing flow.

[0622] Step 1:

[0623] The user enters a question into their device. For example, they might type, "Where can I find the latest sales data?" The user enters the question using the device's keyboard or touchscreen.

[0624] Step 2:

[0625] As soon as the device receives a user's question, it uses its camera and microphone to collect the user's facial expressions and voice data. This data is then analyzed by emotion recognition tools to identify the user's emotions.

[0626] Step 3:

[0627] The device sends the user's question text and analyzed sentiment data to the server. It uses an HTTP POST request, including the question content and sentiment data in the request body.

[0628] Step 4:

[0629] The server analyzes the received request and extracts the question content and sentiment data from the request body. Based on this, the server uses generative artificial intelligence (AI) to identify the intent of the question and keywords.

[0630] Step 5:

[0631] The server searches the database based on the analysis and retrieves the necessary data. For example, it might execute a database query to extract "latest sales data" and identify the relevant data folder (e.g., the Q4 2023 folder).

[0632] Step 6:

[0633] The server generates responses based on the data acquired by the AI. Furthermore, it adjusts the tone and expression of the responses by taking emotional data into consideration. For example, if the user is feeling anxious, the response may include phrases such as, "Please rest assured."

[0634] Step 7:

[0635] The server formats the generated response and sends it to the user's terminal. This is also done in the form of an HTTP response, with the response content included in the response body along with a status code 200 OK.

[0636] Step 8:

[0637] The terminal analyzes the response received from the server and extracts the answer data from the response body. Based on this data, the terminal displays the answer to the user. For example, the terminal screen might display, "The latest sales data is stored in the Q4 2023 folder in SalesDB. Please rest assured."

[0638] This allows users to quickly obtain the information they need and receive emotionally sensitive responses. Through this entire process, the user experience is improved and operations become more efficient.

[0639] (Example 2)

[0640] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0641] Traditional question-answering systems provide appropriate data in response to user questions, but they lack the ability to respond while considering the user's emotions. As a result, they fail to alleviate user anxiety and stress, limiting the user experience. Furthermore, the lack of emotionally balanced responses leads to decreased user satisfaction and impacts the reliability of the system.

[0642] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0643] In this invention, the server includes an input means for the user to input a question, a transmission means for sending the question and emotion data from the input means to the server, an analysis means for analyzing the question and emotion data received by the server and identifying necessary data, an acquisition means for obtaining the data identified by the server from a database, a generation means for generating an appropriate answer including emotion data based on the data obtained from the acquisition means, an answer transmission means for sending the answer generated by the generation means to the user's terminal, and a display means for displaying the answer on the user's terminal. This enables responses that take the user's emotions into consideration, reduces user anxiety and stress, and improves the user experience.

[0644] "Input means" refers to a device or interface for a user to input a question.

[0645] A "transmission method" refers to a device or protocol that has the functionality to send questions and sentiment data to a server over a network.

[0646] "Analysis means" refers to software or algorithms used by a server to analyze received questions and sentiment data to identify necessary data and answers.

[0647] "Acquisition means" refers to a program or interface that has the function of retrieving the necessary data identified by the analysis means from a database.

[0648] "Generation means" refers to natural language generation technologies and algorithms that generate appropriate responses based on data obtained from acquisition means, while also considering emotional data.

[0649] "Response transmission means" refers to the network interface or protocol used to send the generated response to the user's terminal.

[0650] "Display means" refers to a display or UI component on the user's device used to display the received response.

[0651] This invention is a system that analyzes user-inputted questions, automatically acquires necessary data, and provides appropriate answers, further incorporating a function that recognizes the user's emotions and adjusts the content of the answers accordingly. To properly operate this system, the following basic hardware and software are required.

[0652] Hardware to use

[0653] User terminals include personal computers, smartphones, and tablets, which are equipped with cameras and microphones.

[0654] Server: A high-performance computer system that holds the database and the generative AI models.

[0655] Software to use

[0656] Emotion recognition software: This software analyzes input from the device's camera and microphone to extract emotional data from the user's facial expressions and voice. For this purpose, the use of Python's OpenCV and biometric software is recommended.

[0657] Generative AI Model: A generative artificial intelligence (such as GPT-4) that runs on a server to extract the intent and keywords of a question and generate an answer.

[0658] Database system: A database management system, such as Microsoft SQL Server, is used to store the necessary data.

[0659] Specific examples of the system

[0660] 1. The user enters the question "Where can I find the latest sales data?" into the device. At this time, the device's camera and microphone record the user's facial expressions and voice, and emotion recognition software extracts emotion data.

[0661] 2. The terminal sends the entered question and sentiment data to the server in JSON format. An example of the content sent will be in the following format:

[0662] {

[0663] "Question": "Where can I find the latest sales data?"

[0664] "Emotion": "Anxiety"

[0665] }

[0666] 3. The server analyzes the received question and sentiment data. A generative AI model is used to extract the intent of the question and key keywords (e.g., "latest," "sales data," "where," etc.). The analysis results can also be viewed in the form of a prompt message like the following:

[0667] User's question: "Where can I find the latest sales data?"

[0668] User's emotion: "Anxiety"

[0669] 4. The server searches the database and executes a specific SQL query to retrieve the relevant data. For example, it executes a query like this:

[0670] SELECT FROM SalesDB.QuarterlySales WHERE Quarter='2023Q4';

[0671] 5. The server uses a generative AI model based on the acquired data to generate appropriate responses and adjusts the tone of the responses based on sentiment data. For example, it might add the phrase "Please rest assured." to the response.

[0672] 6. The server sends the generated response to the terminal in JSON format, and the terminal receives and displays it.

[0673] Examples of specific responses:

[0674] The latest sales data is stored in the Q4 2023 folder in SalesDB. Please rest assured.

[0675] Through the process described above, users can quickly obtain appropriate answers that address their questions and feelings. Furthermore, this system can reduce user anxiety and stress, improving the user experience.

[0676] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0677] Step 1:

[0678] The user enters a question into the terminal. For example, "Where can I find the latest sales data?" The terminal receives the input as text data and simultaneously records the user's facial expressions and voice using the camera and microphone. The input data includes the question text and audio / video data. The output consists of text data and multimedia data.

[0679] Step 2:

[0680] The device performs emotion recognition using the input question text, recorded audio, and video data. Here, we use Python's OpenCV and an emotion recognition algorithm to extract emotion data from the user's facial expressions and voice. Inputs include the user's question text, facial image, and audio data. Output is emotion data (e.g., "anxiety").

[0681] Step 3:

[0682] The terminal packages the question text and sentiment data in JSON format and sends it to the server over the network. The input consists of the question text and sentiment data, and the output is the data converted to JSON format (e.g., {"Question": "Where can I find the latest sales data?", "Sentiment": "Anxious"}).

[0683] Step 4:

[0684] The server parses the received JSON data. First, it separates the question text from the sentiment data and uses a generative AI model (e.g., GPT-4) to extract the intent of the question and keywords. The input is JSON data, and the output is the parsed intent of the question and keywords (e.g., "latest", "sales data", "where").

[0685] Step 5:

[0686] The server searches the database based on the retrieved intent and keywords. Here, it uses an SQL query to retrieve the relevant data from the "SalesDB" database in Microsoft SQL Server. The input is the parsed keywords, and the output is sales data retrieved from the database (e.g., "Sales data for Q4 2023").

[0687] Step 6:

[0688] The server generates responses using a generative AI model based on acquired data and sentiment data. The tone of the response is adjusted based on the sentiment data. For example, for the sentiment data of "anxiety," the phrase "Please rest assured" is added. The input consists of data and sentiment data acquired from a database, and the output is the adjusted response text (e.g., "The latest sales data is stored in the Q4 2023 folder in SalesDB. Please rest assured.").

[0689] Step 7:

[0690] The server sends the generated response text to the user's terminal in JSON format. The input is the generated response text, and the output is data sent to the terminal in JSON format.

[0691] Step 8:

[0692] The terminal parses the JSON data received from the server and extracts the answer text. Next, the extracted text is displayed on the terminal's screen. The input is the JSON data received from the server, and the output is the answer text displayed in a format visible to the user. The user can then verify this and obtain the answer to the question.

[0693] (Application Example 2)

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

[0695] Traditional question-answering systems have struggled to respond appropriately to user emotions, making it difficult to improve the user experience. Furthermore, especially in security-related emergencies, users often exhibit anxiety and impatience, which can hinder appropriate responses. Such situations increase the risk of decreased user satisfaction. Therefore, there is a need for a system that can respond appropriately to user emotions and improve the user experience.

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

[0697] In this invention, the server includes emotion recognition means for recognizing the user's emotions, emotion transmission means for transmitting the emotions recognized by the emotion recognition means to the server, and emotion analysis means for analyzing the emotions and adjusting the tone and content of the response. This makes it possible to provide a response that corresponds to the user's emotions.

[0698] An "input method" refers to a device or interface used by a user to input a question.

[0699] "Transmission means" refers to a communication mechanism for sending the entered question to the server.

[0700] "Analysis means" refers to a function that analyzes the questions received by the server and identifies the necessary data and answers.

[0701] "Acquisition method" refers to the mechanism by which the server retrieves the identified data from the database.

[0702] "Generation means" refers to a function that generates appropriate answers based on acquired data.

[0703] "Response transmission means" refers to a communication mechanism for sending the generated response to the user's terminal.

[0704] "Display means" refers to a display device that allows the user to check their answers on their terminal.

[0705] "Emotion recognition means" refers to a function that recognizes emotions through the user's facial expressions and voice.

[0706] An "emotion transmission means" is a communication mechanism for sending recognized emotion data to a server.

[0707] "Emotional analysis means" refers to a function that analyzes the emotional data received by the server and adjusts the tone and content of the response.

[0708] This invention provides a system that recognizes the user's emotions and adjusts the tone and content of the response accordingly. This system consists of the following elements:

[0709] Hardware configuration

[0710] 1. Input method: A device or interface through which the user enters a question (e.g., smartphone, tablet, computer).

[0711] 2. Camera and microphone: Devices for capturing the user's facial expressions and voice.

[0712] 3. Communication equipment: Network communication equipment for transmitting question data and sentiment data to the server.

[0713] Software Configuration

[0714] 1. Question Analysis Software: Using a generative AI model installed on the server, the intent of the question and important keywords are analyzed.

[0715] 2. Database access software: Retrieves necessary data from the database on the server.

[0716] 3. Answer generation software: A function that uses a generative AI model to generate answers to questions based on acquired data.

[0717] 4. Emotion Recognition Software: A function that analyzes data acquired from the camera and microphone to recognize the user's emotions (e.g., OpenCV, speech_recognition).

[0718] 5. Sentiment analysis software: A function that analyzes recognized sentiment data and adjusts the tone and content of responses (e.g., sentiment analysis algorithm).

[0719] Processing flow

[0720] 1. Users input questions using a smartphone or computer. At the same time, the camera and microphone capture the user's facial expressions and voice.

[0721] 2. The entered questions and captured sentiment data are sent to the server via the terminal's transmission method.

[0722] 3. The server analyzes the received question and extracts its intent and key keywords (e.g., using generative artificial intelligence).

[0723] 4. Based on the analysis results, the server searches the database and retrieves the necessary data.

[0724] 5. Based on the acquired data, the server's AI generates an appropriate response.

[0725] 6. Simultaneously, the server's sentiment analysis system analyzes the user's sentiment data and adjusts the tone and content of the response.

[0726] 7. The generated response is sent to the user's terminal by the response transmission means, and the user can confirm it through the display means.

[0727] Examples of specific cases and prompt statements

[0728] To illustrate the use of the system, let's consider a scenario where a user has forgotten their password and shows signs of anxiety or frustration:

[0729] Example of a prompt

[0730] The user is expressing anxiety. Please generate appropriate answers to the following questions: I forgot my password.

[0731] In this way, the system can provide quick and appropriate answers while taking the user's feelings into consideration. This improves the user experience and provides a sense of security, especially in emergencies.

[0732] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0733] Step 1:

[0734] The user enters a question.

[0735] Users input questions using their smartphones or computers. During this process, the camera and microphone capture the user's facial expressions and voice. The input device acquires the user's question text and sentiment data, which is then passed on to the next step.

[0736] Step 2:

[0737] The device sends the question and sentiment data to the server.

[0738] The terminal transmits the acquired user question text and sentiment data to the server via internet communication. The input data (question text, sentiment data) is packetized and transferred to the server via the network.

[0739] Step 3:

[0740] The server analyzes the question data and identifies the necessary data.

[0741] The server analyzes the user's question received and extracts its intent and key keywords using generative artificial intelligence. It analyzes the input data (question text), identifies the intent and keywords, and passes them on to the next database search step.

[0742] Step 4:

[0743] The server searches the database and retrieves the necessary data.

[0744] Based on the analysis results, the server searches relevant databases and retrieves the necessary data. Using the input data (intent, keywords), it generates database search queries, extracts appropriate data, and passes it on to the next step.

[0745] Step 5:

[0746] The server analyzes the emotional data.

[0747] The server analyzes the emotional data acquired by the emotion recognition system to identify the user's emotions. It analyzes the input data (emotional data) to identify the user's emotional state (e.g., anxious, excited, calm).

[0748] Step 6:

[0749] The server generates the response data.

[0750] The server generates appropriate responses using a generative AI model based on acquired data and analyzed sentiment data. It generates prompt sentences based on input data (acquired data, sentiment state) and then generates response text based on those prompts.

[0751] Step 7:

[0752] The server sends the generated response to the user's device.

[0753] The server sends the generated response to the terminal. The input data (response text) is packetized and transferred to the user's terminal via the network.

[0754] Step 8:

[0755] The device displays the answer to the user.

[0756] The user's device displays the received responses to the user via a display device. The input data (response text) is analyzed and displayed in a user-friendly format, which the user can then review.

[0757] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0758] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0759] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0760] [Third Embodiment]

[0761] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0762] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0763] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0764] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0765] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0766] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0767] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0768] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0769] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0770] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0771] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0772] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0773] This invention is a system that automatically retrieves necessary data based on questions entered by the user and provides appropriate answers. To properly operate this system, the following basic procedures should be followed.

[0774] User inquiries

[0775] First, the user enters a question into their device. For example, they might enter the question, "Where can I find the latest sales data?" The device is equipped with input methods such as a keyboard or touchscreen, and the user uses these to enter the question.

[0776] Submit a question

[0777] Next, the terminal sends the user's entered question to the server over the network. This is done using a common internet communication protocol (e.g., HTTP). The server then receives the user's question.

[0778] Question analysis

[0779] The server analyzes the received question. The server has a built-in generative artificial intelligence (AI) that analyzes the question content and extracts the intent of the question and necessary keywords. In this step, keywords such as "sales data" and "latest" are extracted.

[0780] Retrieving the relevant information

[0781] Based on the analysis results, the server searches the database to retrieve the relevant information. For example, the server might identify the database folder containing the "latest sales data" and retrieve that information from the database. This database contains the company's sales data and other related data.

[0782] AI-generated answer generation

[0783] Based on the acquired data, the server's AI generates an appropriate response. For example, it might generate a response such as, "The latest sales data is stored in the Q4 2023 folder in SalesDB."

[0784] Submit your response

[0785] The generated response is sent from the server to the user's terminal. This is also done using internet communication protocols.

[0786] Displaying responses to users

[0787] Finally, the terminal receives the response from the server and displays it to the user. This allows the user to quickly obtain the answer, "The latest sales data is stored in the Q4 2023 folder in SalesDB."

[0788] This configuration allows users to efficiently acquire the necessary data and improve the efficiency of their work. Because this system responds quickly and accurately to inquiries such as data storage locations, it is extremely useful in a business environment.

[0789] The following describes the processing flow.

[0790] Step 1:

[0791] The user enters a question into their device. For example, they might type, "Where can I find the latest sales data?" The user enters this question using their device's keyboard or touchscreen.

[0792] Step 2:

[0793] The device stores the user's question as text data and sends this data to the server. Specifically, it uses an HTTP POST request, including the question content in the request body.

[0794] Step 3:

[0795] The server parses the received request and extracts the text data of the question from the request body. The server preprocesses the text data as needed (e.g., tokenization and stop word removal).

[0796] Step 4:

[0797] Generative artificial intelligence (AI) embedded in the server analyzes the question. This AI understands the intent of the question and extracts important keywords. For example, it identifies keywords such as "latest" and "sales data."

[0798] Step 5:

[0799] The server searches the database based on the extracted keywords. For example, it identifies the "latest data" from the database where sales data is stored and retrieves it.

[0800] Step 6:

[0801] Based on the data acquired by the server's AI, natural language generation technology is used to generate appropriate answers. For example, it can generate a specific answer such as, "The latest sales data is stored in the Q4 2023 folder in SalesDB."

[0802] Step 7:

[0803] The server formats the generated response data into an HTTP response format and sends it to the user's terminal. Specifically, it sends the response body containing the response content along with an HTTP status code of 200 OK.

[0804] Step 8:

[0805] The terminal analyzes the response received from the server and extracts the answer data from the response body. The terminal then displays this answer data to the user. For example, the terminal screen might display "The latest sales data is stored in the Q4 2023 folder in SalesDB."

[0806] These steps enable users to quickly obtain the information they need, leading to increased efficiency in their work.

[0807] (Example 1)

[0808] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0809] The objective is to provide an efficient method for users to quickly and accurately obtain specific information. Conventional information retrieval methods require users to spend a lot of time and effort searching for the information they need, resulting in decreased work efficiency. This invention aims to solve these problems by automatically generating the optimal answer to the user's question.

[0810] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0811] In this invention, the server includes an input means for a user to input a question, a transmission means for transmitting the question from the input means to an information processing device, an analysis means for analyzing the question received by the information processing device and identifying the necessary information, an acquisition means for obtaining the information identified by the information processing device from a data storage device, a generation means for generating an appropriate answer based on the information obtained from the acquisition means, an answer transmission means for transmitting the answer generated by the generation means to the user's terminal, and a display means for displaying the answer on the user's terminal. This enables the user to efficiently obtain the necessary information and improve the efficiency of their work.

[0812] "Input means" refers to a device or method for a user to input a question.

[0813] "Transmission means" refers to a device or method for transmitting a question entered by a user to an information processing device.

[0814] An "information processing device" is a computing device or system used to analyze a question, identify necessary information, and generate an answer.

[0815] "Analysis means" refers to a device or method for an information processing device to analyze a question received and identify the necessary information.

[0816] A "data storage device" is a device or system in which necessary information is stored.

[0817] "Acquisition means" refers to the devices or methods by which an information processing device acquires necessary information from a data storage device.

[0818] "Generation means" refers to a device or method for generating an appropriate response based on information obtained from acquisition means.

[0819] "Response transmission means" refers to a device or method for transmitting the response generated by the generation means to the user's terminal.

[0820] "Display means" refers to a device or method for displaying the generated response on the user's terminal.

[0821] This invention relates to a system that automatically acquires necessary data based on questions entered by a user and provides appropriate answers. To properly operate this system, the following configuration and operation are implemented.

[0822] Hardware and Software Overview

[0823] 1. User's terminal

[0824] Hardware used: Personal computers, smartphones, tablets, and other standard input devices (keyboards, touchscreens, etc.)

[0825] Software used: Web browser, mobile application

[0826] 2. Server

[0827] Hardware required: High-performance computing equipment (e.g., server-class machines)

[0828] Software used:

[0829] Question analysis and answer generation: TensorFlow, PyTorch, OpenAI GPT-3, Google BERT

[0830] Database management: MySQL, PostgreSQL

[0831] Processing flow and detailed explanation

[0832] 1. The user enters the question.

[0833] The user enters the question using their own device. For example, they might type "Where can I find the latest sales data?" into the search bar of their web browser.

[0834] 2. Submit your question

[0835] The terminal sends the entered question to the server over the network. The question is sent to the server in JSON format using the HTTP protocol.

[0836] 3. Analysis of the Question

[0837] The server parses the received JSON data and extracts the question. At this stage, generative artificial intelligence (AI) such as TensorFlow or PyTorch is used to identify the intent of the question and keywords. For example, the keywords "sales data" and "latest" might be extracted.

[0838] 4. Obtaining the relevant information

[0839] Based on the analysis results, the server searches for relevant data in databases such as MySQL or PostgreSQL. For example, the server might identify the folder where the "latest sales data" is stored and retrieve the corresponding data.

[0840] 5. Answer generation using AI

[0841] The server's AI generation system (e.g., OpenAI GPT-3, Google BERT) generates appropriate answers based on the acquired data. For example, it might generate an answer such as, "The latest sales data is stored in the Q4 2023 folder in SalesDB."

[0842] 6. Submit your response

[0843] The server converts the generated response back into JSON format and sends it to the user's terminal as an HTTP response.

[0844] 7. Displaying responses to users

[0845] The user's device parses the received JSON data and displays the generated response in a web browser or mobile application. This allows the user to obtain the answer quickly.

[0846] Examples of specific cases and prompt statements

[0847] When a user enters "Where can I find the latest sales data?" into their device, the process proceeds as follows:

[0848] Example of a prompt:

[0849] "The user is asking, 'Where can I find the latest sales data?' Please generate an appropriate answer to this question. The data is in SalesDB, and the most recent data is in the Q4 2023 folder."

[0850] Example of a generated response:

[0851] "The latest sales data is stored in the Q4 2023 folder in SalesDB."

[0852] This system allows users to efficiently obtain the information they need, thereby improving the efficiency of their work.

[0853] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0854] Step 1:

[0855] The user enters the question using their own device. For example, the user might type "Where can I find the latest sales data?" into the search bar of their web browser. This generates the text data for the question.

[0856] Input: A question entered by the user (e.g., "Where can I find the latest sales data?")

[0857] Output: Text data of the question

[0858] Specific actions:

[0859] The user opens a web browser.

[0860] Enter your question in the search bar.

[0861] Click the "Send" button.

[0862] Step 2:

[0863] The terminal sends the user's entered questions to the server via the network. During this process, the text data of the questions is converted to JSON format and sent as an HTTP request.

[0864] Input: Text data of the question on the user's device

[0865] Output: JSON formatted data sent to the server

[0866] Specific actions:

[0867] The terminal converts the question's text data into JSON format.

[0868] Send JSON data to the server as a POST request using the HTTP protocol.

[0869] Step 3:

[0870] The server analyzes the received question. First, the server parses the received JSON data and extracts the question content. Next, it uses generative artificial intelligence (AI) to analyze the intent and keywords of the question.

[0871] Input: Question data in JSON format received by the server

[0872] Output: Analyzed keywords and question intent

[0873] Specific actions:

[0874] The server parses the received JSON data.

[0875] Extract the question content.

[0876] The extracted content is input into a generative AI, which analyzes keywords such as "sales data" and "latest" along with their intent.

[0877] Step 4:

[0878] The server searches the database based on the analysis results. The server queries database management systems such as MySQL and PostgreSQL to retrieve the relevant data.

[0879] Input: Analyzed keywords and question intent

[0880] Output: Retrieved relevant data (e.g., latest sales data)

[0881] Specific actions:

[0882] The server connects to the database.

[0883] Generate and execute SQL queries based on keywords.

[0884] Retrieve the latest sales data from the database.

[0885] Step 5:

[0886] The server's AI generates appropriate answers based on the acquired data. The generated answers are expressed in natural language.

[0887] Input: Retrieved related data

[0888] Output: Generated response (Example: "The latest sales data is stored in the Q4 2023 folder in SalesDB.")

[0889] Specific actions:

[0890] The server generates prompt messages based on the acquired data and inputs them into the AI ​​model.

[0891] The generative AI model generates the answer text.

[0892] Step 6:

[0893] The server converts the generated response back into JSON format and sends it to the user's terminal as an HTTP response.

[0894] Input: Generated answer

[0895] Output: Response data in JSON format sent to the user's device.

[0896] Specific actions:

[0897] The server converts the generated response into JSON format.

[0898] Send JSON data to the user's device using the HTTP protocol.

[0899] Step 7:

[0900] The user's device parses the received JSON data and extracts the response content to display the answer. This allows the response to be displayed on the user's screen.

[0901] Input: Response data in JSON format sent from the server.

[0902] Output: The response displayed on the user's screen (e.g., "The latest sales data is stored in the Q4 2023 folder in SalesDB").

[0903] Specific actions:

[0904] The terminal parses the received JSON data.

[0905] Display the extracted responses in a web browser or application.

[0906] The user checks the answer.

[0907] (Application Example 1)

[0908] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0909] In manufacturing environments, it is crucial for managers to quickly and appropriately obtain production-related information in order to improve production efficiency and solve problems. However, existing systems have limitations in terms of the effort required to manually search for necessary data and the ability to obtain information in real time, making efficient data acquisition and response difficult. Therefore, there is a need for a system that streamlines management operations in the production environment and provides information quickly.

[0910] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0911] In this invention, the server includes an input means for a user to input a question, a transmission means for sending the question from the input means to the server, an analysis means for analyzing the question received by the server and identifying the necessary data, an acquisition means for obtaining the data identified by the server from a database, a generation means for generating an appropriate answer based on the data obtained from the acquisition means, an answer transmission means for sending the answer generated by the generation means to the user's terminal, a display means for displaying the answer on the user's terminal, and a means installed in a work execution device at the manufacturing site for acquiring production-related information in response to inquiries from an administrator and displaying the answer. As a result, administrators can quickly and automatically acquire production-related information at the manufacturing site, and an improvement in work efficiency can be expected.

[0912] A "user" refers to a person who works in the manufacturing or management field and uses the system to obtain information.

[0913] "Input method" refers to interfaces such as keyboards, touchscreens, and voice input devices that users use to input questions.

[0914] "Transmission means" refers to the network communication means used to send the entered question to the server.

[0915] A "server" refers to a central processing unit that analyzes received questions, identifies necessary data, and generates answers using a generative AI.

[0916] "Analysis method" refers to a generative artificial intelligence that extracts the intent and keywords of a question on a server.

[0917] "Acquisition means" refers to the software and hardware used to retrieve data identified by the analysis means from a database.

[0918] "Generation method" refers to software that uses natural language generation technology to generate appropriate responses based on acquired data.

[0919] "Response transmission means" refers to network communication means for transmitting the response generated by the generation means to the user's terminal.

[0920] "Display means" refers to an interface such as a display used to visually display the received response on the user's terminal.

[0921] "Work execution device" refers to robots and automated machinery operating in a manufacturing environment.

[0922] A "manager" refers to a person responsible for acquiring information about the manufacturing site and manufacturing process, and for performing management duties.

[0923] This invention is a system for streamlining information acquisition in manufacturing sites. It automatically acquires necessary data based on user-inputted questions and provides appropriate answers. This system is implemented in the following manner.

[0924] System Configuration

[0925] The system includes a means for user input, a means for sending questions to the server, an analysis and acquisition means by the server, a generation means, an answer transmission means, a display means on the user's terminal, and an information acquisition means installed on the work execution device.

[0926] Program processing

[0927] When a user enters a question into a terminal, the input means receives the question and sends it to the server via the transmission means. The server uses an analysis means (generative artificial intelligence) to analyze the question and extract necessary keywords and intent. Next, the acquisition means retrieves the necessary data from the database based on this extracted information. Subsequently, the generation means generates an appropriate answer using natural language generation technology based on the acquired data.

[0928] The generated answers are sent to the user's terminal via the answer transmission means and displayed to the user by the display means. Furthermore, if a user, acting as an administrator, inputs a question using the information acquisition means installed on the work execution device at the manufacturing site, the data can be automatically acquired and answers provided quickly.

[0929] Hardware and software to be used

[0930] Server: A central processing unit for data analysis and response generation. (Example: High-performance computing server)

[0931] Input methods: Keyboard, touchscreen, voice input device. (Example: Peripherals connected to a Raspberry Pi)

[0932] Generative artificial intelligence: AI models such as Hugging Face Transformers.

[0933] Database: MySQL or other SQL databases.

[0934] Device display method: Display. (Example: Tablet screen)

[0935] Specific example

[0936] For example, suppose a factory manager asks, "What is the current operating status of the production line?" The user enters this question into a terminal via an input device. This question is sent to a server via a transmission device and analyzed by an analysis device (generative artificial intelligence) on the server. The server's acquisition device retrieves information about the current production line from a database, and the generation device generates an answer based on that data. The generated answer will be "The current operating rate is 80%", which is sent to the user's terminal via an answer transmission device and displayed to the manager via a display device.

[0937] Next, here are some examples of prompts for the generated AI model.

[0938] Question: 'Could you tell me the current operating status of your production line?'

[0939] Context: 'Production line A: 80% operational. Production line B: 60% operational. Production line C: 90% operational.'

[0940] This allows users to quickly and accurately obtain the necessary information using an automated system, thereby improving the efficiency of management tasks on the production floor.

[0941] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0942] Step 1:

[0943] The user enters a question into the terminal. Using an input device (keyboard, touchscreen, voice input device), the user enters a question about the production site (e.g., "What is the current operating status of the production line?"). Once the input is complete, the question is sent to the transmission device.

[0944] Input: User's question

[0945] Output: Question data for submission

[0946] Step 2:

[0947] The terminal sends a question to the server via a transmission method. The entered question is sent to the server using a network communication protocol (e.g., HTTP). This process transfers the question data to the server.

[0948] Input: Question data for submission

[0949] Output: Question data received by the server

[0950] Step 3:

[0951] The server analyzes the received question using generative artificial intelligence. The analysis extracts the intent and necessary keywords from the question. For example, the keywords "production line operating status" and "currently" are analyzed.

[0952] Input: Question data received by the server

[0953] Output: Extracted intent and keywords

[0954] Step 4:

[0955] The server searches the database based on the analysis results and retrieves the relevant production information. Using the retrieval method, it searches the database for records that match the specified keywords and obtains the latest operating status of the production line.

[0956] Input: Extracted intent and keywords

[0957] Output: Applicable production information

[0958] Step 5:

[0959] The server generates responses using a generative AI model based on the acquired data. The generation method takes the acquired production information as a prompt and generates an appropriate response using natural language generation technology. For example, a response such as "The current operating rate is 80%" might be generated.

[0960] Input: Relevant production information

[0961] Output: Generated answer

[0962] Step 6:

[0963] The server sends the generated response to the user's terminal via a transmission method. It uses a network communication protocol to send the generated response to the terminal.

[0964] Input: Generated answer

[0965] Output: Response received on the user's device

[0966] Step 7:

[0967] The terminal receives the response and displays it to the user through a display device. The received response is displayed on the screen so that the user can easily confirm it. For example, the terminal's display might show "Current uptime is 80%."

[0968] Input: Response received on the user's device

[0969] Output: The answer displayed on the screen

[0970] This allows users to quickly and automatically obtain critical information from the manufacturing site through the system.

[0971] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0972] This invention combines a system that analyzes user-inputted questions, automatically retrieves necessary data, and provides appropriate answers with a function that recognizes user emotions and adjusts the content of the answers accordingly. To properly operate this system, the following basic procedures should be followed.

[0973] User question reception and sentiment recognition

[0974] First, the user enters a question into their device. For example, they might enter the question, "Where can I find the latest sales data?" Meanwhile, the device is equipped with a camera and microphone, and an emotion recognition system operates to recognize emotions through the user's facial expressions and voice. As the user enters the question, this emotion recognition system analyzes the user's voice and facial expressions and extracts emotion data.

[0975] Sending questions and sentiment data

[0976] Next, the terminal sends the user's entered questions and sentiment data to the server over the network. This procedure allows the server to receive the user's questions and sentiment data.

[0977] Analysis of Questions and Sentiment Data

[0978] The server analyzes the received questions and sentiment data. The server incorporates generative artificial intelligence (AI), which analyzes the question content and sentiment data. The AI ​​understands the intent of the question, extracts important keywords, and adjusts the tone and content of the response based on the sentiment data.

[0979] Retrieving the relevant information

[0980] Based on the analysis results, the server searches the database to retrieve the relevant information. For example, the server identifies the database folder where the "latest sales data" is stored and retrieves that information from the database.

[0981] AI-generated response generation and emotion adjustment

[0982] Based on the acquired data, the server's AI generates an appropriate response. After analyzing the emotional data, the AI ​​adjusts the tone and content of the response. For example, if the user expresses anxiety, the response may include reassuring phrases such as, "Please rest assured."

[0983] Submit your response

[0984] The generated response is sent from the server to the user's terminal.

[0985] Displaying responses to users

[0986] Finally, the terminal receives the response from the server and displays it to the user. This allows the user to quickly receive a response that reflects their own feelings, such as, "The latest sales data is stored in the Q4 2023 folder in SalesDB. Please rest assured."

[0987] This format allows users to obtain information quickly and appropriately, while simultaneously improving the user experience by providing emotionally responsive answers. This system not only contributes to a significant improvement in operational efficiency but also delivers a better user experience by enabling responses that consider the user's emotions.

[0988] The following describes the processing flow.

[0989] Step 1:

[0990] The user enters a question into their device. For example, they might type, "Where can I find the latest sales data?" The user enters the question using the device's keyboard or touchscreen.

[0991] Step 2:

[0992] As soon as the device receives a user's question, it uses its camera and microphone to collect the user's facial expressions and voice data. This data is then analyzed by emotion recognition tools to identify the user's emotions.

[0993] Step 3:

[0994] The device sends the user's question text and analyzed sentiment data to the server. It uses an HTTP POST request, including the question content and sentiment data in the request body.

[0995] Step 4:

[0996] The server analyzes the received request and extracts the question content and sentiment data from the request body. Based on this, the server uses generative artificial intelligence (AI) to identify the intent of the question and keywords.

[0997] Step 5:

[0998] The server searches the database based on the analysis and retrieves the necessary data. For example, it might execute a database query to extract "latest sales data" and identify the relevant data folder (e.g., the Q4 2023 folder).

[0999] Step 6:

[1000] The server generates responses based on the data acquired by the AI. Furthermore, it adjusts the tone and expression of the responses by taking emotional data into consideration. For example, if the user is feeling anxious, the response may include phrases such as, "Please rest assured."

[1001] Step 7:

[1002] The server formats the generated response and sends it to the user's terminal. This is also done in the form of an HTTP response, with the response content included in the response body along with a status code 200 OK.

[1003] Step 8:

[1004] The terminal analyzes the response received from the server and extracts the answer data from the response body. Based on this data, the terminal displays the answer to the user. For example, the terminal screen might display, "The latest sales data is stored in the Q4 2023 folder in SalesDB. Please rest assured."

[1005] This allows users to quickly obtain the information they need and receive emotionally sensitive responses. Through this entire process, the user experience is improved and operations become more efficient.

[1006] (Example 2)

[1007] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1008] Traditional question-answering systems provide appropriate data in response to user questions, but they lack the ability to respond while considering the user's emotions. As a result, they fail to alleviate user anxiety and stress, limiting the user experience. Furthermore, the lack of emotionally balanced responses leads to decreased user satisfaction and impacts the reliability of the system.

[1009] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1010] In this invention, the server includes an input means for the user to input a question, a transmission means for sending the question and emotion data from the input means to the server, an analysis means for analyzing the question and emotion data received by the server and identifying necessary data, an acquisition means for obtaining the data identified by the server from a database, a generation means for generating an appropriate answer including emotion data based on the data obtained from the acquisition means, an answer transmission means for sending the answer generated by the generation means to the user's terminal, and a display means for displaying the answer on the user's terminal. This enables responses that take the user's emotions into consideration, reduces user anxiety and stress, and improves the user experience.

[1011] "Input means" refers to a device or interface for a user to input a question.

[1012] A "transmission method" refers to a device or protocol that has the functionality to send questions and sentiment data to a server over a network.

[1013] "Analysis means" refers to software or algorithms used by a server to analyze received questions and sentiment data to identify necessary data and answers.

[1014] "Acquisition means" refers to a program or interface that has the function of retrieving the necessary data identified by the analysis means from a database.

[1015] "Generation means" refers to natural language generation technologies and algorithms that generate appropriate responses based on data obtained from acquisition means, while also considering emotional data.

[1016] "Response transmission means" refers to the network interface or protocol used to send the generated response to the user's terminal.

[1017] "Display means" refers to a display or UI component on the user's device used to display the received response.

[1018] This invention is a system that analyzes user-inputted questions, automatically acquires necessary data, and provides appropriate answers, further incorporating a function that recognizes the user's emotions and adjusts the content of the answers accordingly. To properly operate this system, the following basic hardware and software are required.

[1019] Hardware to use

[1020] User terminals include personal computers, smartphones, and tablets, which are equipped with cameras and microphones.

[1021] Server: A high-performance computer system that holds the database and the generative AI models.

[1022] Software to use

[1023] Emotion recognition software: This software analyzes input from the device's camera and microphone to extract emotional data from the user's facial expressions and voice. For this purpose, the use of Python's OpenCV and biometric software is recommended.

[1024] Generative AI Model: A generative artificial intelligence (such as GPT-4) that runs on a server to extract the intent and keywords of a question and generate an answer.

[1025] Database system: A database management system, such as Microsoft SQL Server, is used to store the necessary data.

[1026] Specific examples of the system

[1027] 1. The user enters the question "Where can I find the latest sales data?" into the device. At this time, the device's camera and microphone record the user's facial expressions and voice, and emotion recognition software extracts emotion data.

[1028] 2. The terminal sends the entered question and sentiment data to the server in JSON format. An example of the content sent will be in the following format:

[1029] {

[1030] "Question": "Where can I find the latest sales data?"

[1031] "Emotion": "Anxiety"

[1032] }

[1033] 3. The server analyzes the received question and sentiment data. A generative AI model is used to extract the intent of the question and key keywords (e.g., "latest," "sales data," "where," etc.). The analysis results can also be viewed in the form of a prompt message like the following:

[1034] User's question: "Where can I find the latest sales data?"

[1035] User's emotion: "Anxiety"

[1036] 4. The server searches the database and executes a specific SQL query to retrieve the relevant data. For example, it executes a query like this:

[1037] SELECT FROM SalesDB.QuarterlySales WHERE Quarter='2023Q4';

[1038] 5. The server uses a generative AI model based on the acquired data to generate appropriate responses and adjusts the tone of the responses based on sentiment data. For example, it might add the phrase "Please rest assured." to the response.

[1039] 6. The server sends the generated response to the terminal in JSON format, and the terminal receives and displays it.

[1040] Examples of specific responses:

[1041] The latest sales data is stored in the Q4 2023 folder in SalesDB. Please rest assured.

[1042] Through the process described above, users can quickly obtain appropriate answers that address their questions and feelings. Furthermore, this system can reduce user anxiety and stress, improving the user experience.

[1043] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1044] Step 1:

[1045] The user enters a question into the terminal. For example, "Where can I find the latest sales data?" The terminal receives the input as text data and simultaneously records the user's facial expressions and voice using the camera and microphone. The input data includes the question text and audio / video data. The output consists of text data and multimedia data.

[1046] Step 2:

[1047] The device performs emotion recognition using the input question text, recorded audio, and video data. Here, we use Python's OpenCV and an emotion recognition algorithm to extract emotion data from the user's facial expressions and voice. Inputs include the user's question text, facial image, and audio data. Output is emotion data (e.g., "anxiety").

[1048] Step 3:

[1049] The terminal packages the question text and sentiment data in JSON format and sends it to the server over the network. The input consists of the question text and sentiment data, and the output is the data converted to JSON format (e.g., {"Question": "Where can I find the latest sales data?", "Sentiment": "Anxious"}).

[1050] Step 4:

[1051] The server parses the received JSON data. First, it separates the question text from the sentiment data and uses a generative AI model (e.g., GPT-4) to extract the intent of the question and keywords. The input is JSON data, and the output is the parsed intent of the question and keywords (e.g., "latest", "sales data", "where").

[1052] Step 5:

[1053] The server searches the database based on the retrieved intent and keywords. Here, it uses an SQL query to retrieve the relevant data from the "SalesDB" database in Microsoft SQL Server. The input is the parsed keywords, and the output is sales data retrieved from the database (e.g., "Sales data for Q4 2023").

[1054] Step 6:

[1055] The server generates responses using a generative AI model based on acquired data and sentiment data. The tone of the response is adjusted based on the sentiment data. For example, for the sentiment data of "anxiety," the phrase "Please rest assured" is added. The input consists of data and sentiment data acquired from a database, and the output is the adjusted response text (e.g., "The latest sales data is stored in the Q4 2023 folder in SalesDB. Please rest assured.").

[1056] Step 7:

[1057] The server sends the generated response text to the user's terminal in JSON format. The input is the generated response text, and the output is data sent to the terminal in JSON format.

[1058] Step 8:

[1059] The terminal parses the JSON data received from the server and extracts the answer text. Next, the extracted text is displayed on the terminal's screen. The input is the JSON data received from the server, and the output is the answer text displayed in a format visible to the user. The user can then verify this and obtain the answer to the question.

[1060] (Application Example 2)

[1061] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1062] Traditional question-answering systems have struggled to respond appropriately to user emotions, making it difficult to improve the user experience. Furthermore, especially in security-related emergencies, users often exhibit anxiety and impatience, which can hinder appropriate responses. Such situations increase the risk of decreased user satisfaction. Therefore, there is a need for a system that can respond appropriately to user emotions and improve the user experience.

[1063] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1064] In this invention, the server includes emotion recognition means for recognizing the user's emotions, emotion transmission means for transmitting the emotions recognized by the emotion recognition means to the server, and emotion analysis means for analyzing the emotions and adjusting the tone and content of the response. This makes it possible to provide a response that corresponds to the user's emotions.

[1065] An "input method" refers to a device or interface used by a user to input a question.

[1066] "Transmission means" refers to a communication mechanism for sending the entered question to the server.

[1067] "Analysis means" refers to a function that analyzes the questions received by the server and identifies the necessary data and answers.

[1068] "Acquisition method" refers to the mechanism by which the server retrieves the identified data from the database.

[1069] "Generation means" refers to a function that generates appropriate answers based on acquired data.

[1070] "Response transmission means" refers to a communication mechanism for sending the generated response to the user's terminal.

[1071] "Display means" refers to a display device that allows the user to check their answers on their terminal.

[1072] "Emotion recognition means" refers to a function that recognizes emotions through the user's facial expressions and voice.

[1073] An "emotion transmission means" is a communication mechanism for sending recognized emotion data to a server.

[1074] "Emotional analysis means" refers to a function that analyzes the emotional data received by the server and adjusts the tone and content of the response.

[1075] This invention provides a system that recognizes the user's emotions and adjusts the tone and content of the response accordingly. This system consists of the following elements:

[1076] Hardware configuration

[1077] 1. Input method: A device or interface through which the user enters a question (e.g., smartphone, tablet, computer).

[1078] 2. Camera and microphone: Devices for capturing the user's facial expressions and voice.

[1079] 3. Communication equipment: Network communication equipment for transmitting question data and sentiment data to the server.

[1080] Software Configuration

[1081] 1. Question Analysis Software: Using a generative AI model installed on the server, the intent of the question and important keywords are analyzed.

[1082] 2. Database access software: Retrieves necessary data from the database on the server.

[1083] 3. Answer generation software: A function that uses a generative AI model to generate answers to questions based on acquired data.

[1084] 4. Emotion Recognition Software: A function that analyzes data acquired from the camera and microphone to recognize the user's emotions (e.g., OpenCV, speech_recognition).

[1085] 5. Sentiment analysis software: A function that analyzes recognized sentiment data and adjusts the tone and content of responses (e.g., sentiment analysis algorithm).

[1086] Processing flow

[1087] 1. Users input questions using a smartphone or computer. At the same time, the camera and microphone capture the user's facial expressions and voice.

[1088] 2. The entered questions and captured sentiment data are sent to the server via the terminal's transmission method.

[1089] 3. The server analyzes the received question and extracts its intent and key keywords (e.g., using generative artificial intelligence).

[1090] 4. Based on the analysis results, the server searches the database and retrieves the necessary data.

[1091] 5. Based on the acquired data, the server's AI generates an appropriate response.

[1092] 6. Simultaneously, the server's sentiment analysis system analyzes the user's sentiment data and adjusts the tone and content of the response.

[1093] 7. The generated response is sent to the user's terminal by the response transmission means, and the user can confirm it through the display means.

[1094] Examples of specific cases and prompt statements

[1095] To illustrate the use of the system, let's consider a scenario where a user has forgotten their password and shows signs of anxiety or frustration:

[1096] Example of a prompt

[1097] The user is expressing anxiety. Please generate appropriate answers to the following questions: I forgot my password.

[1098] In this way, the system can provide quick and appropriate answers while taking the user's feelings into consideration. This improves the user experience and provides a sense of security, especially in emergencies.

[1099] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1100] Step 1:

[1101] The user enters a question.

[1102] Users input questions using their smartphones or computers. During this process, the camera and microphone capture the user's facial expressions and voice. The input device acquires the user's question text and sentiment data, which is then passed on to the next step.

[1103] Step 2:

[1104] The device sends the question and sentiment data to the server.

[1105] The terminal transmits the acquired user question text and sentiment data to the server via internet communication. The input data (question text, sentiment data) is packetized and transferred to the server via the network.

[1106] Step 3:

[1107] The server analyzes the question data and identifies the necessary data.

[1108] The server analyzes the user's question received and extracts its intent and key keywords using generative artificial intelligence. It analyzes the input data (question text), identifies the intent and keywords, and passes them on to the next database search step.

[1109] Step 4:

[1110] The server searches the database and retrieves the necessary data.

[1111] Based on the analysis results, the server searches relevant databases and retrieves the necessary data. Using the input data (intent, keywords), it generates database search queries, extracts appropriate data, and passes it on to the next step.

[1112] Step 5:

[1113] The server analyzes the emotional data.

[1114] The server analyzes the emotional data acquired by the emotion recognition system to identify the user's emotions. It analyzes the input data (emotional data) to identify the user's emotional state (e.g., anxious, excited, calm).

[1115] Step 6:

[1116] The server generates the response data.

[1117] The server generates appropriate responses using a generative AI model based on acquired data and analyzed sentiment data. It generates prompt sentences based on input data (acquired data, sentiment state) and then generates response text based on those prompts.

[1118] Step 7:

[1119] The server sends the generated response to the user's device.

[1120] The server sends the generated response to the terminal. The input data (response text) is packetized and transferred to the user's terminal via the network.

[1121] Step 8:

[1122] The device displays the answer to the user.

[1123] The user's device displays the received responses to the user via a display device. The input data (response text) is analyzed and displayed in a user-friendly format, which the user can then review.

[1124] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1125] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1126] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[1127] [Fourth Embodiment]

[1128] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1129] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1130] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1131] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[1132] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1133] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1134] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1135] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[1136] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1137] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1138] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1139] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1140] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1141] This invention is a system that automatically retrieves necessary data based on questions entered by the user and provides appropriate answers. To properly operate this system, the following basic procedures should be followed.

[1142] User inquiries

[1143] First, the user enters a question into their device. For example, they might enter the question, "Where can I find the latest sales data?" The device is equipped with input methods such as a keyboard or touchscreen, and the user uses these to enter the question.

[1144] Submit a question

[1145] Next, the terminal sends the user's entered question to the server over the network. This is done using a common internet communication protocol (e.g., HTTP). The server then receives the user's question.

[1146] Question analysis

[1147] The server analyzes the received question. The server has a built-in generative artificial intelligence (AI) that analyzes the question content and extracts the intent of the question and necessary keywords. In this step, keywords such as "sales data" and "latest" are extracted.

[1148] Retrieving the relevant information

[1149] Based on the analysis results, the server searches the database to retrieve the relevant information. For example, the server might identify the database folder containing the "latest sales data" and retrieve that information from the database. This database contains the company's sales data and other related data.

[1150] AI-generated answer generation

[1151] Based on the acquired data, the server's AI generates an appropriate response. For example, it might generate a response such as, "The latest sales data is stored in the Q4 2023 folder in SalesDB."

[1152] Submit your response

[1153] The generated response is sent from the server to the user's terminal. This is also done using internet communication protocols.

[1154] Displaying responses to users

[1155] Finally, the terminal receives the response from the server and displays it to the user. This allows the user to quickly obtain the answer, "The latest sales data is stored in the Q4 2023 folder in SalesDB."

[1156] This configuration allows users to efficiently acquire the necessary data and improve the efficiency of their work. Because this system responds quickly and accurately to inquiries such as data storage locations, it is extremely useful in a business environment.

[1157] The following describes the processing flow.

[1158] Step 1:

[1159] The user enters a question into their device. For example, they might type, "Where can I find the latest sales data?" The user enters this question using their device's keyboard or touchscreen.

[1160] Step 2:

[1161] The device stores the user's question as text data and sends this data to the server. Specifically, it uses an HTTP POST request, including the question content in the request body.

[1162] Step 3:

[1163] The server parses the received request and extracts the text data of the question from the request body. The server preprocesses the text data as needed (e.g., tokenization and stop word removal).

[1164] Step 4:

[1165] Generative artificial intelligence (AI) embedded in the server analyzes the question. This AI understands the intent of the question and extracts important keywords. For example, it identifies keywords such as "latest" and "sales data."

[1166] Step 5:

[1167] The server searches the database based on the extracted keywords. For example, it identifies the "latest data" from the database where sales data is stored and retrieves it.

[1168] Step 6:

[1169] Based on the data acquired by the server's AI, natural language generation technology is used to generate appropriate answers. For example, it can generate a specific answer such as, "The latest sales data is stored in the Q4 2023 folder in SalesDB."

[1170] Step 7:

[1171] The server formats the generated response data into an HTTP response format and sends it to the user's terminal. Specifically, it sends the response body containing the response content along with an HTTP status code of 200 OK.

[1172] Step 8:

[1173] The terminal analyzes the response received from the server and extracts the answer data from the response body. The terminal then displays this answer data to the user. For example, the terminal screen might display "The latest sales data is stored in the Q4 2023 folder in SalesDB."

[1174] These steps enable users to quickly obtain the information they need, leading to increased efficiency in their work.

[1175] (Example 1)

[1176] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1177] The objective is to provide an efficient method for users to quickly and accurately obtain specific information. Conventional information retrieval methods require users to spend a lot of time and effort searching for the information they need, resulting in decreased work efficiency. This invention aims to solve these problems by automatically generating the optimal answer to the user's question.

[1178] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1179] In this invention, the server includes an input means for a user to input a question, a transmission means for transmitting the question from the input means to an information processing device, an analysis means for analyzing the question received by the information processing device and identifying the necessary information, an acquisition means for obtaining the information identified by the information processing device from a data storage device, a generation means for generating an appropriate answer based on the information obtained from the acquisition means, an answer transmission means for transmitting the answer generated by the generation means to the user's terminal, and a display means for displaying the answer on the user's terminal. This enables the user to efficiently obtain the necessary information and improve the efficiency of their work.

[1180] "Input means" refers to a device or method for a user to input a question.

[1181] "Transmission means" refers to a device or method for transmitting a question entered by a user to an information processing device.

[1182] An "information processing device" is a computing device or system used to analyze a question, identify necessary information, and generate an answer.

[1183] "Analysis means" refers to a device or method for an information processing device to analyze a question received and identify the necessary information.

[1184] A "data storage device" is a device or system in which necessary information is stored.

[1185] "Acquisition means" refers to the devices or methods by which an information processing device acquires necessary information from a data storage device.

[1186] "Generation means" refers to a device or method for generating an appropriate response based on information obtained from acquisition means.

[1187] "Response transmission means" refers to a device or method for transmitting the response generated by the generation means to the user's terminal.

[1188] "Display means" refers to a device or method for displaying the generated response on the user's terminal.

[1189] This invention relates to a system that automatically acquires necessary data based on questions entered by a user and provides appropriate answers. To properly operate this system, the following configuration and operation are implemented.

[1190] Hardware and Software Overview

[1191] 1. User's terminal

[1192] Hardware used: Personal computers, smartphones, tablets, and other standard input devices (keyboards, touchscreens, etc.)

[1193] Software used: Web browser, mobile application

[1194] 2. Server

[1195] Hardware required: High-performance computing equipment (e.g., server-class machines)

[1196] Software used:

[1197] Question analysis and answer generation: TensorFlow, PyTorch, OpenAI GPT-3, Google BERT

[1198] Database management: MySQL, PostgreSQL

[1199] Processing flow and detailed explanation

[1200] 1. The user enters the question.

[1201] The user enters the question using their own device. For example, they might type "Where can I find the latest sales data?" into the search bar of their web browser.

[1202] 2. Submit your question

[1203] The terminal sends the entered question to the server over the network. The question is sent to the server in JSON format using the HTTP protocol.

[1204] 3. Analysis of the Question

[1205] The server parses the received JSON data and extracts the question. At this stage, generative artificial intelligence (AI) such as TensorFlow or PyTorch is used to identify the intent of the question and keywords. For example, the keywords "sales data" and "latest" might be extracted.

[1206] 4. Obtaining the relevant information

[1207] Based on the analysis results, the server searches for relevant data in databases such as MySQL or PostgreSQL. For example, the server might identify the folder where the "latest sales data" is stored and retrieve the corresponding data.

[1208] 5. Answer generation using AI

[1209] The server's AI generation system (e.g., OpenAI GPT-3, Google BERT) generates appropriate answers based on the acquired data. For example, it might generate an answer such as, "The latest sales data is stored in the Q4 2023 folder in SalesDB."

[1210] 6. Submit your response

[1211] The server converts the generated response back into JSON format and sends it to the user's terminal as an HTTP response.

[1212] 7. Displaying responses to users

[1213] The user's device parses the received JSON data and displays the generated response in a web browser or mobile application. This allows the user to obtain the answer quickly.

[1214] Examples of specific cases and prompt statements

[1215] When a user enters "Where can I find the latest sales data?" into their device, the process proceeds as follows:

[1216] Example of a prompt:

[1217] "The user is asking, 'Where can I find the latest sales data?' Please generate an appropriate answer to this question. The data is in SalesDB, and the most recent data is in the Q4 2023 folder."

[1218] Example of a generated response:

[1219] "The latest sales data is stored in the Q4 2023 folder in SalesDB."

[1220] This system allows users to efficiently obtain the information they need, thereby improving the efficiency of their work.

[1221] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1222] Step 1:

[1223] The user enters the question using their own device. For example, the user might type "Where can I find the latest sales data?" into the search bar of their web browser. This generates the text data for the question.

[1224] Input: A question entered by the user (e.g., "Where can I find the latest sales data?")

[1225] Output: Text data of the question

[1226] Specific actions:

[1227] The user opens a web browser.

[1228] Enter your question in the search bar.

[1229] Click the "Send" button.

[1230] Step 2:

[1231] The terminal sends the user's entered questions to the server via the network. During this process, the text data of the questions is converted to JSON format and sent as an HTTP request.

[1232] Input: Text data of the question on the user's device

[1233] Output: JSON formatted data sent to the server

[1234] Specific actions:

[1235] The terminal converts the question's text data into JSON format.

[1236] Send JSON data to the server as a POST request using the HTTP protocol.

[1237] Step 3:

[1238] The server analyzes the received question. First, the server parses the received JSON data and extracts the question content. Next, it uses generative artificial intelligence (AI) to analyze the intent and keywords of the question.

[1239] Input: Question data in JSON format received by the server

[1240] Output: Analyzed keywords and question intent

[1241] Specific actions:

[1242] The server parses the received JSON data.

[1243] Extract the question content.

[1244] The extracted content is input into a generative AI, which analyzes keywords such as "sales data" and "latest" along with their intent.

[1245] Step 4:

[1246] The server searches the database based on the analysis results. The server queries database management systems such as MySQL and PostgreSQL to retrieve the relevant data.

[1247] Input: Analyzed keywords and question intent

[1248] Output: Retrieved relevant data (e.g., latest sales data)

[1249] Specific actions:

[1250] The server connects to the database.

[1251] Generate and execute SQL queries based on keywords.

[1252] Retrieve the latest sales data from the database.

[1253] Step 5:

[1254] The server's AI generates appropriate answers based on the acquired data. The generated answers are expressed in natural language.

[1255] Input: Retrieved related data

[1256] Output: Generated response (Example: "The latest sales data is stored in the Q4 2023 folder in SalesDB.")

[1257] Specific actions:

[1258] The server generates prompt messages based on the acquired data and inputs them into the AI ​​model.

[1259] The generative AI model generates the answer text.

[1260] Step 6:

[1261] The server converts the generated response back into JSON format and sends it to the user's terminal as an HTTP response.

[1262] Input: Generated answer

[1263] Output: Response data in JSON format sent to the user's device.

[1264] Specific actions:

[1265] The server converts the generated response into JSON format.

[1266] Send JSON data to the user's device using the HTTP protocol.

[1267] Step 7:

[1268] The user's device parses the received JSON data and extracts the response content to display the answer. This allows the response to be displayed on the user's screen.

[1269] Input: Response data in JSON format sent from the server.

[1270] Output: The response displayed on the user's screen (e.g., "The latest sales data is stored in the Q4 2023 folder in SalesDB").

[1271] Specific actions:

[1272] The terminal parses the received JSON data.

[1273] Display the extracted responses in a web browser or application.

[1274] The user checks the answer.

[1275] (Application Example 1)

[1276] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1277] In manufacturing environments, it is crucial for managers to quickly and appropriately obtain production-related information in order to improve production efficiency and solve problems. However, existing systems have limitations in terms of the effort required to manually search for necessary data and the ability to obtain information in real time, making efficient data acquisition and response difficult. Therefore, there is a need for a system that streamlines management operations in the production environment and provides information quickly.

[1278] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1279] In this invention, the server includes an input means for a user to input a question, a transmission means for sending the question from the input means to the server, an analysis means for analyzing the question received by the server and identifying the necessary data, an acquisition means for obtaining the data identified by the server from a database, a generation means for generating an appropriate answer based on the data obtained from the acquisition means, an answer transmission means for sending the answer generated by the generation means to the user's terminal, a display means for displaying the answer on the user's terminal, and a means installed in a work execution device at the manufacturing site for acquiring production-related information in response to inquiries from an administrator and displaying the answer. As a result, administrators can quickly and automatically acquire production-related information at the manufacturing site, and an improvement in work efficiency can be expected.

[1280] A "user" refers to a person who works in the manufacturing or management field and uses the system to obtain information.

[1281] "Input method" refers to interfaces such as keyboards, touchscreens, and voice input devices that users use to input questions.

[1282] "Transmission means" refers to the network communication means used to send the entered question to the server.

[1283] A "server" refers to a central processing unit that analyzes received questions, identifies necessary data, and generates answers using a generative AI.

[1284] "Analysis method" refers to a generative artificial intelligence that extracts the intent and keywords of a question on a server.

[1285] "Acquisition means" refers to the software and hardware used to retrieve data identified by the analysis means from a database.

[1286] "Generation method" refers to software that uses natural language generation technology to generate appropriate responses based on acquired data.

[1287] "Response transmission means" refers to network communication means for transmitting the response generated by the generation means to the user's terminal.

[1288] "Display means" refers to an interface such as a display used to visually display the received response on the user's terminal.

[1289] "Work execution device" refers to robots and automated machinery operating in a manufacturing environment.

[1290] A "manager" refers to a person responsible for acquiring information about the manufacturing site and manufacturing process, and for performing management duties.

[1291] This invention is a system for streamlining information acquisition in manufacturing sites. It automatically acquires necessary data based on user-inputted questions and provides appropriate answers. This system is implemented in the following manner.

[1292] System Configuration

[1293] The system includes a means for user input, a means for sending questions to the server, an analysis and acquisition means by the server, a generation means, an answer transmission means, a display means on the user's terminal, and an information acquisition means installed on the work execution device.

[1294] Program processing

[1295] When a user enters a question into a terminal, the input means receives the question and sends it to the server via the transmission means. The server uses an analysis means (generative artificial intelligence) to analyze the question and extract necessary keywords and intent. Next, the acquisition means retrieves the necessary data from the database based on this extracted information. Subsequently, the generation means generates an appropriate answer using natural language generation technology based on the acquired data.

[1296] The generated answers are sent to the user's terminal via the answer transmission means and displayed to the user by the display means. Furthermore, if a user, acting as an administrator, inputs a question using the information acquisition means installed on the work execution device at the manufacturing site, the data can be automatically acquired and answers provided quickly.

[1297] Hardware and software to be used

[1298] Server: A central processing unit for data analysis and response generation. (Example: High-performance computing server)

[1299] Input methods: Keyboard, touchscreen, voice input device. (Example: Peripherals connected to a Raspberry Pi)

[1300] Generative artificial intelligence: AI models such as Hugging Face Transformers.

[1301] Database: MySQL or other SQL databases.

[1302] Device display method: Display. (Example: Tablet screen)

[1303] Specific example

[1304] For example, suppose a factory manager asks, "What is the current operating status of the production line?" The user enters this question into a terminal via an input device. This question is sent to a server via a transmission device and analyzed by an analysis device (generative artificial intelligence) on the server. The server's acquisition device retrieves information about the current production line from a database, and the generation device generates an answer based on that data. The generated answer will be "The current operating rate is 80%", which is sent to the user's terminal via an answer transmission device and displayed to the manager via a display device.

[1305] Next, here are some examples of prompts for the generated AI model.

[1306] Question: 'Could you tell me the current operating status of your production line?'

[1307] Context: 'Production line A: 80% operational. Production line B: 60% operational. Production line C: 90% operational.'

[1308] This allows users to quickly and accurately obtain the necessary information using an automated system, thereby improving the efficiency of management tasks on the production floor.

[1309] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1310] Step 1:

[1311] The user enters a question into the terminal. Using an input device (keyboard, touchscreen, voice input device), the user enters a question about the production site (e.g., "What is the current operating status of the production line?"). Once the input is complete, the question is sent to the transmission device.

[1312] Input: User's question

[1313] Output: Question data for submission

[1314] Step 2:

[1315] The terminal sends a question to the server via a transmission method. The entered question is sent to the server using a network communication protocol (e.g., HTTP). This process transfers the question data to the server.

[1316] Input: Question data for submission

[1317] Output: Question data received by the server

[1318] Step 3:

[1319] The server analyzes the received question using generative artificial intelligence. The analysis extracts the intent and necessary keywords from the question. For example, the keywords "production line operating status" and "currently" are analyzed.

[1320] Input: Question data received by the server

[1321] Output: Extracted intent and keywords

[1322] Step 4:

[1323] The server searches the database based on the analysis results and retrieves the relevant production information. Using the retrieval method, it searches the database for records that match the specified keywords and obtains the latest operating status of the production line.

[1324] Input: Extracted intent and keywords

[1325] Output: Applicable production information

[1326] Step 5:

[1327] The server generates responses using a generative AI model based on the acquired data. The generation method takes the acquired production information as a prompt and generates an appropriate response using natural language generation technology. For example, a response such as "The current operating rate is 80%" might be generated.

[1328] Input: Relevant production information

[1329] Output: Generated answer

[1330] Step 6:

[1331] The server sends the generated response to the user's terminal via a transmission method. It uses a network communication protocol to send the generated response to the terminal.

[1332] Input: Generated answer

[1333] Output: Response received on the user's device

[1334] Step 7:

[1335] The terminal receives the response and displays it to the user through a display device. The received response is displayed on the screen so that the user can easily confirm it. For example, the terminal's display might show "Current uptime is 80%."

[1336] Input: Response received on the user's device

[1337] Output: The answer displayed on the screen

[1338] This allows users to quickly and automatically obtain critical information from the manufacturing site through the system.

[1339] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1340] This invention combines a system that analyzes user-inputted questions, automatically retrieves necessary data, and provides appropriate answers with a function that recognizes user emotions and adjusts the content of the answers accordingly. To properly operate this system, the following basic procedures should be followed.

[1341] User question reception and sentiment recognition

[1342] First, the user enters a question into their device. For example, they might enter the question, "Where can I find the latest sales data?" Meanwhile, the device is equipped with a camera and microphone, and an emotion recognition system operates to recognize emotions through the user's facial expressions and voice. As the user enters the question, this emotion recognition system analyzes the user's voice and facial expressions and extracts emotion data.

[1343] Sending questions and sentiment data

[1344] Next, the terminal sends the user's entered questions and sentiment data to the server over the network. This procedure allows the server to receive the user's questions and sentiment data.

[1345] Analysis of Questions and Sentiment Data

[1346] The server analyzes the received questions and sentiment data. The server incorporates generative artificial intelligence (AI), which analyzes the question content and sentiment data. The AI ​​understands the intent of the question, extracts important keywords, and adjusts the tone and content of the response based on the sentiment data.

[1347] Retrieving the relevant information

[1348] Based on the analysis results, the server searches the database to retrieve the relevant information. For example, the server identifies the database folder where the "latest sales data" is stored and retrieves that information from the database.

[1349] AI-generated response generation and emotion adjustment

[1350] Based on the acquired data, the server's AI generates an appropriate response. After analyzing the emotional data, the AI ​​adjusts the tone and content of the response. For example, if the user expresses anxiety, the response may include reassuring phrases such as, "Please rest assured."

[1351] Submit your response

[1352] The generated response is sent from the server to the user's terminal.

[1353] Displaying responses to users

[1354] Finally, the terminal receives the response from the server and displays it to the user. This allows the user to quickly receive a response that reflects their own feelings, such as, "The latest sales data is stored in the Q4 2023 folder in SalesDB. Please rest assured."

[1355] This format allows users to obtain information quickly and appropriately, while simultaneously improving the user experience by providing emotionally responsive answers. This system not only contributes to a significant improvement in operational efficiency but also delivers a better user experience by enabling responses that consider the user's emotions.

[1356] The following describes the processing flow.

[1357] Step 1:

[1358] The user enters a question into their device. For example, they might type, "Where can I find the latest sales data?" The user enters the question using the device's keyboard or touchscreen.

[1359] Step 2:

[1360] As soon as the device receives a user's question, it uses its camera and microphone to collect the user's facial expressions and voice data. This data is then analyzed by emotion recognition tools to identify the user's emotions.

[1361] Step 3:

[1362] The device sends the user's question text and analyzed sentiment data to the server. It uses an HTTP POST request, including the question content and sentiment data in the request body.

[1363] Step 4:

[1364] The server analyzes the received request and extracts the question content and sentiment data from the request body. Based on this, the server uses generative artificial intelligence (AI) to identify the intent of the question and keywords.

[1365] Step 5:

[1366] The server searches the database based on the analysis and retrieves the necessary data. For example, it might execute a database query to extract "latest sales data" and identify the relevant data folder (e.g., the Q4 2023 folder).

[1367] Step 6:

[1368] The server generates responses based on the data acquired by the AI. Furthermore, it adjusts the tone and expression of the responses by taking emotional data into consideration. For example, if the user is feeling anxious, the response may include phrases such as, "Please rest assured."

[1369] Step 7:

[1370] The server formats the generated response and sends it to the user's terminal. This is also done in the form of an HTTP response, with the response content included in the response body along with a status code 200 OK.

[1371] Step 8:

[1372] The terminal analyzes the response received from the server and extracts the answer data from the response body. Based on this data, the terminal displays the answer to the user. For example, the terminal screen might display, "The latest sales data is stored in the Q4 2023 folder in SalesDB. Please rest assured."

[1373] This allows users to quickly obtain the information they need and receive emotionally sensitive responses. Through this entire process, the user experience is improved and operations become more efficient.

[1374] (Example 2)

[1375] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1376] Traditional question-answering systems provide appropriate data in response to user questions, but they lack the ability to respond while considering the user's emotions. As a result, they fail to alleviate user anxiety and stress, limiting the user experience. Furthermore, the lack of emotionally balanced responses leads to decreased user satisfaction and impacts the reliability of the system.

[1377] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1378] In this invention, the server includes an input means for the user to input a question, a transmission means for sending the question and emotion data from the input means to the server, an analysis means for analyzing the question and emotion data received by the server and identifying necessary data, an acquisition means for obtaining the data identified by the server from a database, a generation means for generating an appropriate answer including emotion data based on the data obtained from the acquisition means, an answer transmission means for sending the answer generated by the generation means to the user's terminal, and a display means for displaying the answer on the user's terminal. This enables responses that take the user's emotions into consideration, reduces user anxiety and stress, and improves the user experience.

[1379] "Input means" refers to a device or interface for a user to input a question.

[1380] A "transmission method" refers to a device or protocol that has the functionality to send questions and sentiment data to a server over a network.

[1381] "Analysis means" refers to software or algorithms used by a server to analyze received questions and sentiment data to identify necessary data and answers.

[1382] "Acquisition means" refers to a program or interface that has the function of retrieving the necessary data identified by the analysis means from a database.

[1383] "Generation means" refers to natural language generation technologies and algorithms that generate appropriate responses based on data obtained from acquisition means, while also considering emotional data.

[1384] "Response transmission means" refers to the network interface or protocol used to send the generated response to the user's terminal.

[1385] "Display means" refers to a display or UI component on the user's device used to display the received response.

[1386] This invention is a system that analyzes user-inputted questions, automatically acquires necessary data, and provides appropriate answers, further incorporating a function that recognizes the user's emotions and adjusts the content of the answers accordingly. To properly operate this system, the following basic hardware and software are required.

[1387] Hardware to use

[1388] User terminals include personal computers, smartphones, and tablets, which are equipped with cameras and microphones.

[1389] Server: A high-performance computer system that holds the database and the generative AI models.

[1390] Software to use

[1391] Emotion recognition software: This software analyzes input from the device's camera and microphone to extract emotional data from the user's facial expressions and voice. For this purpose, the use of Python's OpenCV and biometric software is recommended.

[1392] Generative AI Model: A generative artificial intelligence (such as GPT-4) that runs on a server to extract the intent and keywords of a question and generate an answer.

[1393] Database system: A database management system, such as Microsoft SQL Server, is used to store the necessary data.

[1394] Specific examples of the system

[1395] 1. The user enters the question "Where can I find the latest sales data?" into the device. At this time, the device's camera and microphone record the user's facial expressions and voice, and emotion recognition software extracts emotion data.

[1396] 2. The terminal sends the entered question and sentiment data to the server in JSON format. An example of the content sent will be in the following format:

[1397] {

[1398] "Question": "Where can I find the latest sales data?"

[1399] "Emotion": "Anxiety"

[1400] }

[1401] 3. The server analyzes the received question and sentiment data. A generative AI model is used to extract the intent of the question and key keywords (e.g., "latest," "sales data," "where," etc.). The analysis results can also be viewed in the form of a prompt message like the following:

[1402] User's question: "Where can I find the latest sales data?"

[1403] User's emotion: "Anxiety"

[1404] 4. The server searches the database and executes a specific SQL query to retrieve the relevant data. For example, it executes a query like this:

[1405] SELECT FROM SalesDB.QuarterlySales WHERE Quarter='2023Q4';

[1406] 5. The server uses a generative AI model based on the acquired data to generate appropriate responses and adjusts the tone of the responses based on sentiment data. For example, it might add the phrase "Please rest assured." to the response.

[1407] 6. The server sends the generated response to the terminal in JSON format, and the terminal receives and displays it.

[1408] Examples of specific responses:

[1409] The latest sales data is stored in the Q4 2023 folder in SalesDB. Please rest assured.

[1410] Through the process described above, users can quickly obtain appropriate answers that address their questions and feelings. Furthermore, this system can reduce user anxiety and stress, improving the user experience.

[1411] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1412] Step 1:

[1413] The user enters a question into the terminal. For example, "Where can I find the latest sales data?" The terminal receives the input as text data and simultaneously records the user's facial expressions and voice using the camera and microphone. The input data includes the question text and audio / video data. The output consists of text data and multimedia data.

[1414] Step 2:

[1415] The device performs emotion recognition using the input question text, recorded audio, and video data. Here, we use Python's OpenCV and an emotion recognition algorithm to extract emotion data from the user's facial expressions and voice. Inputs include the user's question text, facial image, and audio data. Output is emotion data (e.g., "anxiety").

[1416] Step 3:

[1417] The terminal packages the question text and sentiment data in JSON format and sends it to the server over the network. The input consists of the question text and sentiment data, and the output is the data converted to JSON format (e.g., {"Question": "Where can I find the latest sales data?", "Sentiment": "Anxious"}).

[1418] Step 4:

[1419] The server parses the received JSON data. First, it separates the question text from the sentiment data and uses a generative AI model (e.g., GPT-4) to extract the intent of the question and keywords. The input is JSON data, and the output is the parsed intent of the question and keywords (e.g., "latest", "sales data", "where").

[1420] Step 5:

[1421] The server searches the database based on the retrieved intent and keywords. Here, it uses an SQL query to retrieve the relevant data from the "SalesDB" database in Microsoft SQL Server. The input is the parsed keywords, and the output is sales data retrieved from the database (e.g., "Sales data for Q4 2023").

[1422] Step 6:

[1423] The server generates responses using a generative AI model based on acquired data and sentiment data. The tone of the response is adjusted based on the sentiment data. For example, for the sentiment data of "anxiety," the phrase "Please rest assured" is added. The input consists of data and sentiment data acquired from a database, and the output is the adjusted response text (e.g., "The latest sales data is stored in the Q4 2023 folder in SalesDB. Please rest assured.").

[1424] Step 7:

[1425] The server sends the generated response text to the user's terminal in JSON format. The input is the generated response text, and the output is data sent to the terminal in JSON format.

[1426] Step 8:

[1427] The terminal parses the JSON data received from the server and extracts the answer text. Next, the extracted text is displayed on the terminal's screen. The input is the JSON data received from the server, and the output is the answer text displayed in a format visible to the user. The user can then verify this and obtain the answer to the question.

[1428] (Application Example 2)

[1429] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1430] Traditional question-answering systems have struggled to respond appropriately to user emotions, making it difficult to improve the user experience. Furthermore, especially in security-related emergencies, users often exhibit anxiety and impatience, which can hinder appropriate responses. Such situations increase the risk of decreased user satisfaction. Therefore, there is a need for a system that can respond appropriately to user emotions and improve the user experience.

[1431] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1432] In this invention, the server includes emotion recognition means for recognizing the user's emotions, emotion transmission means for transmitting the emotions recognized by the emotion recognition means to the server, and emotion analysis means for analyzing the emotions and adjusting the tone and content of the response. This makes it possible to provide a response that corresponds to the user's emotions.

[1433] An "input method" refers to a device or interface used by a user to input a question.

[1434] "Transmission means" refers to a communication mechanism for sending the entered question to the server.

[1435] "Analysis means" refers to a function that analyzes the questions received by the server and identifies the necessary data and answers.

[1436] "Acquisition method" refers to the mechanism by which the server retrieves the identified data from the database.

[1437] "Generation means" refers to a function that generates appropriate answers based on acquired data.

[1438] "Response transmission means" refers to a communication mechanism for sending the generated response to the user's terminal.

[1439] "Display means" refers to a display device that allows the user to check their answers on their terminal.

[1440] "Emotion recognition means" refers to a function that recognizes emotions through the user's facial expressions and voice.

[1441] An "emotion transmission means" is a communication mechanism for sending recognized emotion data to a server.

[1442] "Emotional analysis means" refers to a function that analyzes the emotional data received by the server and adjusts the tone and content of the response.

[1443] This invention provides a system that recognizes the user's emotions and adjusts the tone and content of the response accordingly. This system consists of the following elements:

[1444] Hardware configuration

[1445] 1. Input method: A device or interface through which the user enters a question (e.g., smartphone, tablet, computer).

[1446] 2. Camera and microphone: Devices for capturing the user's facial expressions and voice.

[1447] 3. Communication equipment: Network communication equipment for transmitting question data and sentiment data to the server.

[1448] Software Configuration

[1449] 1. Question Analysis Software: Using a generative AI model installed on the server, the intent of the question and important keywords are analyzed.

[1450] 2. Database access software: Retrieves necessary data from the database on the server.

[1451] 3. Answer generation software: A function that uses a generative AI model to generate answers to questions based on acquired data.

[1452] 4. Emotion Recognition Software: A function that analyzes data acquired from the camera and microphone to recognize the user's emotions (e.g., OpenCV, speech_recognition).

[1453] 5. Sentiment analysis software: A function that analyzes recognized sentiment data and adjusts the tone and content of responses (e.g., sentiment analysis algorithm).

[1454] Processing flow

[1455] 1. Users input questions using a smartphone or computer. At the same time, the camera and microphone capture the user's facial expressions and voice.

[1456] 2. The entered questions and captured sentiment data are sent to the server via the terminal's transmission method.

[1457] 3. The server analyzes the received question and extracts its intent and key keywords (e.g., using generative artificial intelligence).

[1458] 4. Based on the analysis results, the server searches the database and retrieves the necessary data.

[1459] 5. Based on the acquired data, the server's AI generates an appropriate response.

[1460] 6. Simultaneously, the server's sentiment analysis system analyzes the user's sentiment data and adjusts the tone and content of the response.

[1461] 7. The generated response is sent to the user's terminal by the response transmission means, and the user can confirm it through the display means.

[1462] Examples of specific cases and prompt statements

[1463] To illustrate the use of the system, let's consider a scenario where a user has forgotten their password and shows signs of anxiety or frustration:

[1464] Example of a prompt

[1465] The user is expressing anxiety. Please generate appropriate answers to the following questions: I forgot my password.

[1466] In this way, the system can provide quick and appropriate answers while taking the user's feelings into consideration. This improves the user experience and provides a sense of security, especially in emergencies.

[1467] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1468] Step 1:

[1469] The user enters a question.

[1470] Users input questions using their smartphones or computers. During this process, the camera and microphone capture the user's facial expressions and voice. The input device acquires the user's question text and sentiment data, which is then passed on to the next step.

[1471] Step 2:

[1472] The device sends the question and sentiment data to the server.

[1473] The terminal transmits the acquired user question text and sentiment data to the server via internet communication. The input data (question text, sentiment data) is packetized and transferred to the server via the network.

[1474] Step 3:

[1475] The server analyzes the question data and identifies the necessary data.

[1476] The server analyzes the user's question received and extracts its intent and key keywords using generative artificial intelligence. It analyzes the input data (question text), identifies the intent and keywords, and passes them on to the next database search step.

[1477] Step 4:

[1478] The server searches the database and retrieves the necessary data.

[1479] Based on the analysis results, the server searches relevant databases and retrieves the necessary data. Using the input data (intent, keywords), it generates database search queries, extracts appropriate data, and passes it on to the next step.

[1480] Step 5:

[1481] The server analyzes the emotional data.

[1482] The server analyzes the emotional data acquired by the emotion recognition system to identify the user's emotions. It analyzes the input data (emotional data) to identify the user's emotional state (e.g., anxious, excited, calm).

[1483] Step 6:

[1484] The server generates the response data.

[1485] The server generates appropriate responses using a generative AI model based on acquired data and analyzed sentiment data. It generates prompt sentences based on input data (acquired data, sentiment state) and then generates response text based on those prompts.

[1486] Step 7:

[1487] The server sends the generated response to the user's device.

[1488] The server sends the generated response to the terminal. The input data (response text) is packetized and transferred to the user's terminal via the network.

[1489] Step 8:

[1490] The device displays the answer to the user.

[1491] The user's device displays the received responses to the user via a display device. The input data (response text) is analyzed and displayed in a user-friendly format, which the user can then review.

[1492] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1493] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1494] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[1495] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1496] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[1497] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[1498] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[1499] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[1500] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[1501] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[1502] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[1503] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[1504] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[1505] 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.

[1506] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[1507] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[1508] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[1509] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[1510] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[1511] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[1512] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[1513] The following is further disclosed regarding the embodiments described above.

[1514] (Claim 1)

[1515] An input method for the user to enter a question,

[1516] A transmission means that sends a question from the input means to the server,

[1517] The aforementioned server analyzes the received question and identifies the necessary data;

[1518] A means for obtaining the data identified by the aforementioned server from a database,

[1519] A generation means that generates an appropriate response based on the data obtained by the acquisition means,

[1520] A response transmission means that transmits the response generated by the generation means to the user's terminal,

[1521] A display means for displaying on the user's terminal,

[1522] A system that includes this.

[1523] (Claim 2)

[1524] The system according to claim 1, wherein the analysis means includes a method for extracting the intent of a question and keywords using generative artificial intelligence.

[1525] (Claim 3)

[1526] The system according to claim 1, wherein the generation means uses natural language generation technology to generate a response based on data obtained from the acquisition means.

[1527] "Example 1"

[1528] (Claim 1)

[1529] An input method for the user to enter a question,

[1530] A transmission means for transmitting a question from the input means to an information processing device,

[1531] The aforementioned information processing device analyzes the received question and identifies the necessary information;

[1532] The information processing device includes an acquisition means for acquiring information identified by the information processing device from a data storage device,

[1533] A generation means that generates an appropriate response based on the information obtained by the acquisition means,

[1534] A response transmission means that transmits the response generated by the generation means to the user's terminal,

[1535] A display means for displaying on the user's terminal,

[1536] A system that includes this.

[1537] (Claim 2)

[1538] The system according to claim 1, wherein the analysis means includes a method for extracting the intent of a question and keywords using generative artificial intelligence.

[1539] (Claim 3)

[1540] The system according to claim 1, wherein the generation means uses natural language generation technology to generate a response based on information obtained from the acquisition means.

[1541] "Application Example 1"

[1542] (Claim 1)

[1543] An input method for the user to enter a question,

[1544] A transmission means that sends a question from the input means to the server,

[1545] The aforementioned server analyzes the received question and identifies the necessary data;

[1546] A means for obtaining the data identified by the aforementioned server from a database,

[1547] A generation means that generates an appropriate response based on the data obtained by the acquisition means,

[1548] A response transmission means that transmits the response generated by the generation means to the user's terminal,

[1549] A display means for displaying on the user's terminal,

[1550] A means of obtaining production-related information and displaying responses in response to inquiries from managers, installed on the work execution device in the manufacturing area.

[1551] A system that includes this.

[1552] (Claim 2)

[1553] The system according to claim 1, wherein the analysis means includes a method for extracting the intent of a question and keywords using generative artificial intelligence.

[1554] (Claim 3)

[1555] The system according to claim 1, wherein the generation means uses natural language generation technology to generate a response based on data obtained from the acquisition means.

[1556] "Example 2 of combining an emotion engine"

[1557] (Claim 1)

[1558] An input method for the user to enter a question,

[1559] A transmission means that sends questions and emotion data from the input means to a server,

[1560] The server analyzes the received questions and sentiment data, and includes an analysis means for identifying necessary data.

[1561] A means for obtaining the data identified by the aforementioned server from a database,

[1562] A generation means that generates an appropriate response, including emotional data, based on the data obtained by the acquisition means,

[1563] A response transmission means that transmits the response generated by the generation means to the user's terminal,

[1564] A display means for displaying on the user's terminal,

[1565] A system that includes this.

[1566] (Claim 2)

[1567] The system according to claim 1, wherein the analysis means includes a method for extracting the intent and keywords of a question and analyzing sentiment data using generative artificial intelligence.

[1568] (Claim 3)

[1569] The system according to claim 1, wherein the generation means includes a method for generating a response based on data obtained from the acquisition means and sentiment data, using natural language generation technology.

[1570] "Application example 2 when combining with an emotional engine"

[1571] (Claim 1)

[1572] An input method for the user to enter a question,

[1573] A transmission means that sends a question from the input means to the server,

[1574] The aforementioned server analyzes the received question and identifies the necessary data;

[1575] A means for obtaining the data identified by the aforementioned server from a database,

[1576] A generation means that generates an appropriate response based on the data obtained by the acquisition means,

[1577] A response transmission means that transmits the response generated by the generation means to the user's terminal,

[1578] A display means for displaying on the user's terminal,

[1579] A means of recognizing the user's emotions,

[1580] An emotion transmission means that transmits the emotion recognized by the emotion recognition means to a server,

[1581] An emotion analysis means for analyzing the aforementioned emotions and adjusting the tone and content of the response,

[1582] A system that includes this.

[1583] (Claim 2)

[1584] The system according to claim 1, wherein the analysis means includes a method for extracting the intent of a question and keywords using generative artificial intelligence.

[1585] (Claim 3)

[1586] The system according to claim 1, which includes a method for generating a response using natural language generation technology based on data obtained from the acquisition means and emotion data analyzed by the emotion analysis means. [Explanation of symbols]

[1587] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. An input method for the user to enter a question, A transmission means that sends a question from the input means to the server, The aforementioned server analyzes the received question and identifies the necessary data; A means for obtaining the data identified by the aforementioned server from a database, A generation means that generates an appropriate response based on the data obtained by the acquisition means, A response transmission means that transmits the response generated by the generation means to the user's terminal, A display means for displaying on the user's terminal, A system that includes this.

2. The system according to claim 1, wherein the analysis means includes a method for extracting the intent of a question and keywords using generative artificial intelligence.

3. The system according to claim 1, wherein the generation means uses natural language generation technology to generate a response based on data obtained from the acquisition means.

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