system

Abstract

Provide a system. 【Solution means】 Means for receiving unstructured data including unclear points, Means for analyzing the received unstructured data and extracting relevant information, Means for searching past materials based on the extracted relevant information, Means for generating a plurality of information sets based on the search results, Means for assigning reference information to each generated information set, Means for presenting a plurality of information sets to the user, Means for displaying an information set on a device for controlling an industrial automation machine, A system including the above.

Description

Technical Field

[0004] , , , ,

[0005] , , , , , ,

[0001] The technology of this 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, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In business operations, rapid acquisition and appropriate response to various types of information, such as understanding of technical terms and exploration of countermeasures based on past cases, are required. However, there is a problem that it takes a lot of time to search for and judge necessary information individually. Furthermore, although the reliability of information and the presentation of reference sources are also important, it is inefficient for users to do these by themselves. There is a need for means to efficiently solve such problems related to information search and judgment.

Means for Solving the Problems

[0005] This invention provides a means for receiving questions containing unclear points, analyzing that information, and extracting relevant information. Furthermore, it provides a means for searching past materials based on the extracted information and generating multiple answers from the search results. Each answer is accompanied by references to enhance reliability, and by presenting these to the user, the system provides a way to quickly and efficiently obtain appropriate information and its references. This enables users to verify information with less effort and make quick decisions.

[0006] "Unclear points" refer to matters or situations that users cannot understand in the course of their work.

[0007] A "question" is a question that a user enters into a system to seek information or find a solution.

[0008] "Analysis" is the process of structurally understanding a received question and extracting relevant information and keywords.

[0009] "Related information" refers to information from past documents and data that is relevant to the question received.

[0010] "Searching" is the process of finding appropriate documents and information from internal or external databases based on the relevant information that has been extracted.

[0011] An "answer" is an explanation or suggestion of the information or solution that the user is looking for, generated based on the search results.

[0012] A "reference" indicates the source of the generated answer and is provided as a reference for users to verify the accuracy of the information.

[0013] "Presentation" refers to the action of displaying generated information or answers to the user, allowing them to select or confirm options. [Brief explanation of the drawing]

[0014] [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 a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of 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 Example 2 when an 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 an emotion engine is combined.

Embodiments for Carrying Out the Invention

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

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

[0017] In the following embodiments, a labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include 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.

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

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

[0020] 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).

[0021] 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."

[0022] [First Embodiment]

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

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0029] 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.

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

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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".

[0035] This invention is an information provision system for efficiently addressing unclear points in business operations. It searches for and presents appropriate information based on questions entered by the user. The following describes the program processing of this system in detail.

[0036] Users input questions by describing unclear points in their work using natural language via their terminal. This input is sent to the server using natural language processing technology. The server analyzes the received questions, extracts relevant keywords and context, and uses this information to access internal databases and knowledge bases to search for relevant documents and past cases.

[0037] Once the search is complete, the server generates multiple answers based on the retrieved information. Using generation AI, it constructs multiple answers in natural language based on the selected information. Each answer is accompanied by references as sources and bibliographies, allowing users to verify the accuracy and origin of the content.

[0038] The terminal displays the answers received from the server to the user, assisting the user in making selections and decisions based on that information. For example, if a user asks a question about troubleshooting a specific piece of business equipment, the server will provide technical documentation related to that equipment, past troubleshooting cases, and solutions based on them. This allows the user to quickly select the appropriate solution and address the problem.

[0039] This system allows users to efficiently obtain information in response to their work-related questions and accelerate decision-making. The collaboration between servers, terminals, and users improves operational efficiency and optimizes information utilization.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The user enters a question about something they don't understand into the device using natural language. The device receives this input and prepares to send the data to the server.

[0043] Step 2:

[0044] The server utilizes natural language processing techniques to analyze user questions received from terminals. This involves understanding the content of the questions and extracting relevant keywords and context.

[0045] Step 3:

[0046] The server searches internal databases and external knowledge bases based on the extracted keywords and context. This search identifies relevant technical documents and past case information.

[0047] Step 4:

[0048] The server initiates a process to generate multiple answers based on relevant information. Using generative AI, it constructs answers in natural language from the acquired information.

[0049] Step 5:

[0050] The server provides references to the source of each answer it generates. This allows users to verify the reliability and source of the information provided.

[0051] Step 6:

[0052] The server sends the generated answer and its references to the terminal. The terminal receives this and prepares to present it to the user.

[0053] Step 7:

[0054] Users can view and review multiple responses presented through their device. They can then select the most appropriate solution to aid in decision-making in their work.

[0055] (Example 1)

[0056] 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."

[0057] In today's business environment, users are required to obtain various information quickly and accurately to make decisions. However, there is a lack of efficient means to quickly search for appropriate information in response to questions that may contain uncertainties, and to provide it in an easily understandable format. This problem is particularly serious in the current situation where business processes are becoming more complex and the volume of data is increasing.

[0058] 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.

[0059] In this invention, the server includes means for analyzing received information, including unknown points, using natural language processing technology to extract important features; means for searching for knowledge resources based on the extracted important features; and means for creating multiple answers using a generative AI model based on the search results. This enables users to obtain answers to their questions quickly and efficiently.

[0060] "Information containing unclear points" refers to documents or data that express issues or questions that users do not fully understand due to a lack of knowledge or insufficient verification.

[0061] "Natural language processing technology" is a general term for the technologies and methods that enable computers to understand, analyze, and process human language.

[0062] "Key features" refer to keywords, contexts, or other relevant information that are deemed particularly relevant from the information presented.

[0063] "Knowledge resources" refer to data, documents, case studies, and databases or other means of information aggregation that store them in a specific domain.

[0064] A "generative AI model" is an algorithm or system that uses artificial intelligence technology to perform pre-training and possesses the ability to generate natural language responses or answers to new information.

[0065] "Source information" refers to the information sources and documents referenced by the generated answer, and is information used to demonstrate the reliability of the information.

[0066] "Information and communication equipment" is a general term for devices and equipment that transmit and receive data, and includes computers, smartphones, tablets, etc.

[0067] This invention is an information provision system that provides quick and appropriate responses to unclear points in business operations. Its purpose is to provide appropriate information tailored to the user's question, which is entered in natural language.

[0068] Users input their questions in natural language using their device and send the information to the server. This device can be a standard computer, smartphone, or tablet. The input information is transferred from the device to the server using a secure communication method (e.g., HTTPS).

[0069] The server analyzes the received information. This analysis utilizes natural language processing techniques, and well-known libraries such as NLTK and spaCy can be used. The server extracts key features from the information and searches for knowledge resources based on them. The databases used here are assumed to be internal knowledge bases or cloud-based databases.

[0070] The server generates multiple answers using a generative AI model based on information that matches the extracted key features. In this scenario, OpenAI's GPT series is a representative generative AI model. Each generated answer is accompanied by source information, which allows the user to be confident in the reliability of the information.

[0071] The terminal's role is to quickly display the answers received from the server to the user. Based on the presented answers, the user can select the most suitable solution to the problem and apply it to their work.

[0072] As a concrete example, if a user wants to know the "installation procedure for new software," they send the question to the server via their terminal. The server analyzes this question, searches relevant installation manuals and FAQ databases, and generates an answer in natural language using a generative AI model. The generated answer is then presented to the user along with the source of the information.

[0073] An example of a prompt message would be, "Please tell me the general troubleshooting steps when the new business software encounters an error."

[0074] In this way, users can obtain the necessary information quickly and accurately, leading to improved work efficiency and faster decision-making.

[0075] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0076] Step 1:

[0077] The user uses a terminal to input questions in natural language. The terminal receives the input and formats it as text data. After checking whether the input is properly structured and performing error checks, it sends the data to the server to proceed to the next step.

[0078] Step 2:

[0079] The server receives text data sent from the terminal. Here, natural language processing techniques are used to analyze the content of the question. This analysis extracts key keywords and relevant contextual information. For example, if the question contains the phrase "software installation," the software name and error message will also be extracted.

[0080] Step 3:

[0081] The server searches for knowledge resources based on the extracted keywords and contextual information. In this step, it executes search queries against internal knowledge bases and external databases to retrieve relevant information and documents. The information is compiled into a dataset and passed on to the next process.

[0082] Step 4:

[0083] The server uses the acquired information as input to generate answers using a generative AI model. This process organizes and integrates the acquired relevant information, creating multiple answers in natural language. Each answer includes source information to ensure the reliability of the information.

[0084] Step 5:

[0085] The generated answer is sent from the server to the terminal. The terminal receives the answer and presents it to the user via the user interface. The interface layout is dynamically adjusted to allow the user to easily view the answer.

[0086] Step 6:

[0087] Users review multiple displayed solutions and select the one best suited to their problem. After this selection, users can then take concrete action. This enables rapid and effective problem-solving.

[0088] (Application Example 1)

[0089] 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."

[0090] In modern industrial settings, the increasing complexity of machinery necessitates immediate action when uncertainties or malfunctions occur. However, conventional information systems have struggled to provide the necessary information quickly and appropriately to resolve problems with industrial automated machinery. This has hindered improvements in work efficiency in industrial settings.

[0091] 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.

[0092] In this invention, the server includes means for analyzing unstructured data specific to industry and extracting relevant information, means for searching for relevant materials based on the extracted information, and means for displaying the information set on a device that controls industrial automated machinery. This enables rapid and accurate problem solving in industrial settings.

[0093] "Unstructured data" refers to data that does not have a fixed format or structure, and includes text and audio data written in natural language.

[0094] "Analysis" is the process of breaking down received data and identifying the information and patterns contained within it.

[0095] "Related information" refers to additional data or knowledge related to the content of the received data, and may include solutions or reference materials.

[0096] An "information set" is a collection of information obtained through searching or generation, including data and guidelines that are useful for solving problems.

[0097] "Reference information" refers to information added to indicate the reliability or source of an information set, and includes information about the source and author.

[0098] "Industrial automated machinery" refers to automated devices and systems used in manufacturing, factories, and other industrial settings.

[0099] A "control device" is an electronic device used to manage the operation of machinery and equipment and to transmit instructions.

[0100] A "display device" is a device used by humans to visually confirm information and data, and includes screens and displays.

[0101] The system for implementing this invention begins with a user describing their questions in order to solve a problem in an industrial automated machine. The questions and concerns entered by the user are sent to a control device. This device analyzes the data using natural language processing technology and extracts relevant information.

[0102] The server processes the extracted relevant information to search for past documents. At this stage, it accesses an internal database and generates the most relevant set of information based on specific keywords and context. Reference information is added to the generated set to ensure the accuracy of the information.

[0103] The server transmits a set of information to a display device that controls industrial automated machinery, displaying it in real time. This allows users to immediately access the information needed to solve problems on-site. For example, if a machine suddenly stops on a production line, a user can ask, "What caused the machine to stop and how can I get it working again?" and relevant technical documentation and troubleshooting procedures will be displayed.

[0104] The hardware used includes control devices and displays for industrial automated machinery, while the software includes the natural language processing library spaCy and the generative AI model GPT-3(registered trademark).

[0105] As a concrete example, the prompt might be entered as, "Please tell me about possible problems that can occur during the assembly process of product X and how to address them." Based on this prompt, the server analyzes the relevant information and presents the optimal solution.

[0106] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0107] Step 1:

[0108] The user inputs any questions or uncertainties into the control device. These are entered in natural language text format. Receiving this input data prepares the device for the next step.

[0109] Step 2:

[0110] The server analyzes the unclear points received from the user using natural language processing techniques. Specifically, it uses spaCy to tokenize the text data and extract keywords and important phrases. This process generates a dataset for searching for relevant information.

[0111] Step 3:

[0112] The server searches its internal database using the keywords extracted in step 2 to find relevant documents. The retrieved data includes documents detailing past troubleshooting cases and solutions. This builds a knowledge base capable of addressing user questions.

[0113] Step 4:

[0114] The server generates an information set based on relevant materials. Using the GPT-3 generation AI model, it creates multiple natural-sounding answers to the user's questions. The information set also includes reference information indicating the accuracy of the answers. This output is then converted into a format that the user can visually recognize.

[0115] Step 5:

[0116] The server transmits the generated information set to a display device on the control device. The information set is immediately visualized on the display device, allowing the user to easily review the information. Based on the provided information, the user can solve problems related to industrial automated machinery and make rapid decisions.

[0117] 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.

[0118] This invention is an information provision system for quickly addressing unclear points and questions in business operations, and further incorporates an emotion engine that recognizes and responds to the user's emotions. The purpose of this invention is for the user to input a question into the system, which will then provide appropriate information in response to that question, as well as respond in accordance with the user's emotions.

[0119] The user inputs questions about work-related issues into the terminal. Before sending the user's question to the server, the terminal's built-in sentiment engine analyzes the user's emotional state from the input text. The sentiment engine recognizes the emotion from the text of the user's question and provides metadata corresponding to that emotion to the server.

[0120] The server uses natural language processing techniques to analyze the received question, extracts relevant data, and then searches its internal database to gather relevant information. In this process, the server takes metadata from the sentiment engine into consideration to generate a response that is appropriate to the user's emotions. For example, if the sentiment engine determines that the user is confused, the server will generate a more polite and detailed response.

[0121] The generated answers are accompanied by references that verify the reliability of the information sources. Furthermore, the priority of these references is adjusted based on the results of the sentiment engine's analysis, allowing users to confidently verify the information.

[0122] Finally, the terminal presents the user with the server-generated answers and references. By referring to the information provided in a tone and content that matches the user's emotions, the user can efficiently solve business problems. For example, if the server analyzes that the user is in a hurry, it will quickly respond to the user's needs by providing an immediate and concise answer.

[0123] This system helps improve work efficiency by providing a more personalized experience through flexible responses that respond to the user's emotions.

[0124] The following describes the processing flow.

[0125] Step 1:

[0126] The user uses a terminal to input work-related questions or uncertainties in natural language. Upon receiving this input, the terminal activates an emotion engine to analyze the user's emotions from the entered text.

[0127] Step 2:

[0128] The device obtains analysis results regarding the user's emotions and sends them to the server along with the question text. The emotion data is used by the server as supplementary information to understand the context of the question.

[0129] Step 3:

[0130] The server analyzes the received question using natural language processing techniques. It extracts the question's subject and related keywords, and then searches its internal database based on this information to gather relevant data.

[0131] Step 4:

[0132] Based on the information collected by the server, a generative AI is used to create multiple responses. In this process, the server takes into account the emotional data sent from the terminal and creates responses that adjust in tone and complexity according to the user's emotions.

[0133] Step 5:

[0134] The server assigns a source reference to each response it generates. Based on sentiment data, the priority of the references is also adjusted as needed.

[0135] Step 6:

[0136] The server sends the generated response and its references to the terminal. The terminal receives this and displays the appropriately formatted response to the user.

[0137] Step 7:

[0138] Users can review the answers presented through their device and refer to references and additional information as needed. Based on the information provided, users can make quick decisions.

[0139] (Example 2)

[0140] 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".

[0141] The problem that this invention aims to solve is to provide prompt and accurate information regarding unclear points and questions in business operations, as well as to achieve appropriate communication that responds to the user's emotions. Conventional information provision systems lack flexible responses based on the user's emotions, which can result in a decrease in the quality of the user experience and the efficiency of operations.

[0142] 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.

[0143] In this invention, the server includes means for analyzing the sentiment of a question, means for generating metadata based on the sentiment analysis results, and means for analyzing the question and metadata and extracting relevant information. This makes it possible to provide information that takes the user's emotions into account, providing a more personalized experience tailored to each individual user and improving operational efficiency.

[0144] A "means of receiving questions" refers to a method for obtaining text input from the user and an interface for passing that input to the next processing step.

[0145] "Means of analyzing emotions" refers to algorithms and technologies used to identify a user's emotions from input text, utilizing natural language processing and sentiment analysis techniques.

[0146] "Methods for generating metadata" refer to supplementary data constructed based on the results of sentiment analysis, and are part of the information used when generating responses.

[0147] "Means of extracting relevant information" refers to methods of retrieving necessary information from databases and resources based on the user's questions and sentiments.

[0148] "Means of searching records" refers to processes or techniques for finding information related to a question, whether within internal or external data storage.

[0149] "Means of generating answers" refers to the process of assembling and displaying information appropriate to a user's question, based on search results and sentiment information.

[0150] "Means of providing data that proves the source of information" refers to methods of providing additional information that indicates the source of the information in order to guarantee the reliability of the information being provided.

[0151] "Means of presenting answers" refers to methods of displaying generated information and data that proves its reliability to the user, and which are adjusted to respond to the user's emotions.

[0152] This invention is an information provision system that addresses users' work-related uncertainties and questions, and is equipped with a function to recognize and respond to the user's emotions. The system is implemented using the following hardware and software.

[0153] Hardware and software configuration

[0154] The terminal is equipped with an emotion engine to receive user input and perform sentiment analysis. The server has the processing power to analyze received questions using natural language processing technology. This uses a specific generative AI model. An internal database provides high-speed search functionality to support information retrieval. The emotion engine and natural language processing technology are implemented with specialized algorithms and machine learning models that can analyze sentiment and meaning from text-based input.

[0155] Data processing and calculations

[0156] The terminal receives user input text, preprocesses it, and performs sentiment analysis using a sentiment engine. The server uses natural language processing algorithms to analyze the content of the question and extracts relevant information from the database. In this process, the sentiment analysis results are used as metadata to generate a response that corresponds to the user's emotions. The generated response is given a reference to prove its reliability.

[0157] Specific example

[0158] As a concrete example, consider a scenario where a user enters a question such as, "I don't know how to proceed with the new project." In this case, the sentiment engine analyzes that the user is confused. Based on this, the server generates a detailed and thorough explanation of how to proceed, and presents it with references to reliable sources. Conversely, if the analysis indicates that the user is in a hurry, a concise and to-the-point answer is provided immediately.

[0159] Example of a prompt

[0160] "I'm confused about how to proceed with the project. Could you please explain the detailed steps?"

[0161] This system aims to provide a better user experience by taking user emotions into consideration and delivering information quickly and accurately.

[0162] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0163] Step 1:

[0164] Users input work-related questions or uncertainties into a terminal. This input is in text format and includes the user's problems and information requests. The terminal passes the input text to a sentiment engine. This engine analyzes the input text and uses natural language processing techniques to identify the emotional state. As a result, metadata indicating the user's emotions is generated.

[0165] Step 2:

[0166] The device sends the metadata obtained from sentiment analysis and the original question to the server. The server analyzes the received question using natural language processing algorithms to extract the main theme and related keywords. This analysis step involves a generative AI model, which provides an appropriate structural understanding of the question. The output is structured data of the question.

[0167] Step 3:

[0168] The server uses the structured data of the question to search its internal database and extract relevant information. Here, the database search is performed based on keywords and themes to identify the information most relevant to the user's question. This process involves generating search queries and retrieving information. The retrieved data is then grouped into the most relevant sets of information for the question.

[0169] Step 4:

[0170] The server combines the compiled relevant information with previously obtained sentiment metadata to generate the most appropriate response for the user. Here, a generative AI model is used to adjust the tone and content to match the user's emotions. If the emotion is "confused," a detailed and polite response is generated; if the emotion is "urgent," a concise response is generated. The output is the adjusted user response.

[0171] Step 5:

[0172] The server adds references to the adjusted user response to prove the source of the information. This process adds metadata indicating the source of citations and data to the response to ensure the reliability of the information provided. This allows the user to evaluate the reliability of the presented information.

[0173] Step 6:

[0174] The device presents the user with the final answer and references sent from the server. Specifically, it either displays the answer on the device's screen or provides the information verbally using speech synthesis. This step allows the user to receive personalized information tailored to their emotions.

[0175] (Application Example 2)

[0176] 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".

[0177] In modern information systems, when users make inquiries that include unclear points, it is necessary not only to provide information but also to respond in a way that takes the user's emotions into consideration. However, conventional systems have difficulty adjusting the tone of response according to emotions, which can lead to user dissatisfaction. In particular, in e-commerce sites, appropriate responses that respond to the user's emotions lead to customer satisfaction, so there is a need for a system that links emotion recognition with information provision.

[0178] 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.

[0179] In this invention, the server includes means for receiving queries, means for analyzing the received queries and extracting relevant information, and means for analyzing the user's emotions using sentiment analysis technology and generating metadata. This makes it possible to provide responses with a tone that matches the user's emotions.

[0180] An "inquiry" is a question or request made in order to obtain information.

[0181] "Analysis" is the act of analyzing specific data or information and extracting meaning and relationships from it.

[0182] "Extraction" refers to selecting necessary elements from data or information.

[0183] "Searching" is the act of examining databases and documents in order to find the information you are looking for.

[0184] "Response" refers to a reply or answer to an inquiry.

[0185] "Reference information" refers to supplementary information added to a response that indicates its reliability or source.

[0186] "User" refers to an individual or legal entity that uses the system.

[0187] "Sentiment analysis" is a technique for identifying a speaker's emotions from written or spoken text.

[0188] "Metadata" refers to supplementary information used to explain other data.

[0189] "Natural language processing" is a technology that enables computers to understand, interpret, and generate human language.

[0190] Implementing this invention requires building a system that processes user inquiries and adjusts responses based on emotions. This system improves the user experience by recognizing the user's emotions and providing information in an appropriate tone.

[0191] When the server receives a query, it first analyzes its content using a natural language processing engine. For example, OpenAI's GPT model can be used for this analysis. Next, a sentiment analysis engine determines the user's emotions, and metadata is generated based on the results. Services such as Google's Cloud Natural Language API can be used for this sentiment analysis.

[0192] Based on the generated metadata, the server adjusts the tone of its response to provide the user with appropriate information. This adjustment involves modifying the style and level of detail in the response to achieve optimal communication tailored to the user's emotions. For example, if a user is confused, the server will generate a detailed and reassuring response to help alleviate their anxiety.

[0193] An example of a prompt message might be: "Analyze the text to indicate the user's emotions and explain the return policy in a tone that matches those emotions."

[0194] The terminal receives a response from the server and presents it to the user. Throughout this entire process, the user receives support tailored to their needs through the system, resulting in improved customer satisfaction.

[0195] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0196] Step 1:

[0197] The server checks the user's inquiry received from the terminal. The input data is the text data of the inquiry entered by the user on their smartphone. As output, the server prepares this text data for analysis. Specifically, the text data is transferred to the server in an appropriate format.

[0198] Step 2:

[0199] The server uses a natural language processing engine to analyze the received text. Here, OpenAI's GPT model is used to process the data in order to understand the user's question. The input is text data, and the output is the analyzed question content and intent. This process specifically involves analyzing grammatical structure and extracting the question's intent.

[0200] Step 3:

[0201] The server uses a sentiment analysis engine to analyze the user's emotional state from the text. This step uses tools such as the Google Cloud Natural Language API to analyze the sentiment within the text. The input is the user's question text, and the output is sentiment metadata. Specific operations include aggregating positive and negative evaluations of words and calculating sentiment intensity.

[0202] Step 4:

[0203] The server uses the analyzed question content and sentiment metadata to search for relevant information in the database and generate an appropriate response. The input used here is the analyzed question content and sentiment metadata, and the output is multiple adjusted responses. Specifically, it searches for related topics and generates and sorts response candidates.

[0204] Step 5:

[0205] The server adds reference information to each generated response, adjusts the response, and outputs it in a tone appropriate to the user. The input is the generated response content, and the output is the tone-adjusted response text. This process involves the specific action of selecting appropriate expressions for the response sentence.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] [Second Embodiment]

[0210] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0211] 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.

[0212] 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).

[0213] 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.

[0214] 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.

[0215] 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).

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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".

[0222] This invention is an information provision system for efficiently addressing unclear points in business operations. It searches for and presents appropriate information based on questions entered by the user. The following describes the program processing of this system in detail.

[0223] Users input questions by describing unclear points in their work using natural language via their terminal. This input is sent to the server using natural language processing technology. The server analyzes the received questions, extracts relevant keywords and context, and uses this information to access internal databases and knowledge bases to search for relevant documents and past cases.

[0224] Once the search is complete, the server generates multiple answers based on the retrieved information. Using generation AI, it constructs multiple answers in natural language based on the selected information. Each answer is accompanied by references as sources and bibliographies, allowing users to verify the accuracy and origin of the content.

[0225] The terminal displays the answers received from the server to the user, assisting the user in making selections and decisions based on that information. For example, if a user asks a question about troubleshooting a specific piece of business equipment, the server will provide technical documentation related to that equipment, past troubleshooting cases, and solutions based on them. This allows the user to quickly select the appropriate solution and address the problem.

[0226] This system allows users to efficiently obtain information in response to their work-related questions and accelerate decision-making. The collaboration between servers, terminals, and users improves operational efficiency and optimizes information utilization.

[0227] The following describes the processing flow.

[0228] Step 1:

[0229] The user enters a question about something they don't understand into the device using natural language. The device receives this input and prepares to send the data to the server.

[0230] Step 2:

[0231] The server utilizes natural language processing techniques to analyze user questions received from terminals. This involves understanding the content of the questions and extracting relevant keywords and context.

[0232] Step 3:

[0233] The server searches internal databases and external knowledge bases based on the extracted keywords and context. This search identifies relevant technical documents and past case information.

[0234] Step 4:

[0235] The server initiates a process to generate multiple answers based on relevant information. Using generative AI, it constructs answers in natural language from the acquired information.

[0236] Step 5:

[0237] The server provides references to the source of each answer it generates. This allows users to verify the reliability and source of the information provided.

[0238] Step 6:

[0239] The server sends the generated answer and its references to the terminal. The terminal receives this and prepares to present it to the user.

[0240] Step 7:

[0241] Users can view and review multiple responses presented through their device. They can then select the most appropriate solution to aid in decision-making in their work.

[0242] (Example 1)

[0243] 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."

[0244] In today's business environment, users are required to obtain various information quickly and accurately to make decisions. However, there is a lack of efficient means to quickly search for appropriate information in response to questions that may contain uncertainties, and to provide it in an easily understandable format. This problem is particularly serious in the current situation where business processes are becoming more complex and the volume of data is increasing.

[0245] 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.

[0246] In this invention, the server includes means for analyzing received information, including unknown points, using natural language processing technology to extract important features; means for searching for knowledge resources based on the extracted important features; and means for creating multiple answers using a generative AI model based on the search results. This enables users to obtain answers to their questions quickly and efficiently.

[0247] "Information containing unclear points" refers to documents or data that express issues or questions that users do not fully understand due to a lack of knowledge or insufficient verification.

[0248] "Natural language processing technology" is a general term for the technologies and methods that enable computers to understand, analyze, and process human language.

[0249] "Key features" refer to keywords, contexts, or other relevant information that are deemed particularly relevant from the information presented.

[0250] "Knowledge resources" refer to data, documents, case studies, and databases or other means of information aggregation that store them in a specific domain.

[0251] A "generative AI model" is an algorithm or system that uses artificial intelligence technology to perform pre-training and possesses the ability to generate natural language responses or answers to new information.

[0252] "Source information" refers to the information sources and documents referenced by the generated answer, and is information used to demonstrate the reliability of the information.

[0253] "Information and communication equipment" is a general term for devices and equipment that transmit and receive data, and includes computers, smartphones, tablets, etc.

[0254] This invention is an information provision system that provides quick and appropriate responses to unclear points in business operations. Its purpose is to provide appropriate information tailored to the user's question, which is entered in natural language.

[0255] Users input their questions in natural language using their device and send the information to the server. This device can be a standard computer, smartphone, or tablet. The input information is transferred from the device to the server using a secure communication method (e.g., HTTPS).

[0256] The server analyzes the received information. This analysis utilizes natural language processing techniques, and well-known libraries such as NLTK and spaCy can be used. The server extracts key features from the information and searches for knowledge resources based on them. The databases used here are assumed to be internal knowledge bases or cloud-based databases.

[0257] The server uses a generative AI model to generate multiple answers based on information that matches the extracted key features. In this scenario, OpenAI's GPT series is a typical generative AI model to use. Each generated answer is accompanied by source information, which allows the user to be confident in the reliability of the information.

[0258] The terminal's role is to quickly display the answers received from the server to the user. Based on the presented answers, the user can select the most suitable solution to the problem and apply it to their work.

[0259] As a concrete example, if a user wants to know the "installation procedure for new software," they send the question to the server via their terminal. The server analyzes this question, searches relevant installation manuals and FAQ databases, and generates an answer in natural language using a generative AI model. The generated answer is then presented to the user along with the source of the information.

[0260] An example of a prompt message would be, "Please tell me the general troubleshooting steps when the new business software encounters an error."

[0261] In this way, users can obtain the necessary information quickly and accurately, leading to improved work efficiency and faster decision-making.

[0262] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0263] Step 1:

[0264] The user uses a terminal to input questions in natural language. The terminal receives the input and formats it as text data. After checking whether the input is properly structured and performing error checks, it sends the data to the server to proceed to the next step.

[0265] Step 2:

[0266] The server receives text data sent from the terminal. Here, natural language processing techniques are used to analyze the content of the question. This analysis extracts key keywords and relevant contextual information. For example, if the question contains the phrase "software installation," the software name and error message will also be extracted.

[0267] Step 3:

[0268] The server searches for knowledge resources based on the extracted keywords and contextual information. In this step, it executes search queries against internal knowledge bases and external databases to retrieve relevant information and documents. The information is compiled into a dataset and passed on to the next process.

[0269] Step 4:

[0270] The server uses the acquired information as input to generate answers using a generative AI model. This process organizes and integrates the acquired relevant information, creating multiple answers in natural language. Each answer includes source information to ensure the reliability of the information.

[0271] Step 5:

[0272] The generated answer is sent from the server to the terminal. The terminal receives the answer and presents it to the user via the user interface. The interface layout is dynamically adjusted to allow the user to easily view the answer.

[0273] Step 6:

[0274] Users review multiple displayed solutions and select the one best suited to their problem. After this selection, users can then take concrete action. This enables rapid and effective problem-solving.

[0275] (Application Example 1)

[0276] 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."

[0277] In modern industrial sites, with the complexity of machines increasing, it is required to immediately address any unclear points or malfunctions. However, conventional information provision systems have had the problem that it is difficult to obtain information for quickly and appropriately solving problems of industrial automatic machines. For this reason, it has hindered the improvement of work efficiency in industrial sites.

[0278] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following respective means.

[0279] In this invention, the server includes means for analyzing unstructured data specialized for industry and extracting related information, means for searching for related materials based on the extracted information, and means for displaying an information set on a device that controls an industrial automatic machine. As a result, it becomes possible to quickly and accurately solve problems in industrial sites.

[0280] "Unstructured data" refers to data that does not have a fixed format or structure, and includes text and voice data described in natural language.

[0281] "Analysis" refers to the process of decomposing the received data and identifying the information and patterns contained therein.

[0282] "Related information" refers to additional data and knowledge related to the content of the received data, and may include solutions and reference materials.

[0283] "Information set" refers to a collection of a series of information obtained by search or generation, and includes data and guidelines useful for problem solving.

[0284] "Reference information" is information added to indicate the reliability and source of an information set, and includes source information and author information.

[0285] "Industrial automatic machine" refers to an automated device or system used in manufacturing industries, factories, etc.

[0286] A "control device" is an electronic device for managing the operation of machines and devices and transmitting instructions.

[0287] A "display device" is a device used for humans to visually confirm information and data, including screens and displays.

[0288] The system for implementing this invention begins with the user describing uncertainties in order to solve problems in industrial automation machines. The uncertainties and questions input by the user are sent to the control device. In this device, data is analyzed using natural language processing technology, and relevant information is extracted.

[0289] The server performs processing to search for past materials based on the extracted relevant information. At this stage, it accesses the internal database and generates the most relevant information set based on specific keywords and context. Reference information is attached to the generated information set to ensure the accuracy of the information.

[0290] The server sends the information set to the display device that controls the industrial automation machine for real-time display. As a result, the user can immediately access the information necessary to solve the problem on-site. For example, when a machine suddenly stops on a certain production line, if the user asks "Please tell me the cause of the machine stop and the recovery method.", relevant technical documents and troubleshooting procedures will be displayed.

[0291] The hardware used includes the control device and display of industrial automation machines, and the software uses the natural language processing library spaCy and the generative AI model GPT-3.

[0292] As a specific example, the prompt text is input in the form of "Please tell me the possible trouble cases and corresponding solutions in the assembly process of Product X." Based on this prompt text, the server analyzes the relevant information and presents the optimal solution.

[0293] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0294] Step 1:

[0295] The user inputs any questions or uncertainties into the control device. These are entered in natural language text format. Receiving this input data prepares the device for the next step.

[0296] Step 2:

[0297] The server analyzes the unclear points received from the user using natural language processing techniques. Specifically, it uses spaCy to tokenize the text data and extract keywords and important phrases. This process generates a dataset for searching for relevant information.

[0298] Step 3:

[0299] The server searches its internal database using the keywords extracted in step 2 to find relevant documents. The retrieved data includes documents detailing past troubleshooting cases and solutions. This builds a knowledge base capable of addressing user questions.

[0300] Step 4:

[0301] The server generates an information set based on relevant materials. Using the GPT-3 generation AI model, it creates multiple natural-sounding answers to the user's questions. The information set also includes reference information indicating the accuracy of the answers. This output is then converted into a format that the user can visually recognize.

[0302] Step 5:

[0303] The server transmits the generated information set to a display device on the control device. The information set is immediately visualized on the display device, allowing the user to easily review the information. Based on the provided information, the user can solve problems related to industrial automated machinery and make rapid decisions.

[0304] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion recognition model 59 and perform specific processing using the user's emotion.

[0305] The present invention is an information providing system for quickly responding to business uncertainties and questions, and further combines an emotion engine that recognizes and responds to the user's emotion. The purpose of this invention is that when the user inputs a question to the system, appropriate information is provided for the question and a response according to the user's emotion is made.

[0306] The user inputs about business uncertainties to the terminal. Here, before the terminal transmits the user's question to the server, the emotion engine installed therein analyzes the user's emotional state from the input text. The emotion engine recognizes the emotion from the text when the user asks a question and provides metadata corresponding to the emotion to the server.

[0307] The server analyzes the received question using natural language processing technology, extracts relevant data, and then searches the internal database to collect relevant information. In this process, the server takes into account the metadata from the emotion engine and generates an answer adapted to the user's emotion. For example, when the emotion engine determines that the user is confused, the server generates a more polite and detailed answer.

[0308] A reference for proving the reliability of the information source is attached to the generated answer. Furthermore, since the priority of this reference is also adjusted based on the analysis result of the emotion engine, the user can check the information with confidence.

[0309] Finally, the terminal presents the user with the server-generated answers and references. By referring to the information provided in a tone and content that matches the user's emotions, the user can efficiently solve business problems. For example, if the server analyzes that the user is in a hurry, it will quickly respond to the user's needs by providing an immediate and concise answer.

[0310] This system helps improve work efficiency by providing a more personalized experience through flexible responses that respond to the user's emotions.

[0311] The following describes the processing flow.

[0312] Step 1:

[0313] The user uses a terminal to input work-related questions or uncertainties in natural language. Upon receiving this input, the terminal activates an emotion engine to analyze the user's emotions from the entered text.

[0314] Step 2:

[0315] The device obtains analysis results regarding the user's emotions and sends them to the server along with the question text. The emotion data is used by the server as supplementary information to understand the context of the question.

[0316] Step 3:

[0317] The server analyzes the received question using natural language processing techniques. It extracts the question's subject and related keywords, and then searches its internal database based on this information to gather relevant data.

[0318] Step 4:

[0319] Based on the information collected by the server, a generative AI is used to create multiple responses. In this process, the server takes into account the emotional data sent from the terminal and creates responses that adjust in tone and complexity according to the user's emotions.

[0320] Step 5:

[0321] The server assigns a source reference to each response it generates. Based on sentiment data, the priority of the references is also adjusted as needed.

[0322] Step 6:

[0323] The server sends the generated response and its references to the terminal. The terminal receives this and displays the appropriately formatted response to the user.

[0324] Step 7:

[0325] Users can review the answers presented through their device and refer to references and additional information as needed. Based on the information provided, users can make quick decisions.

[0326] (Example 2)

[0327] 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".

[0328] The problem that this invention aims to solve is to provide prompt and accurate information regarding unclear points and questions in business operations, as well as to achieve appropriate communication that responds to the user's emotions. Conventional information provision systems lack flexible responses based on the user's emotions, which can result in a decrease in the quality of the user experience and the efficiency of operations.

[0329] 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.

[0330] In this invention, the server includes means for analyzing the sentiment of a question, means for generating metadata based on the sentiment analysis results, and means for analyzing the question and metadata and extracting relevant information. This makes it possible to provide information that takes the user's emotions into account, providing a more personalized experience tailored to each individual user and improving operational efficiency.

[0331] A "means of receiving questions" refers to a method for obtaining text input from the user and an interface for passing that input to the next processing step.

[0332] "Means of analyzing emotions" refers to algorithms and technologies used to identify a user's emotions from input text, utilizing natural language processing and sentiment analysis techniques.

[0333] "Methods for generating metadata" refer to supplementary data constructed based on the results of sentiment analysis, and are part of the information used when generating responses.

[0334] "Means of extracting relevant information" refers to methods of retrieving necessary information from databases and resources based on the user's questions and sentiments.

[0335] "Means of searching records" refers to processes or techniques for finding information related to a question, whether within internal or external data storage.

[0336] "Means of generating answers" refers to the process of assembling and displaying information appropriate to a user's question, based on search results and sentiment information.

[0337] "Means of providing data that proves the source of information" refers to methods of providing additional information that indicates the source of the information in order to guarantee the reliability of the information being provided.

[0338] "Means of presenting answers" refers to methods of displaying generated information and data that proves its reliability to the user, and which are adjusted to respond to the user's emotions.

[0339] This invention is an information provision system that addresses users' work-related uncertainties and questions, and is equipped with a function to recognize and respond to the user's emotions. The system is implemented using the following hardware and software.

[0340] Hardware and software configuration

[0341] The terminal is equipped with an emotion engine to receive user input and perform sentiment analysis. The server has the processing power to analyze received questions using natural language processing technology. This uses a specific generative AI model. An internal database provides high-speed search functionality to support information retrieval. The emotion engine and natural language processing technology are implemented with specialized algorithms and machine learning models that can analyze sentiment and meaning from text-based input.

[0342] Data processing and calculations

[0343] The terminal receives user input text, preprocesses it, and performs sentiment analysis using a sentiment engine. The server uses natural language processing algorithms to analyze the content of the question and extracts relevant information from the database. In this process, the sentiment analysis results are used as metadata to generate a response that corresponds to the user's emotions. The generated response is given a reference to prove its reliability.

[0344] Specific example

[0345] As a concrete example, consider a scenario where a user enters a question such as, "I don't know how to proceed with the new project." In this case, the sentiment engine analyzes that the user is confused. Based on this, the server generates a detailed and thorough explanation of how to proceed, and presents it with references to reliable sources. Conversely, if the analysis indicates that the user is in a hurry, a concise and to-the-point answer is provided immediately.

[0346] Example of a prompt

[0347] "I'm confused about how to proceed with the project. Could you please explain the detailed steps?"

[0348] This system aims to provide a better user experience by taking user emotions into consideration and delivering information quickly and accurately.

[0349] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0350] Step 1:

[0351] Users input work-related questions or uncertainties into a terminal. This input is in text format and includes the user's problems and information requests. The terminal passes the input text to a sentiment engine. This engine analyzes the input text and uses natural language processing techniques to identify the emotional state. As a result, metadata indicating the user's emotions is generated.

[0352] Step 2:

[0353] The device sends the metadata obtained from sentiment analysis and the original question to the server. The server analyzes the received question using natural language processing algorithms to extract the main theme and related keywords. This analysis step involves a generative AI model, which provides an appropriate structural understanding of the question. The output is structured data of the question.

[0354] Step 3:

[0355] The server uses the structured data of the question to search its internal database and extract relevant information. Here, the database search is performed based on keywords and themes to identify the information most relevant to the user's question. This process involves generating search queries and retrieving information. The retrieved data is then grouped into the most relevant sets of information for the question.

[0356] Step 4:

[0357] The server combines the compiled relevant information with previously obtained sentiment metadata to generate the most appropriate response for the user. Here, a generative AI model is used to adjust the tone and content to match the user's emotions. If the emotion is "confused," a detailed and polite response is generated; if the emotion is "urgent," a concise response is generated. The output is the adjusted user response.

[0358] Step 5:

[0359] The server adds references to the adjusted user response to prove the source of the information. This process adds metadata indicating the source of citations and data to the response to ensure the reliability of the information provided. This allows the user to evaluate the reliability of the presented information.

[0360] Step 6:

[0361] The device presents the user with the final answer and references sent from the server. Specifically, it either displays the answer on the device's screen or provides the information verbally using speech synthesis. This step allows the user to receive personalized information tailored to their emotions.

[0362] (Application Example 2)

[0363] 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."

[0364] In modern information systems, when users make inquiries that include unclear points, it is necessary not only to provide information but also to respond in a way that takes the user's emotions into consideration. However, conventional systems have difficulty adjusting the tone of response according to emotions, which can lead to user dissatisfaction. In particular, in e-commerce sites, appropriate responses that respond to the user's emotions lead to customer satisfaction, so there is a need for a system that links emotion recognition with information provision.

[0365] 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.

[0366] In this invention, the server includes means for receiving queries, means for analyzing the received queries and extracting relevant information, and means for analyzing the user's emotions using sentiment analysis technology and generating metadata. This makes it possible to provide responses with a tone that matches the user's emotions.

[0367] An "inquiry" is a question or request made in order to obtain information.

[0368] "Analysis" is the act of analyzing specific data or information and extracting meaning and relationships from it.

[0369] "Extraction" refers to selecting necessary elements from data or information.

[0370] "Searching" is the act of examining databases and documents in order to find the information you are looking for.

[0371] "Response" refers to a reply or answer to an inquiry.

[0372] "Reference information" refers to supplementary information added to a response that indicates its reliability or source.

[0373] "User" refers to an individual or legal entity that uses the system.

[0374] "Sentiment analysis" is a technique for identifying a speaker's emotions from written or spoken text.

[0375] "Metadata" refers to supplementary information used to explain other data.

[0376] "Natural language processing" is a technology that enables computers to understand, interpret, and generate human language.

[0377] Implementing this invention requires building a system that processes user inquiries and adjusts responses based on emotions. This system improves the user experience by recognizing the user's emotions and providing information in an appropriate tone.

[0378] When the server receives a query, it first analyzes its content using a natural language processing engine. For example, OpenAI's GPT model can be used for this analysis. Next, a sentiment analysis engine determines the user's emotions, and metadata is generated based on the results. Services such as the Google Cloud Natural Language API can be used for this sentiment analysis.

[0379] Based on the generated metadata, the server adjusts the tone of its response to provide the user with appropriate information. This adjustment involves modifying the style and level of detail in the response to achieve optimal communication tailored to the user's emotions. For example, if a user is confused, the server will generate a detailed and reassuring response to help alleviate their anxiety.

[0380] An example of a prompt message might be: "Analyze the text to indicate the user's emotions and explain the return policy in a tone that matches those emotions."

[0381] The terminal receives a response from the server and presents it to the user. Throughout this entire process, the user receives support tailored to their needs through the system, resulting in improved customer satisfaction.

[0382] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0383] Step 1:

[0384] The server checks the user's inquiry received from the terminal. The input data is the text data of the inquiry entered by the user on their smartphone. As output, the server prepares this text data for analysis. Specifically, the text data is transferred to the server in an appropriate format.

[0385] Step 2:

[0386] The server uses a natural language processing engine to analyze the received text. Here, OpenAI's GPT model is used to process the data in order to understand the user's question. The input is text data, and the output is the analyzed question content and intent. This process specifically involves analyzing grammatical structure and extracting the question's intent.

[0387] Step 3:

[0388] The server uses a sentiment analysis engine to analyze the user's emotional state from the text. This step uses tools such as the Google Cloud Natural Language API to analyze the sentiment within the text. The input is the user's question text, and the output is sentiment metadata. Specific operations include aggregating positive and negative evaluations of words and calculating sentiment intensity.

[0389] Step 4:

[0390] The server uses the analyzed question content and sentiment metadata to search for relevant information in the database and generate an appropriate response. The input used here is the analyzed question content and sentiment metadata, and the output is multiple adjusted responses. Specifically, it searches for related topics and generates and sorts response candidates.

[0391] Step 5:

[0392] The server adds reference information to each generated response, adjusts the response, and outputs it in a tone appropriate to the user. The input is the generated response content, and the output is the tone-adjusted response text. This process involves the specific action of selecting appropriate expressions for the response sentence.

[0393] 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.

[0394] 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.

[0395] 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.

[0396] [Third Embodiment]

[0397] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0398] 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.

[0399] 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).

[0400] 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.

[0401] 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.

[0402] 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).

[0403] 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.

[0404] 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.

[0405] 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.

[0406] 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.

[0407] 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.

[0408] 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".

[0409] This invention is an information provision system for efficiently addressing unclear points in business operations. It searches for and presents appropriate information based on questions entered by the user. The following describes the program processing of this system in detail.

[0410] Users input questions by describing unclear points in their work using natural language via their terminal. This input is sent to the server using natural language processing technology. The server analyzes the received questions, extracts relevant keywords and context, and uses this information to access internal databases and knowledge bases to search for relevant documents and past cases.

[0411] Once the search is complete, the server generates multiple answers based on the retrieved information. Using generation AI, it constructs multiple answers in natural language based on the selected information. Each answer is accompanied by references as sources and bibliographies, allowing users to verify the accuracy and origin of the content.

[0412] The terminal displays the answers received from the server to the user, assisting the user in making selections and decisions based on that information. For example, if a user asks a question about troubleshooting a specific piece of business equipment, the server will provide technical documentation related to that equipment, past troubleshooting cases, and solutions based on them. This allows the user to quickly select the appropriate solution and address the problem.

[0413] This system allows users to efficiently obtain information in response to their work-related questions and accelerate decision-making. The collaboration between servers, terminals, and users improves operational efficiency and optimizes information utilization.

[0414] The following describes the processing flow.

[0415] Step 1:

[0416] The user enters a question about something they don't understand into the device using natural language. The device receives this input and prepares to send the data to the server.

[0417] Step 2:

[0418] The server utilizes natural language processing techniques to analyze user questions received from terminals. This involves understanding the content of the questions and extracting relevant keywords and context.

[0419] Step 3:

[0420] The server searches internal databases and external knowledge bases based on the extracted keywords and context. This search identifies relevant technical documents and past case information.

[0421] Step 4:

[0422] The server initiates a process to generate multiple answers based on relevant information. Using generative AI, it constructs answers in natural language from the acquired information.

[0423] Step 5:

[0424] The server provides references to the source of each answer it generates. This allows users to verify the reliability and source of the information provided.

[0425] Step 6:

[0426] The server sends the generated answer and its references to the terminal. The terminal receives this and prepares to present it to the user.

[0427] Step 7:

[0428] Users can view and review multiple responses presented through their device. They can then select the most appropriate solution to aid in decision-making in their work.

[0429] (Example 1)

[0430] 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."

[0431] In today's business environment, users are required to obtain various information quickly and accurately to make decisions. However, there is a lack of efficient means to quickly search for appropriate information in response to questions that may contain uncertainties, and to provide it in an easily understandable format. This problem is particularly serious in the current situation where business processes are becoming more complex and the volume of data is increasing.

[0432] 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.

[0433] In this invention, the server includes means for analyzing received information, including unknown points, using natural language processing technology to extract important features; means for searching for knowledge resources based on the extracted important features; and means for creating multiple answers using a generative AI model based on the search results. This enables users to obtain answers to their questions quickly and efficiently.

[0434] "Information containing unclear points" refers to documents or data that express issues or questions that users do not fully understand due to a lack of knowledge or insufficient verification.

[0435] "Natural language processing technology" is a general term for the technologies and methods that enable computers to understand, analyze, and process human language.

[0436] "Key features" refer to keywords, contexts, or other relevant information that are deemed particularly relevant from the information presented.

[0437] "Knowledge resources" refer to data, documents, case studies, and databases or other means of information aggregation that store them in a specific domain.

[0438] A "generative AI model" is an algorithm or system that uses artificial intelligence technology to perform pre-training and possesses the ability to generate natural language responses or answers to new information.

[0439] "Source information" refers to the information sources and documents referenced by the generated answer, and is information used to demonstrate the reliability of the information.

[0440] "Information and communication equipment" is a general term for devices and equipment that transmit and receive data, and includes computers, smartphones, tablets, etc.

[0441] This invention is an information provision system that provides quick and appropriate responses to unclear points in business operations. Its purpose is to provide appropriate information tailored to the user's question, which is entered in natural language.

[0442] Users input their questions in natural language using their device and send the information to the server. This device can be a standard computer, smartphone, or tablet. The input information is transferred from the device to the server using a secure communication method (e.g., HTTPS).

[0443] The server analyzes the received information. This analysis utilizes natural language processing techniques, and well-known libraries such as NLTK and spaCy can be used. The server extracts key features from the information and searches for knowledge resources based on them. The databases used here are assumed to be internal knowledge bases or cloud-based databases.

[0444] The server uses a generative AI model to generate multiple answers based on information that matches the extracted key features. In this scenario, OpenAI's GPT series is a typical generative AI model to use. Each generated answer is accompanied by source information, which allows the user to be confident in the reliability of the information.

[0445] The terminal's role is to quickly display the answers received from the server to the user. Based on the presented answers, the user can select the most suitable solution to the problem and apply it to their work.

[0446] As a concrete example, if a user wants to know the "installation procedure for new software," they send the question to the server via their terminal. The server analyzes this question, searches relevant installation manuals and FAQ databases, and generates an answer in natural language using a generative AI model. The generated answer is then presented to the user along with the source of the information.

[0447] An example of a prompt message would be, "Please tell me the general troubleshooting steps when the new business software encounters an error."

[0448] In this way, users can obtain the necessary information quickly and accurately, leading to improved work efficiency and faster decision-making.

[0449] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0450] Step 1:

[0451] The user uses a terminal to input questions in natural language. The terminal receives the input and formats it as text data. After checking whether the input is properly structured and performing error checks, it sends the data to the server to proceed to the next step.

[0452] Step 2:

[0453] The server receives text data sent from the terminal. Here, natural language processing techniques are used to analyze the content of the question. This analysis extracts key keywords and relevant contextual information. For example, if the question contains the phrase "software installation," the software name and error message will also be extracted.

[0454] Step 3:

[0455] The server searches for knowledge resources based on the extracted keywords and contextual information. In this step, it executes search queries against internal knowledge bases and external databases to retrieve relevant information and documents. The information is compiled into a dataset and passed on to the next process.

[0456] Step 4:

[0457] The server uses the acquired information as input to generate answers using a generative AI model. This process organizes and integrates the acquired relevant information, creating multiple answers in natural language. Each answer includes source information to ensure the reliability of the information.

[0458] Step 5:

[0459] The generated answer is sent from the server to the terminal. The terminal receives the answer and presents it to the user via the user interface. The interface layout is dynamically adjusted to allow the user to easily view the answer.

[0460] Step 6:

[0461] Users review multiple displayed solutions and select the one best suited to their problem. After this selection, users can then take concrete action. This enables rapid and effective problem-solving.

[0462] (Application Example 1)

[0463] 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."

[0464] In modern industrial settings, the increasing complexity of machinery necessitates immediate action when uncertainties or malfunctions occur. However, conventional information systems have struggled to provide the necessary information quickly and appropriately to resolve problems with industrial automated machinery. This has hindered improvements in work efficiency in industrial settings.

[0465] 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.

[0466] In this invention, the server includes means for analyzing unstructured data specific to industry and extracting relevant information, means for searching for relevant materials based on the extracted information, and means for displaying the information set on a device that controls industrial automated machinery. This enables rapid and accurate problem solving in industrial settings.

[0467] "Unstructured data" refers to data that does not have a fixed format or structure, and includes text and audio data written in natural language.

[0468] "Analysis" is the process of breaking down received data and identifying the information and patterns contained within it.

[0469] "Related information" refers to additional data or knowledge related to the content of the received data, and may include solutions or reference materials.

[0470] An "information set" is a collection of information obtained through searching or generation, including data and guidelines that are useful for solving problems.

[0471] "Reference information" refers to information added to indicate the reliability or source of an information set, and includes information about the source and author.

[0472] "Industrial automated machinery" refers to automated devices and systems used in manufacturing, factories, and other industrial settings.

[0473] A "control device" is an electronic device used to manage the operation of machinery and equipment and to transmit instructions.

[0474] A "display device" is a device used by humans to visually confirm information and data, and includes screens and displays.

[0475] The system for implementing this invention begins with a user describing their questions in order to solve a problem in an industrial automated machine. The questions and concerns entered by the user are sent to a control device. This device analyzes the data using natural language processing technology and extracts relevant information.

[0476] The server processes the extracted relevant information to search for past documents. At this stage, it accesses an internal database and generates the most relevant set of information based on specific keywords and context. Reference information is added to the generated set to ensure the accuracy of the information.

[0477] The server transmits a set of information to a display device that controls industrial automated machinery, displaying it in real time. This allows users to immediately access the information needed to solve problems on-site. For example, if a machine suddenly stops on a production line, a user can ask, "What caused the machine to stop and how can I get it working again?" and relevant technical documentation and troubleshooting procedures will be displayed.

[0478] The hardware used includes control devices and displays for industrial automated machinery, while the software includes the natural language processing library spaCy and the generative AI model GPT-3.

[0479] As a concrete example, the prompt might be entered as, "Please tell me about possible problems that can occur during the assembly process of product X and how to address them." Based on this prompt, the server analyzes the relevant information and presents the optimal solution.

[0480] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0481] Step 1:

[0482] The user inputs any questions or uncertainties into the control device. These are entered in natural language text format. Receiving this input data prepares the device for the next step.

[0483] Step 2:

[0484] The server analyzes the unclear points received from the user using natural language processing techniques. Specifically, it uses spaCy to tokenize the text data and extract keywords and important phrases. This process generates a dataset for searching for relevant information.

[0485] Step 3:

[0486] The server searches its internal database using the keywords extracted in step 2 to find relevant documents. The retrieved data includes documents detailing past troubleshooting cases and solutions. This builds a knowledge base capable of addressing user questions.

[0487] Step 4:

[0488] The server generates an information set based on relevant materials. Using the GPT-3 generation AI model, it creates multiple natural-sounding answers to the user's questions. The information set also includes reference information indicating the accuracy of the answers. This output is then converted into a format that the user can visually recognize.

[0489] Step 5:

[0490] The server transmits the generated information set to a display device on the control device. The information set is immediately visualized on the display device, allowing the user to easily review the information. Based on the provided information, the user can solve problems related to industrial automated machinery and make rapid decisions.

[0491] 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.

[0492] This invention is an information provision system for quickly addressing unclear points and questions in business operations, and further incorporates an emotion engine that recognizes and responds to the user's emotions. The purpose of this invention is for the user to input a question into the system, which will then provide appropriate information in response to that question, as well as respond in accordance with the user's emotions.

[0493] The user inputs questions about work-related issues into the terminal. Before sending the user's question to the server, the terminal's built-in sentiment engine analyzes the user's emotional state from the input text. The sentiment engine recognizes the emotion from the text of the user's question and provides metadata corresponding to that emotion to the server.

[0494] The server uses natural language processing techniques to analyze the received question, extracts relevant data, and then searches its internal database to gather relevant information. In this process, the server takes metadata from the sentiment engine into consideration to generate a response that is appropriate to the user's emotions. For example, if the sentiment engine determines that the user is confused, the server will generate a more polite and detailed response.

[0495] The generated answers are accompanied by references that verify the reliability of the information sources. Furthermore, the priority of these references is adjusted based on the results of the sentiment engine's analysis, allowing users to confidently verify the information.

[0496] Finally, the terminal presents the user with the server-generated answers and references. By referring to the information provided in a tone and content that matches the user's emotions, the user can efficiently solve business problems. For example, if the server analyzes that the user is in a hurry, it will quickly respond to the user's needs by providing an immediate and concise answer.

[0497] This system helps improve work efficiency by providing a more personalized experience through flexible responses that respond to the user's emotions.

[0498] The following describes the processing flow.

[0499] Step 1:

[0500] The user uses a terminal to input work-related questions or uncertainties in natural language. Upon receiving this input, the terminal activates an emotion engine to analyze the user's emotions from the entered text.

[0501] Step 2:

[0502] The device obtains analysis results regarding the user's emotions and sends them to the server along with the question text. The emotion data is used by the server as supplementary information to understand the context of the question.

[0503] Step 3:

[0504] The server analyzes the received question using natural language processing techniques. It extracts the question's subject and related keywords, and then searches its internal database based on this information to gather relevant data.

[0505] Step 4:

[0506] Based on the information collected by the server, a generative AI is used to create multiple responses. In this process, the server takes into account the emotional data sent from the terminal and creates responses that adjust in tone and complexity according to the user's emotions.

[0507] Step 5:

[0508] The server assigns a source reference to each response it generates. Based on sentiment data, the priority of the references is also adjusted as needed.

[0509] Step 6:

[0510] The server sends the generated response and its references to the terminal. The terminal receives this and displays the appropriately formatted response to the user.

[0511] Step 7:

[0512] Users can review the answers presented through their device and refer to references and additional information as needed. Based on the information provided, users can make quick decisions.

[0513] (Example 2)

[0514] 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."

[0515] The problem that this invention aims to solve is to provide prompt and accurate information regarding unclear points and questions in business operations, as well as to achieve appropriate communication that responds to the user's emotions. Conventional information provision systems lack flexible responses based on the user's emotions, which can result in a decrease in the quality of the user experience and the efficiency of operations.

[0516] 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.

[0517] In this invention, the server includes means for analyzing the sentiment of a question, means for generating metadata based on the sentiment analysis results, and means for analyzing the question and metadata and extracting relevant information. This makes it possible to provide information that takes the user's emotions into account, providing a more personalized experience tailored to each individual user and improving operational efficiency.

[0518] A "means of receiving questions" refers to a method for obtaining text input from the user and an interface for passing that input to the next processing step.

[0519] "Means of analyzing emotions" refers to algorithms and technologies used to identify a user's emotions from input text, utilizing natural language processing and sentiment analysis techniques.

[0520] "Methods for generating metadata" refer to supplementary data constructed based on the results of sentiment analysis, and are part of the information used when generating responses.

[0521] "Means of extracting relevant information" refers to methods of retrieving necessary information from databases and resources based on the user's questions and sentiments.

[0522] "Means of searching records" refers to processes or techniques for finding information related to a question, whether within internal or external data storage.

[0523] "Means of generating answers" refers to the process of assembling and displaying information appropriate to a user's question, based on search results and sentiment information.

[0524] "Means of providing data that proves the source of information" refers to methods of providing additional information that indicates the source of the information in order to guarantee the reliability of the information being provided.

[0525] "Means of presenting answers" refers to methods of displaying generated information and data that proves its reliability to the user, and which are adjusted to respond to the user's emotions.

[0526] This invention is an information provision system that addresses users' work-related uncertainties and questions, and is equipped with a function to recognize and respond to the user's emotions. The system is implemented using the following hardware and software.

[0527] Hardware and software configuration

[0528] The terminal is equipped with an emotion engine to receive user input and perform sentiment analysis. The server has the processing power to analyze received questions using natural language processing technology. This uses a specific generative AI model. An internal database provides high-speed search functionality to support information retrieval. The emotion engine and natural language processing technology are implemented with specialized algorithms and machine learning models that can analyze sentiment and meaning from text-based input.

[0529] Data processing and calculations

[0530] The terminal receives user input text, preprocesses it, and performs sentiment analysis using a sentiment engine. The server uses natural language processing algorithms to analyze the content of the question and extracts relevant information from the database. In this process, the sentiment analysis results are used as metadata to generate a response that corresponds to the user's emotions. The generated response is given a reference to prove its reliability.

[0531] Specific example

[0532] As a concrete example, consider a scenario where a user enters a question such as, "I don't know how to proceed with the new project." In this case, the sentiment engine analyzes that the user is confused. Based on this, the server generates a detailed and thorough explanation of how to proceed, and presents it with references to reliable sources. Conversely, if the analysis indicates that the user is in a hurry, a concise and to-the-point answer is provided immediately.

[0533] Example of a prompt

[0534] "I'm confused about how to proceed with the project. Could you please explain the detailed steps?"

[0535] This system aims to provide a better user experience by taking user emotions into consideration and delivering information quickly and accurately.

[0536] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0537] Step 1:

[0538] Users input work-related questions or uncertainties into a terminal. This input is in text format and includes the user's problems and information requests. The terminal passes the input text to a sentiment engine. This engine analyzes the input text and uses natural language processing techniques to identify the emotional state. As a result, metadata indicating the user's emotions is generated.

[0539] Step 2:

[0540] The device sends the metadata obtained from sentiment analysis and the original question to the server. The server analyzes the received question using natural language processing algorithms to extract the main theme and related keywords. This analysis step involves a generative AI model, which provides an appropriate structural understanding of the question. The output is structured data of the question.

[0541] Step 3:

[0542] The server uses the structured data of the question to search its internal database and extract relevant information. Here, the database search is performed based on keywords and themes to identify the information most relevant to the user's question. This process involves generating search queries and retrieving information. The retrieved data is then grouped into the most relevant sets of information for the question.

[0543] Step 4:

[0544] The server combines the compiled relevant information with previously obtained sentiment metadata to generate the most appropriate response for the user. Here, a generative AI model is used to adjust the tone and content to match the user's emotions. If the emotion is "confused," a detailed and polite response is generated; if the emotion is "urgent," a concise response is generated. The output is the adjusted user response.

[0545] Step 5:

[0546] The server adds references to the adjusted user response to prove the source of the information. This process adds metadata indicating the source of citations and data to the response to ensure the reliability of the information provided. This allows the user to evaluate the reliability of the presented information.

[0547] Step 6:

[0548] The device presents the user with the final answer and references sent from the server. Specifically, it either displays the answer on the device's screen or provides the information verbally using speech synthesis. This step allows the user to receive personalized information tailored to their emotions.

[0549] (Application Example 2)

[0550] 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."

[0551] In modern information systems, when users make inquiries that include unclear points, it is necessary not only to provide information but also to respond in a way that takes the user's emotions into consideration. However, conventional systems have difficulty adjusting the tone of response according to emotions, which can lead to user dissatisfaction. In particular, in e-commerce sites, appropriate responses that respond to the user's emotions lead to customer satisfaction, so there is a need for a system that links emotion recognition with information provision.

[0552] 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.

[0553] In this invention, the server includes means for receiving queries, means for analyzing the received queries and extracting relevant information, and means for analyzing the user's emotions using sentiment analysis technology and generating metadata. This makes it possible to provide responses with a tone that matches the user's emotions.

[0554] An "inquiry" is a question or request made in order to obtain information.

[0555] "Analysis" is the act of analyzing specific data or information and extracting meaning and relationships from it.

[0556] "Extraction" refers to selecting necessary elements from data or information.

[0557] "Searching" is the act of examining databases and documents in order to find the information you are looking for.

[0558] "Response" refers to a reply or answer to an inquiry.

[0559] "Reference information" refers to supplementary information added to a response that indicates its reliability or source.

[0560] "User" refers to an individual or legal entity that uses the system.

[0561] "Sentiment analysis" is a technique for identifying a speaker's emotions from written or spoken text.

[0562] "Metadata" refers to supplementary information used to explain other data.

[0563] "Natural language processing" is a technology that enables computers to understand, interpret, and generate human language.

[0564] Implementing this invention requires building a system that processes user inquiries and adjusts responses based on emotions. This system improves the user experience by recognizing the user's emotions and providing information in an appropriate tone.

[0565] When the server receives a query, it first analyzes its content using a natural language processing engine. For example, OpenAI's GPT model can be used for this analysis. Next, a sentiment analysis engine determines the user's emotions, and metadata is generated based on the results. Services such as the Google Cloud Natural Language API can be used for this sentiment analysis.

[0566] Based on the generated metadata, the server adjusts the tone of its response to provide the user with appropriate information. This adjustment involves modifying the style and level of detail in the response to achieve optimal communication tailored to the user's emotions. For example, if a user is confused, the server will generate a detailed and reassuring response to help alleviate their anxiety.

[0567] An example of a prompt message might be: "Analyze the text to indicate the user's emotions and explain the return policy in a tone that matches those emotions."

[0568] The terminal receives a response from the server and presents it to the user. Throughout this entire process, the user receives support tailored to their needs through the system, resulting in improved customer satisfaction.

[0569] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0570] Step 1:

[0571] The server checks the user's inquiry received from the terminal. The input data is the text data of the inquiry entered by the user on their smartphone. As output, the server prepares this text data for analysis. Specifically, the text data is transferred to the server in an appropriate format.

[0572] Step 2:

[0573] The server uses a natural language processing engine to analyze the received text. Here, OpenAI's GPT model is used to process the data in order to understand the user's question. The input is text data, and the output is the analyzed question content and intent. This process specifically involves analyzing grammatical structure and extracting the question's intent.

[0574] Step 3:

[0575] The server uses a sentiment analysis engine to analyze the user's emotional state from the text. This step uses tools such as the Google Cloud Natural Language API to analyze the sentiment within the text. The input is the user's question text, and the output is sentiment metadata. Specific operations include aggregating positive and negative evaluations of words and calculating sentiment intensity.

[0576] Step 4:

[0577] The server uses the analyzed question content and sentiment metadata to search for relevant information in the database and generate an appropriate response. The input used here is the analyzed question content and sentiment metadata, and the output is multiple adjusted responses. Specifically, it searches for related topics and generates and sorts response candidates.

[0578] Step 5:

[0579] The server adds reference information to each generated response, adjusts the response, and outputs it in a tone appropriate to the user. The input is the generated response content, and the output is the tone-adjusted response text. This process involves the specific action of selecting appropriate expressions for the response sentence.

[0580] 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.

[0581] 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.

[0582] 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.

[0583] [Fourth Embodiment]

[0584] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0585] 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.

[0586] 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).

[0587] 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.

[0588] 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.

[0589] 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).

[0590] 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.

[0591] 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.

[0592] 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.

[0593] 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.

[0594] 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.

[0595] 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.

[0596] 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".

[0597] This invention is an information provision system for efficiently addressing unclear points in business operations. It searches for and presents appropriate information based on questions entered by the user. The following describes the program processing of this system in detail.

[0598] Users input questions by describing unclear points in their work using natural language via their terminal. This input is sent to the server using natural language processing technology. The server analyzes the received questions, extracts relevant keywords and context, and uses this information to access internal databases and knowledge bases to search for relevant documents and past cases.

[0599] Once the search is complete, the server generates multiple answers based on the retrieved information. Using generation AI, it constructs multiple answers in natural language based on the selected information. Each answer is accompanied by references as sources and bibliographies, allowing users to verify the accuracy and origin of the content.

[0600] The terminal displays the answers received from the server to the user, assisting the user in making selections and decisions based on that information. For example, if a user asks a question about troubleshooting a specific piece of business equipment, the server will provide technical documentation related to that equipment, past troubleshooting cases, and solutions based on them. This allows the user to quickly select the appropriate solution and address the problem.

[0601] This system allows users to efficiently obtain information in response to their work-related questions and accelerate decision-making. The collaboration between servers, terminals, and users improves operational efficiency and optimizes information utilization.

[0602] The following describes the processing flow.

[0603] Step 1:

[0604] The user enters a question about something they don't understand into the device using natural language. The device receives this input and prepares to send the data to the server.

[0605] Step 2:

[0606] The server utilizes natural language processing techniques to analyze user questions received from terminals. This involves understanding the content of the questions and extracting relevant keywords and context.

[0607] Step 3:

[0608] The server searches internal databases and external knowledge bases based on the extracted keywords and context. This search identifies relevant technical documents and past case information.

[0609] Step 4:

[0610] The server initiates a process to generate multiple answers based on relevant information. Using generative AI, it constructs answers in natural language from the acquired information.

[0611] Step 5:

[0612] The server provides references to the source of each answer it generates. This allows users to verify the reliability and source of the information provided.

[0613] Step 6:

[0614] The server sends the generated answer and its references to the terminal. The terminal receives this and prepares to present it to the user.

[0615] Step 7:

[0616] Users can view and review multiple responses presented through their device. They can then select the most appropriate solution to aid in decision-making in their work.

[0617] (Example 1)

[0618] 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".

[0619] In today's business environment, users are required to obtain various information quickly and accurately to make decisions. However, there is a lack of efficient means to quickly search for appropriate information in response to questions that may contain uncertainties, and to provide it in an easily understandable format. This problem is particularly serious in the current situation where business processes are becoming more complex and the volume of data is increasing.

[0620] 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.

[0621] In this invention, the server includes means for analyzing received information, including unknown points, using natural language processing technology to extract important features; means for searching for knowledge resources based on the extracted important features; and means for creating multiple answers using a generative AI model based on the search results. This enables users to obtain answers to their questions quickly and efficiently.

[0622] "Information containing unclear points" refers to documents or data that express issues or questions that users do not fully understand due to a lack of knowledge or insufficient verification.

[0623] "Natural language processing technology" is a general term for the technologies and methods that enable computers to understand, analyze, and process human language.

[0624] "Key features" refer to keywords, contexts, or other relevant information that are deemed particularly relevant from the information presented.

[0625] "Knowledge resources" refer to data, documents, case studies, and databases or other means of information aggregation that store them in a specific domain.

[0626] A "generative AI model" is an algorithm or system that uses artificial intelligence technology to perform pre-training and possesses the ability to generate natural language responses or answers to new information.

[0627] "Source information" refers to the information sources and documents referenced by the generated answer, and is information used to demonstrate the reliability of the information.

[0628] "Information and communication equipment" is a general term for devices and equipment that transmit and receive data, and includes computers, smartphones, tablets, etc.

[0629] This invention is an information provision system that provides quick and appropriate responses to unclear points in business operations. Its purpose is to provide appropriate information tailored to the user's question, which is entered in natural language.

[0630] Users input their questions in natural language using their device and send the information to the server. This device can be a standard computer, smartphone, or tablet. The input information is transferred from the device to the server using a secure communication method (e.g., HTTPS).

[0631] The server analyzes the received information. This analysis utilizes natural language processing techniques, and well-known libraries such as NLTK and spaCy can be used. The server extracts key features from the information and searches for knowledge resources based on them. The databases used here are assumed to be internal knowledge bases or cloud-based databases.

[0632] The server uses a generative AI model to generate multiple answers based on information that matches the extracted key features. In this scenario, OpenAI's GPT series is a typical generative AI model to use. Each generated answer is accompanied by source information, which allows the user to be confident in the reliability of the information.

[0633] The terminal's role is to quickly display the answers received from the server to the user. Based on the presented answers, the user can select the most suitable solution to the problem and apply it to their work.

[0634] As a concrete example, if a user wants to know the "installation procedure for new software," they send the question to the server via their terminal. The server analyzes this question, searches relevant installation manuals and FAQ databases, and generates an answer in natural language using a generative AI model. The generated answer is then presented to the user along with the source of the information.

[0635] An example of a prompt message would be, "Please tell me the general troubleshooting steps when the new business software encounters an error."

[0636] In this way, users can obtain the necessary information quickly and accurately, leading to improved work efficiency and faster decision-making.

[0637] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0638] Step 1:

[0639] The user uses a terminal to input questions in natural language. The terminal receives the input and formats it as text data. After checking whether the input is properly structured and performing error checks, it sends the data to the server to proceed to the next step.

[0640] Step 2:

[0641] The server receives text data sent from the terminal. Here, natural language processing techniques are used to analyze the content of the question. This analysis extracts key keywords and relevant contextual information. For example, if the question contains the phrase "software installation," the software name and error message will also be extracted.

[0642] Step 3:

[0643] The server searches for knowledge resources based on the extracted keywords and contextual information. In this step, it executes search queries against internal knowledge bases and external databases to retrieve relevant information and documents. The information is compiled into a dataset and passed on to the next process.

[0644] Step 4:

[0645] The server uses the acquired information as input to generate answers using a generative AI model. This process organizes and integrates the acquired relevant information, creating multiple answers in natural language. Each answer includes source information to ensure the reliability of the information.

[0646] Step 5:

[0647] The generated answer is sent from the server to the terminal. The terminal receives the answer and presents it to the user via the user interface. The interface layout is dynamically adjusted to allow the user to easily view the answer.

[0648] Step 6:

[0649] Users review multiple displayed solutions and select the one best suited to their problem. After this selection, users can then take concrete action. This enables rapid and effective problem-solving.

[0650] (Application Example 1)

[0651] 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".

[0652] In modern industrial settings, the increasing complexity of machinery necessitates immediate action when uncertainties or malfunctions occur. However, conventional information systems have struggled to provide the necessary information quickly and appropriately to resolve problems with industrial automated machinery. This has hindered improvements in work efficiency in industrial settings.

[0653] 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.

[0654] In this invention, the server includes means for analyzing unstructured data specific to industry and extracting relevant information, means for searching for relevant materials based on the extracted information, and means for displaying the information set on a device that controls industrial automated machinery. This enables rapid and accurate problem solving in industrial settings.

[0655] "Unstructured data" refers to data that does not have a fixed format or structure, and includes text and audio data written in natural language.

[0656] "Analysis" is the process of breaking down received data and identifying the information and patterns contained within it.

[0657] "Related information" refers to additional data or knowledge related to the content of the received data, and may include solutions or reference materials.

[0658] An "information set" is a collection of information obtained through searching or generation, including data and guidelines that are useful for solving problems.

[0659] "Reference information" refers to information added to indicate the reliability or source of an information set, and includes information about the source and author.

[0660] "Industrial automated machinery" refers to automated devices and systems used in manufacturing, factories, and other industrial settings.

[0661] A "control device" is an electronic device used to manage the operation of machinery and equipment and to transmit instructions.

[0662] A "display device" is a device used by humans to visually confirm information and data, and includes screens and displays.

[0663] The system for implementing this invention begins with a user describing their questions in order to solve a problem in an industrial automated machine. The questions and concerns entered by the user are sent to a control device. This device analyzes the data using natural language processing technology and extracts relevant information.

[0664] The server processes the extracted relevant information to search for past documents. At this stage, it accesses an internal database and generates the most relevant set of information based on specific keywords and context. Reference information is added to the generated set to ensure the accuracy of the information.

[0665] The server transmits a set of information to a display device that controls industrial automated machinery, displaying it in real time. This allows users to immediately access the information needed to solve problems on-site. For example, if a machine suddenly stops on a production line, a user can ask, "What caused the machine to stop and how can I get it working again?" and relevant technical documentation and troubleshooting procedures will be displayed.

[0666] The hardware used includes control devices and displays for industrial automated machinery, while the software includes the natural language processing library spaCy and the generative AI model GPT-3.

[0667] As a concrete example, the prompt might be entered as, "Please tell me about possible problems that can occur during the assembly process of product X and how to address them." Based on this prompt, the server analyzes the relevant information and presents the optimal solution.

[0668] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0669] Step 1:

[0670] The user inputs any questions or uncertainties into the control device. These are entered in natural language text format. Receiving this input data prepares the device for the next step.

[0671] Step 2:

[0672] The server analyzes the unclear points received from the user using natural language processing techniques. Specifically, it uses spaCy to tokenize the text data and extract keywords and important phrases. This process generates a dataset for searching for relevant information.

[0673] Step 3:

[0674] The server searches its internal database using the keywords extracted in step 2 to find relevant documents. The retrieved data includes documents detailing past troubleshooting cases and solutions. This builds a knowledge base capable of addressing user questions.

[0675] Step 4:

[0676] The server generates an information set based on relevant materials. Using the GPT-3 generation AI model, it creates multiple natural-sounding answers to the user's questions. The information set also includes reference information indicating the accuracy of the answers. This output is then converted into a format that the user can visually recognize.

[0677] Step 5:

[0678] The server transmits the generated information set to a display device on the control device. The information set is immediately visualized on the display device, allowing the user to easily review the information. Based on the provided information, the user can solve problems related to industrial automated machinery and make rapid decisions.

[0679] 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.

[0680] This invention is an information provision system for quickly addressing unclear points and questions in business operations, and further incorporates an emotion engine that recognizes and responds to the user's emotions. The purpose of this invention is for the user to input a question into the system, which will then provide appropriate information in response to that question, as well as respond in accordance with the user's emotions.

[0681] The user inputs questions about work-related issues into the terminal. Before sending the user's question to the server, the terminal's built-in sentiment engine analyzes the user's emotional state from the input text. The sentiment engine recognizes the emotion from the text of the user's question and provides metadata corresponding to that emotion to the server.

[0682] The server uses natural language processing techniques to analyze the received question, extracts relevant data, and then searches its internal database to gather relevant information. In this process, the server takes metadata from the sentiment engine into consideration to generate a response that is appropriate to the user's emotions. For example, if the sentiment engine determines that the user is confused, the server will generate a more polite and detailed response.

[0683] The generated answers are accompanied by references that verify the reliability of the information sources. Furthermore, the priority of these references is adjusted based on the results of the sentiment engine's analysis, allowing users to confidently verify the information.

[0684] Finally, the terminal presents the user with the server-generated answers and references. By referring to the information provided in a tone and content that matches the user's emotions, the user can efficiently solve business problems. For example, if the server analyzes that the user is in a hurry, it will quickly respond to the user's needs by providing an immediate and concise answer.

[0685] This system helps improve work efficiency by providing a more personalized experience through flexible responses that respond to the user's emotions.

[0686] The following describes the processing flow.

[0687] Step 1:

[0688] The user uses a terminal to input work-related questions or uncertainties in natural language. Upon receiving this input, the terminal activates an emotion engine to analyze the user's emotions from the entered text.

[0689] Step 2:

[0690] The device obtains analysis results regarding the user's emotions and sends them to the server along with the question text. The emotion data is used by the server as supplementary information to understand the context of the question.

[0691] Step 3:

[0692] The server analyzes the received question using natural language processing techniques. It extracts the question's subject and related keywords, and then searches its internal database based on this information to gather relevant data.

[0693] Step 4:

[0694] Based on the information collected by the server, a generative AI is used to create multiple responses. In this process, the server takes into account the emotional data sent from the terminal and creates responses that adjust in tone and complexity according to the user's emotions.

[0695] Step 5:

[0696] The server assigns a source reference to each response it generates. Based on sentiment data, the priority of the references is also adjusted as needed.

[0697] Step 6:

[0698] The server sends the generated response and its references to the terminal. The terminal receives this and displays the appropriately formatted response to the user.

[0699] Step 7:

[0700] Users can review the answers presented through their device and refer to references and additional information as needed. Based on the information provided, users can make quick decisions.

[0701] (Example 2)

[0702] 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".

[0703] The problem that this invention aims to solve is to provide prompt and accurate information regarding unclear points and questions in business operations, as well as to achieve appropriate communication that responds to the user's emotions. Conventional information provision systems lack flexible responses based on the user's emotions, which can result in a decrease in the quality of the user experience and the efficiency of operations.

[0704] 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.

[0705] In this invention, the server includes means for analyzing the sentiment of a question, means for generating metadata based on the sentiment analysis results, and means for analyzing the question and metadata and extracting relevant information. This makes it possible to provide information that takes the user's emotions into account, providing a more personalized experience tailored to each individual user and improving operational efficiency.

[0706] A "means of receiving questions" refers to a method for obtaining text input from the user and an interface for passing that input to the next processing step.

[0707] "Means of analyzing emotions" refers to algorithms and technologies used to identify a user's emotions from input text, utilizing natural language processing and sentiment analysis techniques.

[0708] "Methods for generating metadata" refer to supplementary data constructed based on the results of sentiment analysis, and are part of the information used when generating responses.

[0709] "Means of extracting relevant information" refers to methods of retrieving necessary information from databases and resources based on the user's questions and sentiments.

[0710] "Means of searching records" refers to processes or techniques for finding information related to a question, whether within internal or external data storage.

[0711] "Means of generating answers" refers to the process of assembling and displaying information appropriate to a user's question, based on search results and sentiment information.

[0712] "Means of providing data that proves the source of information" refers to methods of providing additional information that indicates the source of the information in order to guarantee the reliability of the information being provided.

[0713] "Means of presenting answers" refers to methods of displaying generated information and data that proves its reliability to the user, and which are adjusted to respond to the user's emotions.

[0714] This invention is an information provision system that addresses users' work-related uncertainties and questions, and is equipped with a function to recognize and respond to the user's emotions. The system is implemented using the following hardware and software.

[0715] Hardware and software configuration

[0716] The terminal is equipped with an emotion engine to receive user input and perform sentiment analysis. The server has the processing power to analyze received questions using natural language processing technology. This uses a specific generative AI model. An internal database provides high-speed search functionality to support information retrieval. The emotion engine and natural language processing technology are implemented with specialized algorithms and machine learning models that can analyze sentiment and meaning from text-based input.

[0717] Data processing and calculations

[0718] The terminal receives user input text, preprocesses it, and performs sentiment analysis using a sentiment engine. The server uses natural language processing algorithms to analyze the content of the question and extracts relevant information from the database. In this process, the sentiment analysis results are used as metadata to generate a response that corresponds to the user's emotions. The generated response is given a reference to prove its reliability.

[0719] Specific example

[0720] As a concrete example, consider a scenario where a user enters a question such as, "I don't know how to proceed with the new project." In this case, the sentiment engine analyzes that the user is confused. Based on this, the server generates a detailed and thorough explanation of how to proceed, and presents it with references to reliable sources. Conversely, if the analysis indicates that the user is in a hurry, a concise and to-the-point answer is provided immediately.

[0721] Example of a prompt

[0722] "I'm confused about how to proceed with the project. Could you please explain the detailed steps?"

[0723] This system aims to provide a better user experience by taking user emotions into consideration and delivering information quickly and accurately.

[0724] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0725] Step 1:

[0726] Users input work-related questions or uncertainties into a terminal. This input is in text format and includes the user's problems and information requests. The terminal passes the input text to a sentiment engine. This engine analyzes the input text and uses natural language processing techniques to identify the emotional state. As a result, metadata indicating the user's emotions is generated.

[0727] Step 2:

[0728] The device sends the metadata obtained from sentiment analysis and the original question to the server. The server analyzes the received question using natural language processing algorithms to extract the main theme and related keywords. This analysis step involves a generative AI model, which provides an appropriate structural understanding of the question. The output is structured data of the question.

[0729] Step 3:

[0730] The server uses the structured data of the question to search its internal database and extract relevant information. Here, the database search is performed based on keywords and themes to identify the information most relevant to the user's question. This process involves generating search queries and retrieving information. The retrieved data is then grouped into the most relevant sets of information for the question.

[0731] Step 4:

[0732] The server combines the compiled relevant information with previously obtained sentiment metadata to generate the most appropriate response for the user. Here, a generative AI model is used to adjust the tone and content to match the user's emotions. If the emotion is "confused," a detailed and polite response is generated; if the emotion is "urgent," a concise response is generated. The output is the adjusted user response.

[0733] Step 5:

[0734] The server adds references to the adjusted user response to prove the source of the information. This process adds metadata indicating the source of citations and data to the response to ensure the reliability of the information provided. This allows the user to evaluate the reliability of the presented information.

[0735] Step 6:

[0736] The device presents the user with the final answer and references sent from the server. Specifically, it either displays the answer on the device's screen or provides the information verbally using speech synthesis. This step allows the user to receive personalized information tailored to their emotions.

[0737] (Application Example 2)

[0738] 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".

[0739] In modern information systems, when users make inquiries that include unclear points, it is necessary not only to provide information but also to respond in a way that takes the user's emotions into consideration. However, conventional systems have difficulty adjusting the tone of response according to emotions, which can lead to user dissatisfaction. In particular, in e-commerce sites, appropriate responses that respond to the user's emotions lead to customer satisfaction, so there is a need for a system that links emotion recognition with information provision.

[0740] 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.

[0741] In this invention, the server includes means for receiving queries, means for analyzing the received queries and extracting relevant information, and means for analyzing the user's emotions using sentiment analysis technology and generating metadata. This makes it possible to provide responses with a tone that matches the user's emotions.

[0742] An "inquiry" is a question or request made in order to obtain information.

[0743] "Analysis" is the act of analyzing specific data or information and extracting meaning and relationships from it.

[0744] "Extraction" refers to selecting necessary elements from data or information.

[0745] "Searching" is the act of examining databases and documents in order to find the information you are looking for.

[0746] "Response" refers to a reply or answer to an inquiry.

[0747] "Reference information" refers to supplementary information added to a response that indicates its reliability or source.

[0748] "User" refers to an individual or legal entity that uses the system.

[0749] "Sentiment analysis" is a technique for identifying a speaker's emotions from written or spoken text.

[0750] "Metadata" refers to supplementary information used to explain other data.

[0751] "Natural language processing" is a technology that enables computers to understand, interpret, and generate human language.

[0752] Implementing this invention requires building a system that processes user inquiries and adjusts responses based on emotions. This system improves the user experience by recognizing the user's emotions and providing information in an appropriate tone.

[0753] When the server receives a query, it first analyzes its content using a natural language processing engine. For example, OpenAI's GPT model can be used for this analysis. Next, a sentiment analysis engine determines the user's emotions, and metadata is generated based on the results. Services such as the Google Cloud Natural Language API can be used for this sentiment analysis.

[0754] Based on the generated metadata, the server adjusts the tone of its response to provide the user with appropriate information. This adjustment involves modifying the style and level of detail in the response to achieve optimal communication tailored to the user's emotions. For example, if a user is confused, the server will generate a detailed and reassuring response to help alleviate their anxiety.

[0755] An example of a prompt message might be: "Analyze the text to indicate the user's emotions and explain the return policy in a tone that matches those emotions."

[0756] The terminal receives a response from the server and presents it to the user. Throughout this entire process, the user receives support tailored to their needs through the system, resulting in improved customer satisfaction.

[0757] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0758] Step 1:

[0759] The server checks the user's inquiry received from the terminal. The input data is the text data of the inquiry entered by the user on their smartphone. As output, the server prepares this text data for analysis. Specifically, the text data is transferred to the server in an appropriate format.

[0760] Step 2:

[0761] The server uses a natural language processing engine to analyze the received text. Here, OpenAI's GPT model is used to process the data in order to understand the user's question. The input is text data, and the output is the analyzed question content and intent. This process specifically involves analyzing grammatical structure and extracting the question's intent.

[0762] Step 3:

[0763] The server uses a sentiment analysis engine to analyze the user's emotional state from the text. This step uses tools such as the Google Cloud Natural Language API to analyze the sentiment within the text. The input is the user's question text, and the output is sentiment metadata. Specific operations include aggregating positive and negative evaluations of words and calculating sentiment intensity.

[0764] Step 4:

[0765] The server uses the analyzed question content and sentiment metadata to search for relevant information in the database and generate an appropriate response. The input used here is the analyzed question content and sentiment metadata, and the output is multiple adjusted responses. Specifically, it searches for related topics and generates and sorts response candidates.

[0766] Step 5:

[0767] The server adds reference information to each generated response, adjusts the response, and outputs it in a tone appropriate to the user. The input is the generated response content, and the output is the tone-adjusted response text. This process involves the specific action of selecting appropriate expressions for the response sentence.

[0768] 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.

[0769] 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.

[0770] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0771] 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.

[0772] 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.

[0773] 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.

[0774] 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.

[0775] 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.

[0776] 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."

[0777] 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.

[0778] 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.

[0779] 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.

[0780] 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.

[0781] 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.

[0782] 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.

[0783] 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.

[0784] 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.

[0785] 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.

[0786] 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.

[0787] 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.

[0788] 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.

[0789] The following is further disclosed regarding the embodiments described above.

[0790] (Claim 1)

[0791] A means of receiving questions that include unclear points,

[0792] A means of analyzing the received questions and extracting relevant information,

[0793] A means of searching for past materials based on the extracted related information,

[0794] A means of generating multiple answers based on search results,

[0795] A means of assigning references to each generated answer,

[0796] A means of presenting users with multiple answers,

[0797] A system that includes this.

[0798] (Claim 2)

[0799] The system according to claim 1, which analyzes a question using natural language processing technology.

[0800] (Claim 3)

[0801] The system according to claim 1, which presents the generated response to the user via a terminal.

[0802] "Example 1"

[0803] (Claim 1)

[0804] Means of receiving information that includes unclear points,

[0805] A means of analyzing received information using natural language processing technology and extracting important features,

[0806] A means of searching for knowledge resources based on the extracted key features,

[0807] A method for generating multiple answers using a generative AI model based on search results,

[0808] A means of attaching source information to each answer created,

[0809] A means of providing users with multiple answers,

[0810] A system that includes this.

[0811] (Claim 2)

[0812] The system according to claim 1, which generates an answer in natural language using a generative AI model.

[0813] (Claim 3)

[0814] The system according to claim 1, which provides the generated answer to the user via an information and communication device.

[0815] "Application Example 1"

[0816] (Claim 1)

[0817] A means of receiving unstructured data that includes unclear points,

[0818] A means of analyzing received unstructured data and extracting relevant information,

[0819] A means of searching for past materials based on the extracted related information,

[0820] A means for generating multiple sets of information based on search results,

[0821] A means for assigning reference information to each generated information set,

[0822] A means of presenting users with multiple sets of information,

[0823] A means for displaying a set of information on a device that controls industrial automated machinery,

[0824] A system that includes this.

[0825] (Claim 2)

[0826] The system according to claim 1, which analyzes unstructured data using natural language processing technology.

[0827] (Claim 3)

[0828] The system according to claim 1, which presents the generated information set to the user via a display device.

[0829] "Example 2 of combining an emotion engine"

[0830] (Claim 1)

[0831] A means of receiving questions that include unclear points,

[0832] A means of analyzing the emotions in response to the received question,

[0833] A means for generating metadata based on sentiment analysis results,

[0834] A means for analyzing questions and metadata and extracting relevant information,

[0835] A means of searching records based on the extracted relevant information,

[0836] A means for generating an answer based on search results and sentiment analysis results,

[0837] A means of attaching data that proves the source of information to each generated answer,

[0838] A means of presenting each answer in a way that is adapted to the user's emotions,

[0839] A system that includes this.

[0840] (Claim 2)

[0841] The system according to claim 1, which analyzes a question using natural language processing technology and generates an answer that takes into account information based on sentiment analysis.

[0842] (Claim 3)

[0843] The system according to claim 1, which presents generated responses and data proving the source of information via a terminal in a manner that corresponds to the user's emotions.

[0844] "Application example 2 when combining with an emotional engine"

[0845] (Claim 1)

[0846] A means of receiving inquiries that include unclear points,

[0847] A means of analyzing received inquiries and extracting relevant information,

[0848] A means of searching for past materials based on the extracted related information,

[0849] Means for generating multiple responses based on the search results,

[0850] A means for attaching reference information to each generated response,

[0851] A means of presenting the user with multiple responses,

[0852] A means of analyzing user emotions and generating metadata,

[0853] A means for adjusting the tone of the response based on the generated metadata,

[0854] A system that includes this.

[0855] (Claim 2)

[0856] The system according to claim 1, which analyzes queries using natural language processing techniques and generates metadata using sentiment analysis techniques.

[0857] (Claim 3)

[0858] The system according to claim 1, which presents the generated response to the user via an information terminal. [Explanation of Symbols]

[0859] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of receiving unstructured data that includes unclear points, A means of analyzing received unstructured data and extracting relevant information, A means of searching for past materials based on the extracted related information, A means for generating multiple sets of information based on search results, A means for assigning reference information to each generated information set, A means of presenting users with multiple sets of information, A means for displaying a set of information on a device that controls industrial automated machinery, A system that includes this.

2. The system according to claim 1, which analyzes unstructured data using natural language processing technology.

3. The system according to claim 1, which presents the generated information set to the user via a display device.

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