system

Abstract

Provide a system. 【Solution means】 Means for collecting data from an information processing device and creating an information index based on it, Means for analyzing a question in natural language from a user and extracting relevant phrases, Means for searching for relevant information from the index based on the extracted phrases, Means for scrutinizing and selecting relevant information from the search results and generating a summary, Means for providing the generated summary information to the user's terminal, Means for converting voice input in natural language into text, Means for selecting an optimal route based on past traffic data and real-time traffic information, Means for presenting the selected route information by voice and visually, A system including the above.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds 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] The personalization of information within an enterprise and inefficient searches pose constraints on business operations for new employees and transferees, making it difficult to timely grasp necessary information and past cases. As a result, the speed and efficiency of business operations have declined, affecting the overall performance.

Means for Solving the Problems

[0005] This invention creates an information index based on data collected from an information processing device, analyzes natural language questions from users to extract relevant terms, searches the index for relevant information based on the extracted terms, and generates a summary of the refined and selected information from the search results. The summary information is provided to the user's terminal, aiming to expedite information retrieval and prevent reliance on individual expertise. Furthermore, by using an automated learning algorithm, feedback from users is analyzed to improve the accuracy of the information refinement and selection process. In addition, by registering newly acquired information from users in a shared information base and updating it as searchable information, efficient knowledge sharing within the company is realized.

[0006] An "information processing device" is an electronic device designed to collect and process data.

[0007] "Data" refers to a collection of information stored in digital format, which serves as the foundation for obtaining useful knowledge through processing and analysis.

[0008] An "information index" is a reference list that is organized and structured to allow for effective retrieval of information within data.

[0009] "Natural language" is the language that humans use on a daily basis, which is parsed by programs and used to understand the intent of questions.

[0010] A "summary" is information that extracts the key points from detailed information or documents and presents them in a concise manner.

[0011] A "terminal" is a computer or electronic device used by a user to input and output information.

[0012] An "automatic learning algorithm" is a mechanism in which a program learns independently based on past data and experience, improving its accuracy and performance.

[0013] A "shared information base" is a centrally managed data storage system for sharing and accumulating information within an organization. [Brief explanation of the drawing]

[0014] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This 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] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This 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] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This 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] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

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

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

[0017] In the following embodiments, a processor with a reference numeral (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 RAM (Random Access Memory) with a reference numeral 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 storage with a reference numeral is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, 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 relates to an information system configured to efficiently manage and retrieve information within a company, enabling users to quickly obtain the information they need. This system includes multiple information processing devices, each playing a role in data collection, analysis, retrieval, summarization, and presentation.

[0036] Users connect to the system via a terminal and input questions in natural language. The server receives these questions, analyzes their intent using natural language processing technology, and extracts relevant keywords. Based on this, the server searches for relevant information from an indexed information database within the company. The search results are refined by an AI model, and information deemed highly relevant is selected. The selected information is summarized and presented to the user via the terminal. At this stage, users can obtain the necessary information and utilize it in their work.

[0037] For example, if a user asks, "Please tell me about past success stories in new product development," the server will extract the keywords "new product," "development," and "success stories." Based on this, it will search for past project reports and documents that detail success stories, summarize the information indicating the factors behind those successes, and present it to the user.

[0038] Furthermore, through user feedback, the server utilizes an automated learning algorithm to improve the accuracy of the information. When users gain new insights, they can add them to the shared information base, resulting in the efficient use of knowledge throughout the organization. In this way, information becomes siloed and operational efficiency is improved.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] Users connect to the system using a terminal and input questions in natural language. These questions might take the form of, for example, "I would like to know about past success stories in new product development."

[0042] Step 2:

[0043] The server passes the user's question to a natural language processing engine, which analyzes the intent of the question. Important keywords and phrases are extracted, and these are used to prepare the data necessary for information retrieval.

[0044] Step 3:

[0045] The server uses a pre-built information index to search for related documents and data based on extracted keywords. The information index is designed to efficiently search the vast amount of data within the company.

[0046] Step 4:

[0047] The server analyzes the relevant information collected through searches and uses an AI model to evaluate the importance and relevance of the information. Based on the evaluation results, it selects the most relevant information.

[0048] Step 5:

[0049] The server uses a summarization algorithm to summarize the selected information and format it in a way that is easy for the user to understand. This summary includes key points and insights.

[0050] Step 6:

[0051] The server sends the generated summary information to the terminal and presents it to the user. The user reviews this summary and uses the necessary parts for their work.

[0052] Step 7:

[0053] Users can provide feedback based on the information presented. The server receives this feedback and uses an automated learning algorithm to improve the system's accuracy, which is then reflected in future searches and summaries.

[0054] Step 8:

[0055] By registering newly acquired knowledge and information in a shared information base, users can accumulate and share information. Other users can also search this data, promoting efficient knowledge utilization throughout the organization.

[0056] (Example 1)

[0057] 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."

[0058] Modern businesses generate vast amounts of information daily, requiring efficient management and rapid retrieval. Furthermore, extracting the information users need and providing it in a format usable for their work is challenging. Preventing information from becoming tied to specific individuals and sharing knowledge across the entire organization to improve operational efficiency are also key issues.

[0059] 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.

[0060] In this invention, the server includes means for collecting data from an information processing device and creating indicators based thereon, means for analyzing natural language inquiries from users and extracting relevant expressions, and means for scrutinizing and selecting relevant information from search results using a generative AI model. This enables users to efficiently search for information and quickly obtain appropriately summarized information.

[0061] An "information processing device" refers to a computing device that has the function of collecting, managing, and analyzing data.

[0062] An "indicator" refers to a structured dataset designed to effectively search and utilize information.

[0063] "Natural language" refers to the language that humans use on a daily basis, and the text that is input into a computer system in a format that can be analyzed.

[0064] A "generative AI model" refers to an algorithm used to analyze and select data based on its relevance using artificial intelligence.

[0065] A "summary" refers to a document that extracts the main points from a vast amount of information and expresses them concisely.

[0066] "Feedback" refers to evaluations and opinions provided by users after using the system, and the system is improved based on this feedback.

[0067] A "shared knowledge base" refers to a collection of information and know-how that is managed to be easily accessible to members within an organization.

[0068] The information management system of the present invention aims to efficiently collect, analyze, search, and present to users the large amount of information generated within a company. The embodiments for carrying out the invention are described in detail below.

[0069] User operation

[0070] Users input questions into the system using natural language via their own devices. For example, they might ask, "Please tell me about the progress of new product development." At this stage, no specific hardware is required, but general-purpose computers and smart devices are expected.

[0071] Server Processing

[0072] The server receives natural language input sent by the user. For text analysis, the server utilizes natural language processing libraries such as SpaCy and NLTK to analyze the intent of the input sentence. Through this analysis, important terms such as "new product," "development," and "progress" are extracted.

[0073] Subsequently, the server uses a database management system such as PostgreSQL or MySQL (registered trademark) to search the company's information database. Based on the extracted terms, it generates queries and retrieves relevant information.

[0074] The server passes the search results to a generating AI model. This model utilizes advanced AI technologies such as GPT and BERT to select information that is highly relevant to the user's question.

[0075] The selected information is summarized concisely based on a summarization algorithm. The summarized information is formatted to allow users to quickly understand it, by removing excessive details.

[0076] Presentation of results

[0077] Ultimately, the server sends the summarized information to the terminal, providing it to the user. This allows the user to utilize the information they were looking for in their work.

[0078] Using Feedback

[0079] Users can provide feedback on the accuracy and satisfaction level of the information provided. This feedback is received by the server using an automated learning algorithm and used to continuously improve the accuracy of the information provided. In addition, if users gain new insights, they can add them to the shared knowledge base and update it so that it can be effectively used throughout the organization.

[0080] Presentation of specific examples

[0081] Example prompt: "Please provide a report on the results of your marketing campaigns over the past year."

[0082] In this specific example, the server uses keywords such as "marketing campaign," "results," and "past year" to consolidate, summarize, and present relevant information.

[0083] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0084] Step 1:

[0085] The user uses a terminal to input a question in natural language. For example, they might type, "I'd like to know the progress of the new product development." This input is sent to the server as a prompt to the system.

[0086] Step 2:

[0087] The server receives prompt messages sent from the terminal and performs analysis using a natural language processing engine. Specifically, it uses libraries such as SpaCy and NLTK to identify the subject, predicate, and object from the input sentence and extract keywords such as "new product," "development," and "progress." This analysis converts the data into a structured format.

[0088] Step 3:

[0089] The server uses the extracted keywords to issue queries to a database management system (e.g., PostgreSQL or MySQL). The database searches for relevant documents and information and returns the search results to the server. Here, the information collected by the database is structured as key-data pairs.

[0090] Step 4:

[0091] The server uses a generative AI model (e.g., BERT or GPT) to scrutinize the search results obtained from the database. The generative AI model analyzes a large amount of text data and selects the information most relevant to the user's prompt. In doing so, it performs natural language processing with contextual understanding and highlights the selected information.

[0092] Step 5:

[0093] The server processes highly relevant information using a summarization algorithm. The summarization process employs rule-based or neural network-based techniques to eliminate redundancy while preserving key points. As a result, a concise summary is generated.

[0094] Step 6:

[0095] The server sends the generated summary information to the terminal and presents it to the user. The user can easily understand the provided information and use it as a reference for their work. This final output completes the information provision cycle for the entire system.

[0096] (Application Example 1)

[0097] 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."

[0098] In autonomous vehicles, obtaining real-time optimal route information remains a challenge. In particular, it is essential to accurately analyze the intent behind user inquiries in natural language and combine historical traffic data with real-time information to select the optimal route. To address this challenge, a system is needed that can rapidly process voice input and perform highly accurate route selection using both past and present data.

[0099] 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.

[0100] In this invention, the server includes means for converting natural language voice input into text, means for selecting the optimal route based on past traffic data and real-time traffic information, and means for presenting the selected route information via voice and visual means. This makes it possible to quickly analyze a user's voice inquiry in natural language and provide the optimal travel route.

[0101] An "information processing device" is a device that performs information processing such as data collection, analysis, retrieval, summarization, and presentation.

[0102] "Natural language" refers to the language that humans use on a daily basis, and includes forms of expression such as text and speech.

[0103] An "index" is a categorized and organized reference list used to efficiently search for information.

[0104] "Traffic data" refers to data that includes travel information such as past and present traffic flow, congestion status, and accident information.

[0105] "Real-time information" refers to the latest information based on events currently in progress, and is acquired in real time.

[0106] A "route" refers to the path taken from the starting point to the destination, and is selected to ensure efficient travel.

[0107] "Voice input" refers to a method in which users provide instructions or information to an information system using their voice.

[0108] "Visual presentation" refers to a means of providing information visually through a screen or display.

[0109] This document describes embodiments for carrying out the invention. The system that realizes an application example of this invention is designed to be incorporated into the navigation system of an autonomous vehicle. The server converts the user's voice into text using speech recognition software. In this process, speech recognition technologies such as Google® Speech API are utilized. The transcribed questions are analyzed using natural language processing algorithms (e.g., spaCy or GPT model) to clarify the intent of the questions.

[0110] Based on the keywords extracted, the server selects the optimal route by combining historical traffic databases and real-time traffic information. SQL databases and the Google Maps API are used for data retrieval and analysis, and an AI model using TENSORFLOW® selects the most relevant routes.

[0111] The selected route is displayed on the terminal's screen and also provided as audio guidance. This combination of visual presentation and audio guidance supports users in managing their travel.

[0112] As a concrete example, consider a scenario where a user voice-inputs, "Tell me the fastest route home that avoids congestion," during a busy weekend. In this case, the system analyzes past congestion data and current traffic conditions to select the optimal route. An example of a prompt to the generated AI model would be, "Design a system that calculates and presents the shortest route based on the driver's current location and destination, which are entered in natural language."

[0113] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0114] Step 1:

[0115] The user provides voice input. The device acquires this voice data and converts it to text using the Google Speech API. In this step, the input is voice data, and the output is the corresponding text data.

[0116] Step 2:

[0117] The server analyzes the transcribed questions using natural language processing algorithms. It employs tools like spaCy and the GPT model to extract important keywords and phrases. The input is text data, and the output is a list of extracted keywords.

[0118] Step 3:

[0119] The server searches a historical traffic database using SQL queries based on the extracted keywords, and also retrieves real-time traffic information using the Google Maps API. The input for this step is a list of keywords, and the output is historical and real-time traffic data.

[0120] Step 4:

[0121] The server uses an AI model powered by TensorFlow to select the optimal route based on the acquired traffic data. This process calculates an efficient route by processing a large amount of data, including past trends and current conditions. The input is traffic data, and the output is optimal route information.

[0122] Step 5:

[0123] The server transmits selected route information to the terminal, which then provides this information to the user in the form of visual and audio presentations. The input is optimal route information, and the output is a route presentation in a format that the user can understand.

[0124] 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.

[0125] This invention relates to an information management system that recognizes user emotions and utilizes that information to optimize information provision. This system includes an information processing device and operates by combining multiple emotion engines.

[0126] Users access the system via a terminal and input questions and feedback in natural language. The server receives this information and first analyzes the content of the questions and feedback using a natural language processing engine. Simultaneously, an emotion engine recognizes the user's emotions from the input text and audio data and analyzes their emotional state.

[0127] The server uses keywords extracted through natural language processing to search for relevant documents and data by referring to an information index. Information obtained from the sentiment engine is used to evaluate the search results. In other words, the prioritization of information is adjusted according to the user's emotional state; for example, if the user is anxious, a more concise and direct answer is provided.

[0128] Once the information is identified, the server uses an AI model to examine it and a summary generation algorithm creates an appropriate summary. During this process, sentiment data from the sentiment engine is used to adjust the content and presentation of the summary, ensuring that the information is presented in a way that suits the user's current psychological state.

[0129] As a concrete example, when a user types "I want to know the latest progress on Project X," the server analyzes the question, and the emotion engine determines the user's current emotions (e.g., frustration, anxiety). Based on this information, the server searches for relevant progress reports, generates a summary tailored to the user's emotions (e.g., including encouragement and positive elements), and presents it to the terminal.

[0130] Furthermore, the system analyzes user sentiment data and feedback using an automated learning algorithm to improve the accuracy of information retrieval and summarization processes. This allows the system to continuously improve over time, enabling more personalized information delivery. Additionally, users can register newly acquired information in a shared information base, making it searchable by other users and strengthening knowledge sharing within the company. This approach prevents information from becoming tied to specific individuals and improves operational efficiency.

[0131] The following describes the processing flow.

[0132] Step 1:

[0133] The user connects to the system via a terminal and inputs questions in natural language. At the same time, voice data and text data, including the user's emotions, are also sent to the system.

[0134] Step 2:

[0135] The server receives input data from the user and first uses a natural language processing engine to analyze the content of the question. Here, it extracts important keywords and phrases to understand the context and intent of the question.

[0136] Step 3:

[0137] Simultaneously, the server activates an emotion engine to analyze the user's emotional state from the input voice and text. For example, it can determine emotions such as impatience, anxiety, or joy from voice tone and context.

[0138] Step 4:

[0139] The server uses information obtained from natural language processing and sentiment analysis to refer to an information index and search for documents and information in relevant databases. Here, sentiment information is used to evaluate the importance of the search results.

[0140] Step 5:

[0141] The server evaluates the searched information and uses an AI model to refine its content. It adjusts the importance and display order of information according to the user's emotional state to determine which information is most useful.

[0142] Step 6:

[0143] The server utilizes a summarization algorithm to summarize information in a way that takes the user's emotions into consideration. For example, if the information is deemed urgent, it will choose clearer and more direct language.

[0144] Step 7:

[0145] The server sends the generated summary to the terminal and presents it to the user. The user receives this information and uses it for their work as needed.

[0146] Step 8:

[0147] Users may provide feedback on the information they provide, and the server analyzes this feedback to improve processing accuracy through an automated learning algorithm. This learning process improves the accuracy of the system's information provision and user satisfaction.

[0148] (Example 2)

[0149] 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".

[0150] In modern information systems, users are required to quickly and appropriately retrieve necessary information from a vast amount of data. However, information provision that responds to users' emotional states is insufficient. For example, users may become confused by too much information, or their stress may increase due to irrelevant information. Furthermore, system improvements over time are limited, meaning that the information users receive is not always optimal.

[0151] 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.

[0152] In this invention, the server includes means for receiving natural language data input from a user via a terminal, natural language processing means for analyzing the received natural language data and extracting themes and important words, and emotion recognition means for recognizing emotions from the user's input data and using that emotion information for information evaluation. This makes it possible to provide information according to the user's emotional state, enabling more personalized services. Furthermore, by using an automated learning algorithm, continuous system improvement can be achieved.

[0153] A "natural language processing method" is a processing method that analyzes natural language data received from a user and extracts themes and important words.

[0154] An "emotion recognition method" is a processing method that recognizes the emotional state from the user's input data and evaluates the information provided based on that information.

[0155] A "generative AI model" is an artificial intelligence model used to analyze information and generate summaries while considering the user's emotional state.

[0156] An "automatic learning algorithm" is a data processing method used to analyze user feedback and sentiment data to improve the accuracy of system information scrutiny and selection methods.

[0157] A "shared information base" is a database system that allows users to register newly acquired information and make it available to other users as searchable information.

[0158] This invention relates to an information management system that recognizes and utilizes user emotions in order to optimize information delivery. This system operates by combining a server, terminals, and software such as an emotion recognition engine and a natural language processing engine.

[0159] The server is responsible for receiving natural language data entered by the user through their device. The received data is analyzed by a natural language processing engine, and the main topic and important words are extracted. At the same time, the server uses an emotion recognition engine to recognize the user's emotional state from the input data. Based on this emotional information, the server evaluates the relevant information and generates a summary. In summary generation using a generative AI model, appropriate expressions are provided according to the user's current emotions.

[0160] As a concrete example, consider a scenario where a user inputs "I want to know the latest progress on Project X" through a terminal. In this case, the server analyzes the question and evaluates the emotions the user may be feeling (e.g., frustration or anxiety). Based on this information, the server searches for relevant progress reports and generates a summary that takes the user's emotions into account, providing more positive and easy-to-understand information.

[0161] An example of a prompt might be, "Please tell me about the project's progress. I'm feeling a little anxious right now." Based on this prompt, the system will provide information tailored to the user's emotions.

[0162] In this way, by combining servers, terminals, and advanced AI technology to recognize user emotions and utilize that information, the accuracy of information provision is improved, and a more personalized user experience is realized.

[0163] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0164] Step 1:

[0165] The server receives natural language data entered by the user from the terminal. This input data is in text or audio format and contains the information the user is seeking. The server treats this data as initial input and prepares it for subsequent processing steps.

[0166] Step 2:

[0167] The server sends the received natural language data to the natural language processing engine. The natural language processing engine analyzes the text or audio data to extract the subject and important keywords. Techniques such as morphological analysis and syntactic analysis are used for this data processing, and ultimately the relevant keywords are output.

[0168] Step 3:

[0169] The server sends the extracted keywords to the emotion recognition engine. The emotion recognition engine infers emotions from the user's input and uses the results to analyze the emotional state. This process evaluates word selection and context to obtain emotional data as output. This information is used later for information evaluation and summarization.

[0170] Step 4:

[0171] The server searches for relevant information by referencing the system's information index based on keywords extracted through natural language processing and sentiment information obtained through sentiment recognition. This search process evaluates relevant documents and information within the database and retrieves the information that best suits the user's needs as output.

[0172] Step 5:

[0173] The server sends the acquired relevant information to the generating AI model. The generating AI model examines this information and generates a summary. Here, information is selected and its expression is adjusted according to the user's emotional state. The output of the AI ​​model is a summary processed in a way that is optimal for the user.

[0174] Step 6:

[0175] The server sends the generated summary to the terminal and presents it to the user. This allows the user to receive information in a way that takes their emotions into consideration, enabling them to enjoy a more meaningful information experience.

[0176] (Application Example 2)

[0177] 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".

[0178] In modern society, information overload makes it difficult for users to access necessary information quickly and appropriately, sometimes leading to emotional burdens. Furthermore, because the information provided is not optimized for users' emotions, there is a challenge in that users are unable to effectively utilize that information.

[0179] 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.

[0180] In this invention, the server includes means for collecting data from an information processing device and creating an information index based thereon, means for analyzing the user's emotions and adaptively adjusting the information provided based on that analysis, and means for providing content based on the user's emotions. This enables the user to quickly access information and content that is appropriate to their emotional state.

[0181] An "information processing device" is a device that includes hardware and software for collecting user data and supporting the indexing and retrieval of information.

[0182] "Data" refers to all input information collected from users, including text data and audio data.

[0183] An "information index" is a structure for efficiently searching for information within a database built on data.

[0184] "Users" refer to individuals or end-users who utilize this system, and information is provided to them according to their feelings and requests.

[0185] "Natural language" refers to the forms of language that people use on a daily basis, and is expressed in forms such as text and audio.

[0186] "Related terms" are important words and phrases included in natural language input from users, and are key elements in information retrieval.

[0187] "Methods of searching" refers to the process of systematically finding relevant information using an index.

[0188] "Means of analyzing emotions" refer to technologies used to identify a user's emotional state and reflect it in the information provided.

[0189] "Means of providing content" refers to the process of presenting information and materials in a format that is appropriate to the user's emotions.

[0190] This invention is a system for optimizing information provision based on user emotions using an information processing device. The server collects user data from terminals such as smartphones and smart glasses and creates an information index. Questions and feedback entered by the user in natural language are analyzed using a natural language processing engine and relevant words are extracted. Based on the extracted words, the server searches the index for relevant information and then scrutinizes and selects that information.

[0191] In addition, the server uses an emotion analysis module to analyze user emotions in real time. Specifically, it leverages the Google Cloud Vision API and Amazon Rekognition to identify user emotions from image and audio data. This emotion analysis is used to adjust the priority of information provision and deliver content accordingly.

[0192] The generated information and content are further refined by an AI model and summarized in a way that is suitable for the user. At this stage, the results of sentiment analysis are incorporated into the summarization algorithm.

[0193] For example, if a user searches for information on their smartphone while at work, and emotional analysis reveals that they are experiencing "stress," the server will prioritize displaying information or entertainment content that has a relaxing effect on that user.

[0194] An example of a prompt to input into the generative AI model might be: "Generate recommendations based on the user's current emotions. The user is feeling stressed." This prompt allows the system to provide optimized information.

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

[0196] Step 1:

[0197] The device collects text and voice data from the user. Text and voice input are acquired via the microphone and camera and sent to the server. Here, data preprocessing is performed using the Google Cloud Vision API and Amazon Rekognition.

[0198] Step 2:

[0199] The server analyzes the received data using a natural language processing engine, extracting relevant terms from the input questions and feedback. This analysis serves as a key for referencing the information index. The output is a list of the analyzed terms, which is used to proceed to the next search step.

[0200] Step 3:

[0201] The server uses an emotion analysis module to identify the user's emotional state from text and audio data. The input is the raw data obtained in step 1, and the output is an emotion label (e.g., relaxed, stressed). Here, an emotion analysis algorithm is used to perform data calculations and evaluate the user's emotions.

[0202] Step 4:

[0203] The server uses the extracted list of terms to search for related information from the information index. The input is the list of terms obtained in step 2, and the output is a list of related information and content. In this step, the existing database is searched and the information is prioritized.

[0204] Step 5:

[0205] The server scrutinizes the search results and generates a summary using a generative AI model. The input for this step is the information list output in step 4 and the sentiment labels output in step 3. The output is summary information adjusted according to the user's sentiment. Here, a summary generation algorithm is used to create a concise summary of the information.

[0206] Step 6:

[0207] The server provides the generated summary to the user's terminal, and the user utilizes the presented information. The input is the summary information generated in step 5, and the output is the final display on the user interface. In this step, information is provided according to the user's situation, improving the user experience.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] [Second Embodiment]

[0212] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0213] 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.

[0214] 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).

[0215] 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.

[0216] 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.

[0217] 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).

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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".

[0224] This invention relates to an information system configured to efficiently manage and retrieve information within a company, enabling users to quickly obtain the information they need. This system includes multiple information processing devices, each playing a role in data collection, analysis, retrieval, summarization, and presentation.

[0225] Users connect to the system via a terminal and input questions in natural language. The server receives these questions, analyzes their intent using natural language processing technology, and extracts relevant keywords. Based on this, the server searches for relevant information from an indexed information database within the company. The search results are refined by an AI model, and information deemed highly relevant is selected. The selected information is summarized and presented to the user via the terminal. At this stage, users can obtain the necessary information and utilize it in their work.

[0226] For example, if a user asks, "Please tell me about past success stories in new product development," the server will extract the keywords "new product," "development," and "success stories." Based on this, it will search for past project reports and documents that detail success stories, summarize the information indicating the factors behind those successes, and present it to the user.

[0227] Furthermore, through user feedback, the server utilizes an automated learning algorithm to improve the accuracy of the information. When users gain new insights, they can add them to the shared information base, resulting in the efficient use of knowledge throughout the organization. In this way, information becomes siloed and operational efficiency is improved.

[0228] The following describes the processing flow.

[0229] Step 1:

[0230] Users connect to the system using a terminal and input questions in natural language. These questions might take the form of, for example, "I would like to know about past success stories in new product development."

[0231] Step 2:

[0232] The server passes the user's question to a natural language processing engine, which analyzes the intent of the question. Important keywords and phrases are extracted, and these are used to prepare the data necessary for information retrieval.

[0233] Step 3:

[0234] The server uses a pre-built information index to search for related documents and data based on extracted keywords. The information index is designed to efficiently search the vast amount of data within the company.

[0235] Step 4:

[0236] The server analyzes the relevant information collected through searches and uses an AI model to evaluate the importance and relevance of the information. Based on the evaluation results, it selects the most relevant information.

[0237] Step 5:

[0238] The server uses a summarization algorithm to summarize the selected information and format it in a way that is easy for the user to understand. This summary includes key points and insights.

[0239] Step 6:

[0240] The server sends the generated summary information to the terminal and presents it to the user. The user reviews this summary and uses the necessary parts for their work.

[0241] Step 7:

[0242] Users can provide feedback based on the information presented. The server receives this feedback and uses an automated learning algorithm to improve the system's accuracy, which is then reflected in future searches and summaries.

[0243] Step 8:

[0244] By registering newly acquired knowledge and information in a shared information base, users can accumulate and share information. Other users can also search this data, promoting efficient knowledge utilization throughout the organization.

[0245] (Example 1)

[0246] 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."

[0247] Modern businesses generate vast amounts of information daily, requiring efficient management and rapid retrieval. Furthermore, extracting the information users need and providing it in a format usable for their work is challenging. Preventing information from becoming tied to specific individuals and sharing knowledge across the entire organization to improve operational efficiency are also key issues.

[0248] 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.

[0249] In this invention, the server includes means for collecting data from an information processing device and creating indicators based thereon, means for analyzing natural language inquiries from users and extracting relevant expressions, and means for scrutinizing and selecting relevant information from search results using a generative AI model. This enables users to efficiently search for information and quickly obtain appropriately summarized information.

[0250] An "information processing device" refers to a computing device that has the function of collecting, managing, and analyzing data.

[0251] An "indicator" refers to a structured dataset designed to effectively search and utilize information.

[0252] "Natural language" refers to the language that humans use on a daily basis, and the text that is input into a computer system in a format that can be analyzed.

[0253] A "generative AI model" refers to an algorithm used to analyze and select data based on its relevance using artificial intelligence.

[0254] A "summary" refers to a document that extracts the main points from a vast amount of information and expresses them concisely.

[0255] "Feedback" refers to evaluations and opinions provided by users after using the system, and the system is improved based on this feedback.

[0256] A "shared knowledge base" refers to a collection of information and know-how that is managed to be easily accessible to members within an organization.

[0257] The information management system of the present invention aims to efficiently collect, analyze, search, and present to users the large amount of information generated within a company. The embodiments for carrying out the invention are described in detail below.

[0258] User operation

[0259] Users input questions into the system using natural language via their own devices. For example, they might ask, "Please tell me about the progress of new product development." At this stage, no specific hardware is required, but general-purpose computers and smart devices are expected.

[0260] Server Processing

[0261] The server receives natural language input sent by the user. For text analysis, the server utilizes natural language processing libraries such as SpaCy and NLTK to analyze the intent of the input sentence. Through this analysis, important terms such as "new product," "development," and "progress" are extracted.

[0262] The server then uses a database management system such as PostgreSQL or MySQL to search the company's information database. Based on the extracted terms, it generates queries and retrieves relevant information.

[0263] The server passes the search results to a generating AI model. This model utilizes advanced AI technologies such as GPT and BERT to select information that is highly relevant to the user's question.

[0264] The selected information is summarized concisely based on a summarization algorithm. The summarized information is formatted to allow users to quickly understand it, by removing excessive details.

[0265] Presentation of results

[0266] Ultimately, the server sends the summarized information to the terminal, providing it to the user. This allows the user to utilize the information they were looking for in their work.

[0267] Using Feedback

[0268] Users can provide feedback on the accuracy and satisfaction level of the information provided. This feedback is received by the server using an automated learning algorithm and used to continuously improve the accuracy of the information provided. In addition, if users gain new insights, they can add them to the shared knowledge base and update it so that it can be effectively used throughout the organization.

[0269] Presentation of specific examples

[0270] Example prompt: "Please provide a report on the results of your marketing campaigns over the past year."

[0271] In this specific example, the server uses keywords such as "marketing campaign," "results," and "past year" to consolidate, summarize, and present relevant information.

[0272] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0273] Step 1:

[0274] The user uses a terminal to input a question in natural language. For example, they might type, "I'd like to know the progress of the new product development." This input is sent to the server as a prompt to the system.

[0275] Step 2:

[0276] The server receives prompt messages sent from the terminal and performs analysis using a natural language processing engine. Specifically, it uses libraries such as SpaCy and NLTK to identify the subject, predicate, and object from the input sentence and extract keywords such as "new product," "development," and "progress." This analysis converts the data into a structured format.

[0277] Step 3:

[0278] The server uses the extracted keywords to issue queries to a database management system (e.g., PostgreSQL or MySQL). The database searches for relevant documents and information and returns the search results to the server. Here, the information collected by the database is structured as key-data pairs.

[0279] Step 4:

[0280] The server uses a generative AI model (e.g., BERT or GPT) to scrutinize the search results obtained from the database. The generative AI model analyzes a large amount of text data and selects the information most relevant to the user's prompt. In doing so, it performs natural language processing with contextual understanding and highlights the selected information.

[0281] Step 5:

[0282] The server processes highly relevant information using a summarization algorithm. The summarization process employs rule-based or neural network-based techniques to eliminate redundancy while preserving key points. As a result, a concise summary is generated.

[0283] Step 6:

[0284] The server transmits the generated summary information to the terminal and presents it to the user. The user can easily understand the provided information and use it as a reference for their work. This final output completes the information provision cycle of the entire system.

[0285] (Application Example 1)

[0286] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0287] In an autonomous vehicle, it is still an issue for the driver to obtain optimal route information in real time. In particular, when the user asks a question in natural language, it is required to accurately analyze the intention and select an optimal route by combining past traffic data and real-time information. To solve this problem, a system that can quickly process voice input and perform high-precision route selection using past and current data is needed.

[0288] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0289] In this invention, the server includes means for converting voice input in natural language into text, means for selecting an optimal route based on past traffic data and real-time traffic information, and means for presenting the selected route information by voice and visually. Thereby, when the user asks a voice question in natural language, it is possible to quickly analyze it and provide an optimal moving route.

[0290] An "information processing device" is a device that performs information processing such as data collection, analysis, search, summarization, and presentation.

[0291] "Natural language" refers to the language that humans use in daily life, especially including forms expressed in text or voice.

[0292] An "index" is a categorized and organized reference list used to efficiently search for information.

[0293] "Traffic data" refers to data that includes travel information such as past and present traffic flow, congestion status, and accident information.

[0294] "Real-time information" refers to the latest information based on events currently in progress, and is acquired in real time.

[0295] A "route" refers to the path taken from the starting point to the destination, and is selected to ensure efficient travel.

[0296] "Voice input" refers to a method in which users provide instructions or information to an information system using their voice.

[0297] "Visual presentation" refers to a means of providing information visually through a screen or display.

[0298] This document describes an embodiment for carrying out the invention. The system that realizes an application example of this invention is designed to be incorporated into the navigation system of an autonomous vehicle. The server converts the user's speech into text using speech recognition software. In this process, speech recognition technologies such as Google Speech API are utilized. The transcribed questions are analyzed using natural language processing algorithms (e.g., spaCy or GPT models) to clarify the intent of the questions.

[0299] Based on the keywords extracted, the server selects the optimal route by combining historical traffic databases and real-time traffic information. SQL databases and the Google Maps API are used for data retrieval and analysis, and an AI model using TensorFlow selects the most relevant routes.

[0300] The selected route is displayed on the terminal's display and also provided as voice guidance. By combining this visual presentation and voice guidance, it supports the movement management of the user.

[0301] As a specific example, consider the case where a user makes a voice input of "Tell me the fastest route home to avoid congestion" during a congested time on the weekend. At this time, the system analyzes the past congestion data and the current traffic situation and selects the optimal route. Also, as an example of the prompt text for the generative AI model, it would be "Design a system that calculates and presents the shortest route based on the current location and destination of the driver input in natural language."

[0302] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0303] Step 1:

[0304] The user makes a voice input. The terminal acquires the voice data and converts it into text using the Google Speech API. The input for this step is voice data, and the output is the corresponding text data.

[0305] Step 2:

[0306] The server analyzes the texturized question using natural language processing algorithms. At this time, it uses spaCy or GPT models to extract important keywords and phrases. The input is text data, and the output is the extracted keyword list.

[0307] Step 3:

[0308] Based on the extracted keywords, the server searches the past traffic database using an SQL query and acquires real-time traffic information using the Google Maps API. The input for this step is the keyword list, and the output is the past and real-time traffic data.

[0309] Step 4:

[0310] The server uses an AI model powered by TensorFlow to select the optimal route based on the acquired traffic data. This process calculates an efficient route by processing a large amount of data, including past trends and current conditions. The input is traffic data, and the output is optimal route information.

[0311] Step 5:

[0312] The server transmits selected route information to the terminal, which then provides this information to the user in the form of visual and audio presentations. The input is optimal route information, and the output is a route presentation in a format that the user can understand.

[0313] 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.

[0314] This invention relates to an information management system that recognizes user emotions and utilizes that information to optimize information provision. This system includes an information processing device and operates by combining multiple emotion engines.

[0315] Users access the system via a terminal and input questions and feedback in natural language. The server receives this information and first analyzes the content of the questions and feedback using a natural language processing engine. Simultaneously, an emotion engine recognizes the user's emotions from the input text and audio data and analyzes their emotional state.

[0316] The server uses keywords extracted through natural language processing to search for relevant documents and data by referring to an information index. Information obtained from the sentiment engine is used to evaluate the search results. In other words, the prioritization of information is adjusted according to the user's emotional state; for example, if the user is anxious, a more concise and direct answer is provided.

[0317] Once the information is identified, the server uses an AI model to examine it and a summary generation algorithm creates an appropriate summary. During this process, sentiment data from the sentiment engine is used to adjust the content and presentation of the summary, ensuring that the information is presented in a way that suits the user's current psychological state.

[0318] As a concrete example, when a user types "I want to know the latest progress on Project X," the server analyzes the question, and the emotion engine determines the user's current emotions (e.g., frustration, anxiety). Based on this information, the server searches for relevant progress reports, generates a summary tailored to the user's emotions (e.g., including encouragement and positive elements), and presents it to the terminal.

[0319] Furthermore, the system analyzes user sentiment data and feedback using an automated learning algorithm to improve the accuracy of information retrieval and summarization processes. This allows the system to continuously improve over time, enabling more personalized information delivery. Additionally, users can register newly acquired information in a shared information base, making it searchable by other users and strengthening knowledge sharing within the company. This approach prevents information from becoming tied to specific individuals and improves operational efficiency.

[0320] The following describes the processing flow.

[0321] Step 1:

[0322] The user connects to the system via a terminal and inputs questions in natural language. At the same time, voice data and text data, including the user's emotions, are also sent to the system.

[0323] Step 2:

[0324] The server receives input data from the user and first uses a natural language processing engine to analyze the content of the question. Here, it extracts important keywords and phrases to understand the context and intent of the question.

[0325] Step 3:

[0326] Simultaneously, the server activates an emotion engine to analyze the user's emotional state from the input voice and text. For example, it can determine emotions such as impatience, anxiety, or joy from voice tone and context.

[0327] Step 4:

[0328] The server uses information obtained from natural language processing and sentiment analysis to refer to an information index and search for documents and information in relevant databases. Here, sentiment information is used to evaluate the importance of the search results.

[0329] Step 5:

[0330] The server evaluates the searched information and uses an AI model to refine its content. It adjusts the importance and display order of information according to the user's emotional state to determine which information is most useful.

[0331] Step 6:

[0332] The server utilizes a summarization algorithm to summarize information in a way that takes the user's emotions into consideration. For example, if the information is deemed urgent, it will choose clearer and more direct language.

[0333] Step 7:

[0334] The server sends the generated summary to the terminal and presents it to the user. The user receives this information and uses it for their work as needed.

[0335] Step 8:

[0336] Users may provide feedback on the information they provide, and the server analyzes this feedback to improve processing accuracy through an automated learning algorithm. This learning process improves the accuracy of the system's information provision and user satisfaction.

[0337] (Example 2)

[0338] 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".

[0339] In modern information systems, users are required to quickly and appropriately retrieve necessary information from a vast amount of data. However, information provision that responds to users' emotional states is insufficient. For example, users may become confused by too much information, or their stress may increase due to irrelevant information. Furthermore, system improvements over time are limited, meaning that the information users receive is not always optimal.

[0340] 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.

[0341] In this invention, the server includes means for receiving natural language data input from a user via a terminal, natural language processing means for analyzing the received natural language data and extracting themes and important words, and emotion recognition means for recognizing emotions from the user's input data and using that emotion information for information evaluation. This makes it possible to provide information according to the user's emotional state, enabling more personalized services. Furthermore, by using an automated learning algorithm, continuous system improvement can be achieved.

[0342] A "natural language processing method" is a processing method that analyzes natural language data received from a user and extracts themes and important words.

[0343] An "emotion recognition method" is a processing method that recognizes the emotional state from the user's input data and evaluates the information provided based on that information.

[0344] A "generative AI model" is an artificial intelligence model used to analyze information and generate summaries while considering the user's emotional state.

[0345] An "automatic learning algorithm" is a data processing method used to analyze user feedback and sentiment data to improve the accuracy of system information scrutiny and selection methods.

[0346] A "shared information base" is a database system that allows users to register newly acquired information and make it available to other users as searchable information.

[0347] This invention relates to an information management system that recognizes and utilizes user emotions in order to optimize information delivery. This system operates by combining a server, terminals, and software such as an emotion recognition engine and a natural language processing engine.

[0348] The server is responsible for receiving natural language data entered by the user through their device. The received data is analyzed by a natural language processing engine, and the main topic and important words are extracted. At the same time, the server uses an emotion recognition engine to recognize the user's emotional state from the input data. Based on this emotional information, the server evaluates the relevant information and generates a summary. In summary generation using a generative AI model, appropriate expressions are provided according to the user's current emotions.

[0349] As a concrete example, consider a scenario where a user inputs "I want to know the latest progress on Project X" through a terminal. In this case, the server analyzes the question and evaluates the emotions the user may be feeling (e.g., frustration or anxiety). Based on this information, the server searches for relevant progress reports and generates a summary that takes the user's emotions into account, providing more positive and easy-to-understand information.

[0350] An example of a prompt might be, "Please tell me about the project's progress. I'm feeling a little anxious right now." Based on this prompt, the system will provide information tailored to the user's emotions.

[0351] In this way, by combining servers, terminals, and advanced AI technology to recognize user emotions and utilize that information, the accuracy of information provision is improved, and a more personalized user experience is realized.

[0352] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0353] Step 1:

[0354] The server receives natural language data entered by the user from the terminal. This input data is in text or audio format and contains the information the user is seeking. The server treats this data as initial input and prepares it for subsequent processing steps.

[0355] Step 2:

[0356] The server sends the received natural language data to the natural language processing engine. The natural language processing engine analyzes the text or audio data to extract the subject and important keywords. Techniques such as morphological analysis and syntactic analysis are used for this data processing, and ultimately the relevant keywords are output.

[0357] Step 3:

[0358] The server sends the extracted keywords to the emotion recognition engine. The emotion recognition engine infers emotions from the user's input and uses the results to analyze the emotional state. This process evaluates word selection and context to obtain emotional data as output. This information is used later for information evaluation and summarization.

[0359] Step 4:

[0360] The server searches for relevant information by referencing the system's information index based on keywords extracted through natural language processing and sentiment information obtained through sentiment recognition. This search process evaluates relevant documents and information within the database and retrieves the information that best suits the user's needs as output.

[0361] Step 5:

[0362] The server sends the acquired relevant information to the generating AI model. The generating AI model examines this information and generates a summary. Here, information is selected and its expression is adjusted according to the user's emotional state. The output of the AI ​​model is a summary processed in a way that is optimal for the user.

[0363] Step 6:

[0364] The server sends the generated summary to the terminal and presents it to the user. This allows the user to receive information in a way that takes their emotions into consideration, enabling them to enjoy a more meaningful information experience.

[0365] (Application Example 2)

[0366] 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."

[0367] In modern society, information overload makes it difficult for users to access necessary information quickly and appropriately, sometimes leading to emotional burdens. Furthermore, because the information provided is not optimized for users' emotions, there is a challenge in that users are unable to effectively utilize that information.

[0368] 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.

[0369] In this invention, the server includes means for collecting data from an information processing device and creating an information index based thereon, means for analyzing the user's emotions and adaptively adjusting the information provided based on that analysis, and means for providing content based on the user's emotions. This enables the user to quickly access information and content that is appropriate to their emotional state.

[0370] An "information processing device" is a device that includes hardware and software for collecting user data and supporting the indexing and retrieval of information.

[0371] "Data" refers to all input information collected from users, including text data and audio data.

[0372] An "information index" is a structure for efficiently searching for information within a database built on data.

[0373] "Users" refer to individuals or end-users who utilize this system, and information is provided to them according to their feelings and requests.

[0374] "Natural language" refers to the forms of language that people use on a daily basis, and is expressed in forms such as text and audio.

[0375] "Related terms" are important words and phrases included in natural language input from users, and are key elements in information retrieval.

[0376] "Methods of searching" refers to the process of systematically finding relevant information using an index.

[0377] "Means of analyzing emotions" refer to technologies used to identify a user's emotional state and reflect it in the information provided.

[0378] "Means of providing content" refers to the process of presenting information and materials in a format that is appropriate to the user's emotions.

[0379] This invention is a system for optimizing information provision based on user emotions using an information processing device. The server collects user data from terminals such as smartphones and smart glasses and creates an information index. Questions and feedback entered by the user in natural language are analyzed using a natural language processing engine and relevant words are extracted. Based on the extracted words, the server searches the index for relevant information and then scrutinizes and selects that information.

[0380] In addition, the server uses an emotion analysis module to analyze user emotions in real time. Specifically, it leverages the Google Cloud Vision API and Amazon Rekognition to identify user emotions from image and audio data. This emotion analysis is used to adjust the priority of information provision and deliver content accordingly.

[0381] The generated information and content are further refined by an AI model and summarized in a way that is suitable for the user. At this stage, the results of sentiment analysis are incorporated into the summarization algorithm.

[0382] For example, if a user searches for information on their smartphone while at work, and emotional analysis reveals that they are experiencing "stress," the server will prioritize displaying information or entertainment content that has a relaxing effect on that user.

[0383] An example of a prompt to input into the generative AI model might be: "Generate recommendations based on the user's current emotions. The user is feeling stressed." This prompt allows the system to provide optimized information.

[0384] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0385] Step 1:

[0386] The device collects text and voice data from the user. Text and voice input are acquired via the microphone and camera and sent to the server. Here, data preprocessing is performed using the Google Cloud Vision API and Amazon Rekognition.

[0387] Step 2:

[0388] The server analyzes the received data using a natural language processing engine, extracting relevant terms from the input questions and feedback. This analysis serves as a key for referencing the information index. The output is a list of the analyzed terms, which is used to proceed to the next search step.

[0389] Step 3:

[0390] The server uses an emotion analysis module to identify the user's emotional state from text and audio data. The input is the raw data obtained in step 1, and the output is an emotion label (e.g., relaxed, stressed). Here, an emotion analysis algorithm is used to perform data calculations and evaluate the user's emotions.

[0391] Step 4:

[0392] The server uses the extracted list of terms to search for related information from the information index. The input is the list of terms obtained in step 2, and the output is a list of related information and content. In this step, the existing database is searched and the information is prioritized.

[0393] Step 5:

[0394] The server scrutinizes the search results and generates a summary using a generative AI model. The input for this step is the information list output in step 4 and the sentiment labels output in step 3. The output is summary information adjusted according to the user's sentiment. Here, a summary generation algorithm is used to create a concise summary of the information.

[0395] Step 6:

[0396] The server provides the generated summary to the user's terminal, and the user utilizes the presented information. The input is the summary information generated in step 5, and the output is the final display on the user interface. In this step, information is provided according to the user's situation, improving the user experience.

[0397] 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.

[0398] 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.

[0399] 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.

[0400] [Third Embodiment]

[0401] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0402] 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.

[0403] 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).

[0404] 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.

[0405] 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.

[0406] 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).

[0407] 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.

[0408] 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.

[0409] 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.

[0410] 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.

[0411] 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.

[0412] 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".

[0413] This invention relates to an information system configured to efficiently manage and retrieve information within a company, enabling users to quickly obtain the information they need. This system includes multiple information processing devices, each playing a role in data collection, analysis, retrieval, summarization, and presentation.

[0414] Users connect to the system via a terminal and input questions in natural language. The server receives these questions, analyzes their intent using natural language processing technology, and extracts relevant keywords. Based on this, the server searches for relevant information from an indexed information database within the company. The search results are refined by an AI model, and information deemed highly relevant is selected. The selected information is summarized and presented to the user via the terminal. At this stage, users can obtain the necessary information and utilize it in their work.

[0415] For example, if a user asks, "Please tell me about past success stories in new product development," the server will extract the keywords "new product," "development," and "success stories." Based on this, it will search for past project reports and documents that detail success stories, summarize the information indicating the factors behind those successes, and present it to the user.

[0416] Furthermore, through user feedback, the server utilizes an automated learning algorithm to improve the accuracy of the information. When users gain new insights, they can add them to the shared information base, resulting in the efficient use of knowledge throughout the organization. In this way, information becomes siloed and operational efficiency is improved.

[0417] The following describes the processing flow.

[0418] Step 1:

[0419] Users connect to the system using a terminal and input questions in natural language. These questions might take the form of, for example, "I would like to know about past success stories in new product development."

[0420] Step 2:

[0421] The server passes the user's question to a natural language processing engine, which analyzes the intent of the question. Important keywords and phrases are extracted, and these are used to prepare the data necessary for information retrieval.

[0422] Step 3:

[0423] The server uses a pre-built information index to search for related documents and data based on extracted keywords. The information index is designed to efficiently search the vast amount of data within the company.

[0424] Step 4:

[0425] The server analyzes the relevant information collected through searches and uses an AI model to evaluate the importance and relevance of the information. Based on the evaluation results, it selects the most relevant information.

[0426] Step 5:

[0427] The server uses a summarization algorithm to summarize the selected information and format it in a way that is easy for the user to understand. This summary includes key points and insights.

[0428] Step 6:

[0429] The server sends the generated summary information to the terminal and presents it to the user. The user reviews this summary and uses the necessary parts for their work.

[0430] Step 7:

[0431] Users can provide feedback based on the information presented. The server receives this feedback and uses an automated learning algorithm to improve the system's accuracy, which is then reflected in future searches and summaries.

[0432] Step 8:

[0433] By registering newly acquired knowledge and information in a shared information base, users can accumulate and share information. Other users can also search this data, promoting efficient knowledge utilization throughout the organization.

[0434] (Example 1)

[0435] 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."

[0436] Modern businesses generate vast amounts of information daily, requiring efficient management and rapid retrieval. Furthermore, extracting the information users need and providing it in a format usable for their work is challenging. Preventing information from becoming tied to specific individuals and sharing knowledge across the entire organization to improve operational efficiency are also key issues.

[0437] 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.

[0438] In this invention, the server includes means for collecting data from an information processing device and creating indicators based thereon, means for analyzing natural language inquiries from users and extracting relevant expressions, and means for scrutinizing and selecting relevant information from search results using a generative AI model. This enables users to efficiently search for information and quickly obtain appropriately summarized information.

[0439] An "information processing device" refers to a computing device that has the function of collecting, managing, and analyzing data.

[0440] An "indicator" refers to a structured dataset designed to effectively search and utilize information.

[0441] "Natural language" refers to the language that humans use on a daily basis, and the text that is input into a computer system in a format that can be analyzed.

[0442] A "generative AI model" refers to an algorithm used to analyze and select data based on its relevance using artificial intelligence.

[0443] A "summary" refers to a document that extracts the main points from a vast amount of information and expresses them concisely.

[0444] "Feedback" refers to evaluations and opinions provided by users after using the system, and the system is improved based on this feedback.

[0445] A "shared knowledge base" refers to a collection of information and know-how that is managed to be easily accessible to members within an organization.

[0446] The information management system of the present invention aims to efficiently collect, analyze, search, and present to users the large amount of information generated within a company. The embodiments for carrying out the invention are described in detail below.

[0447] User operation

[0448] Users input questions into the system using natural language via their own devices. For example, they might ask, "Please tell me about the progress of new product development." At this stage, no specific hardware is required, but general-purpose computers and smart devices are expected.

[0449] Server Processing

[0450] The server receives natural language input sent by the user. For text analysis, the server utilizes natural language processing libraries such as SpaCy and NLTK to analyze the intent of the input sentence. Through this analysis, important terms such as "new product," "development," and "progress" are extracted.

[0451] The server then uses a database management system such as PostgreSQL or MySQL to search the company's information database. Based on the extracted terms, it generates queries and retrieves relevant information.

[0452] The server passes the search results to a generating AI model. This model utilizes advanced AI technologies such as GPT and BERT to select information that is highly relevant to the user's question.

[0453] The selected information is summarized concisely based on a summarization algorithm. The summarized information is formatted to allow users to quickly understand it, by removing excessive details.

[0454] Presentation of results

[0455] Ultimately, the server sends the summarized information to the terminal, providing it to the user. This allows the user to utilize the information they were looking for in their work.

[0456] Using Feedback

[0457] Users can provide feedback on the accuracy and satisfaction level of the information provided. This feedback is received by the server using an automated learning algorithm and used to continuously improve the accuracy of the information provided. In addition, if users gain new insights, they can add them to the shared knowledge base and update it so that it can be effectively used throughout the organization.

[0458] Presentation of specific examples

[0459] Example prompt: "Please provide a report on the results of your marketing campaigns over the past year."

[0460] In this specific example, the server uses keywords such as "marketing campaign," "results," and "past year" to consolidate, summarize, and present relevant information.

[0461] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0462] Step 1:

[0463] The user uses a terminal to input a question in natural language. For example, they might type, "I'd like to know the progress of the new product development." This input is sent to the server as a prompt to the system.

[0464] Step 2:

[0465] The server receives prompt messages sent from the terminal and performs analysis using a natural language processing engine. Specifically, it uses libraries such as SpaCy and NLTK to identify the subject, predicate, and object from the input sentence and extract keywords such as "new product," "development," and "progress." This analysis converts the data into a structured format.

[0466] Step 3:

[0467] The server uses the extracted keywords to issue queries to a database management system (e.g., PostgreSQL or MySQL). The database searches for relevant documents and information and returns the search results to the server. Here, the information collected by the database is structured as key-data pairs.

[0468] Step 4:

[0469] The server uses a generative AI model (e.g., BERT or GPT) to scrutinize the search results obtained from the database. The generative AI model analyzes a large amount of text data and selects the information most relevant to the user's prompt. In doing so, it performs natural language processing with contextual understanding and highlights the selected information.

[0470] Step 5:

[0471] The server processes highly relevant information using a summarization algorithm. The summarization process employs rule-based or neural network-based techniques to eliminate redundancy while preserving key points. As a result, a concise summary is generated.

[0472] Step 6:

[0473] The server sends the generated summary information to the terminal and presents it to the user. The user can easily understand the provided information and use it as a reference for their work. This final output completes the information provision cycle for the entire system.

[0474] (Application Example 1)

[0475] 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."

[0476] In autonomous vehicles, obtaining real-time optimal route information remains a challenge. In particular, it is essential to accurately analyze the intent behind user inquiries in natural language and combine historical traffic data with real-time information to select the optimal route. To address this challenge, a system is needed that can rapidly process voice input and perform highly accurate route selection using both past and present data.

[0477] 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.

[0478] In this invention, the server includes means for converting natural language voice input into text, means for selecting the optimal route based on past traffic data and real-time traffic information, and means for presenting the selected route information via voice and visual means. This makes it possible to quickly analyze a user's voice inquiry in natural language and provide the optimal travel route.

[0479] An "information processing device" is a device that performs information processing such as data collection, analysis, retrieval, summarization, and presentation.

[0480] "Natural language" refers to the language that humans use on a daily basis, and includes forms of expression such as text and speech.

[0481] An "index" is a categorized and organized reference list used to efficiently search for information.

[0482] "Traffic data" refers to data that includes travel information such as past and present traffic flow, congestion status, and accident information.

[0483] "Real-time information" refers to the latest information based on events currently in progress, and is acquired in real time.

[0484] A "route" refers to the path taken from the starting point to the destination, and is selected to ensure efficient travel.

[0485] "Voice input" refers to a method in which users provide instructions or information to an information system using their voice.

[0486] "Visual presentation" refers to a means of providing information visually through a screen or display.

[0487] This document describes an embodiment for carrying out the invention. The system that realizes an application example of this invention is designed to be incorporated into the navigation system of an autonomous vehicle. The server converts the user's speech into text using speech recognition software. In this process, speech recognition technologies such as Google Speech API are utilized. The transcribed questions are analyzed using natural language processing algorithms (e.g., spaCy or GPT models) to clarify the intent of the questions.

[0488] Based on the keywords extracted, the server selects the optimal route by combining historical traffic databases and real-time traffic information. SQL databases and the Google Maps API are used for data retrieval and analysis, and an AI model using TensorFlow selects the most relevant routes.

[0489] The selected route is displayed on the terminal's screen and also provided as audio guidance. This combination of visual presentation and audio guidance supports users in managing their travel.

[0490] As a concrete example, consider a scenario where a user voice-inputs, "Tell me the fastest route home that avoids congestion," during a busy weekend. In this case, the system analyzes past congestion data and current traffic conditions to select the optimal route. An example of a prompt to the generated AI model would be, "Design a system that calculates and presents the shortest route based on the driver's current location and destination, which are entered in natural language."

[0491] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0492] Step 1:

[0493] The user provides voice input. The device acquires this voice data and converts it to text using the Google Speech API. In this step, the input is voice data, and the output is the corresponding text data.

[0494] Step 2:

[0495] The server analyzes the transcribed questions using natural language processing algorithms. It employs tools like spaCy and the GPT model to extract important keywords and phrases. The input is text data, and the output is a list of extracted keywords.

[0496] Step 3:

[0497] The server searches a historical traffic database using SQL queries based on the extracted keywords, and also retrieves real-time traffic information using the Google Maps API. The input for this step is a list of keywords, and the output is historical and real-time traffic data.

[0498] Step 4:

[0499] The server uses an AI model powered by TensorFlow to select the optimal route based on the acquired traffic data. This process calculates an efficient route by processing a large amount of data, including past trends and current conditions. The input is traffic data, and the output is optimal route information.

[0500] Step 5:

[0501] The server transmits selected route information to the terminal, which then provides this information to the user in the form of visual and audio presentations. The input is optimal route information, and the output is a route presentation in a format that the user can understand.

[0502] 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.

[0503] This invention relates to an information management system that recognizes user emotions and utilizes that information to optimize information provision. This system includes an information processing device and operates by combining multiple emotion engines.

[0504] Users access the system via a terminal and input questions and feedback in natural language. The server receives this information and first analyzes the content of the questions and feedback using a natural language processing engine. Simultaneously, an emotion engine recognizes the user's emotions from the input text and audio data and analyzes their emotional state.

[0505] The server uses keywords extracted through natural language processing to search for relevant documents and data by referring to an information index. Information obtained from the sentiment engine is used to evaluate the search results. In other words, the prioritization of information is adjusted according to the user's emotional state; for example, if the user is anxious, a more concise and direct answer is provided.

[0506] Once the information is identified, the server uses an AI model to examine it and a summary generation algorithm creates an appropriate summary. During this process, sentiment data from the sentiment engine is used to adjust the content and presentation of the summary, ensuring that the information is presented in a way that suits the user's current psychological state.

[0507] As a concrete example, when a user types "I want to know the latest progress on Project X," the server analyzes the question, and the emotion engine determines the user's current emotions (e.g., frustration, anxiety). Based on this information, the server searches for relevant progress reports, generates a summary tailored to the user's emotions (e.g., including encouragement and positive elements), and presents it to the terminal.

[0508] Furthermore, the system analyzes user sentiment data and feedback using an automated learning algorithm to improve the accuracy of information retrieval and summarization processes. This allows the system to continuously improve over time, enabling more personalized information delivery. Additionally, users can register newly acquired information in a shared information base, making it searchable by other users and strengthening knowledge sharing within the company. This approach prevents information from becoming tied to specific individuals and improves operational efficiency.

[0509] The following describes the processing flow.

[0510] Step 1:

[0511] The user connects to the system via a terminal and inputs questions in natural language. At the same time, voice data and text data, including the user's emotions, are also sent to the system.

[0512] Step 2:

[0513] The server receives input data from the user and first uses a natural language processing engine to analyze the content of the question. Here, it extracts important keywords and phrases to understand the context and intent of the question.

[0514] Step 3:

[0515] Simultaneously, the server activates an emotion engine to analyze the user's emotional state from the input voice and text. For example, it can determine emotions such as impatience, anxiety, or joy from voice tone and context.

[0516] Step 4:

[0517] The server uses information obtained from natural language processing and sentiment analysis to refer to an information index and search for documents and information in relevant databases. Here, sentiment information is used to evaluate the importance of the search results.

[0518] Step 5:

[0519] The server evaluates the searched information and uses an AI model to refine its content. It adjusts the importance and display order of information according to the user's emotional state to determine which information is most useful.

[0520] Step 6:

[0521] The server utilizes a summarization algorithm to summarize information in a way that takes the user's emotions into consideration. For example, if the information is deemed urgent, it will choose clearer and more direct language.

[0522] Step 7:

[0523] The server sends the generated summary to the terminal and presents it to the user. The user receives this information and uses it for their work as needed.

[0524] Step 8:

[0525] Users may provide feedback on the information they provide, and the server analyzes this feedback to improve processing accuracy through an automated learning algorithm. This learning process improves the accuracy of the system's information provision and user satisfaction.

[0526] (Example 2)

[0527] 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."

[0528] In modern information systems, users are required to quickly and appropriately retrieve necessary information from a vast amount of data. However, information provision that responds to users' emotional states is insufficient. For example, users may become confused by too much information, or their stress may increase due to irrelevant information. Furthermore, system improvements over time are limited, meaning that the information users receive is not always optimal.

[0529] 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.

[0530] In this invention, the server includes means for receiving natural language data input from a user via a terminal, natural language processing means for analyzing the received natural language data and extracting themes and important words, and emotion recognition means for recognizing emotions from the user's input data and using that emotion information for information evaluation. This makes it possible to provide information according to the user's emotional state, enabling more personalized services. Furthermore, by using an automated learning algorithm, continuous system improvement can be achieved.

[0531] A "natural language processing method" is a processing method that analyzes natural language data received from a user and extracts themes and important words.

[0532] An "emotion recognition method" is a processing method that recognizes the emotional state from the user's input data and evaluates the information provided based on that information.

[0533] A "generative AI model" is an artificial intelligence model used to analyze information and generate summaries while considering the user's emotional state.

[0534] An "automatic learning algorithm" is a data processing method used to analyze user feedback and sentiment data to improve the accuracy of system information scrutiny and selection methods.

[0535] A "shared information base" is a database system that allows users to register newly acquired information and make it available to other users as searchable information.

[0536] This invention relates to an information management system that recognizes and utilizes user emotions in order to optimize information delivery. This system operates by combining a server, terminals, and software such as an emotion recognition engine and a natural language processing engine.

[0537] The server is responsible for receiving natural language data entered by the user through their device. The received data is analyzed by a natural language processing engine, and the main topic and important words are extracted. At the same time, the server uses an emotion recognition engine to recognize the user's emotional state from the input data. Based on this emotional information, the server evaluates the relevant information and generates a summary. In summary generation using a generative AI model, appropriate expressions are provided according to the user's current emotions.

[0538] As a concrete example, consider a scenario where a user inputs "I want to know the latest progress on Project X" through a terminal. In this case, the server analyzes the question and evaluates the emotions the user may be feeling (e.g., frustration or anxiety). Based on this information, the server searches for relevant progress reports and generates a summary that takes the user's emotions into account, providing more positive and easy-to-understand information.

[0539] An example of a prompt might be, "Please tell me about the project's progress. I'm feeling a little anxious right now." Based on this prompt, the system will provide information tailored to the user's emotions.

[0540] In this way, by combining servers, terminals, and advanced AI technology to recognize user emotions and utilize that information, the accuracy of information provision is improved, and a more personalized user experience is realized.

[0541] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0542] Step 1:

[0543] The server receives natural language data entered by the user from the terminal. This input data is in text or audio format and contains the information the user is seeking. The server treats this data as initial input and prepares it for subsequent processing steps.

[0544] Step 2:

[0545] The server sends the received natural language data to the natural language processing engine. The natural language processing engine analyzes the text or audio data to extract the subject and important keywords. Techniques such as morphological analysis and syntactic analysis are used for this data processing, and ultimately the relevant keywords are output.

[0546] Step 3:

[0547] The server sends the extracted keywords to the emotion recognition engine. The emotion recognition engine infers emotions from the user's input and uses the results to analyze the emotional state. This process evaluates word selection and context to obtain emotional data as output. This information is used later for information evaluation and summarization.

[0548] Step 4:

[0549] The server searches for relevant information by referencing the system's information index based on keywords extracted through natural language processing and sentiment information obtained through sentiment recognition. This search process evaluates relevant documents and information within the database and retrieves the information that best suits the user's needs as output.

[0550] Step 5:

[0551] The server sends the acquired relevant information to the generating AI model. The generating AI model examines this information and generates a summary. Here, information is selected and its expression is adjusted according to the user's emotional state. The output of the AI ​​model is a summary processed in a way that is optimal for the user.

[0552] Step 6:

[0553] The server sends the generated summary to the terminal and presents it to the user. This allows the user to receive information in a way that takes their emotions into consideration, enabling them to enjoy a more meaningful information experience.

[0554] (Application Example 2)

[0555] 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."

[0556] In modern society, information overload makes it difficult for users to access necessary information quickly and appropriately, sometimes leading to emotional burdens. Furthermore, because the information provided is not optimized for users' emotions, there is a challenge in that users are unable to effectively utilize that information.

[0557] 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.

[0558] In this invention, the server includes means for collecting data from an information processing device and creating an information index based thereon, means for analyzing the user's emotions and adaptively adjusting the information provided based on that analysis, and means for providing content based on the user's emotions. This enables the user to quickly access information and content that is appropriate to their emotional state.

[0559] An "information processing device" is a device that includes hardware and software for collecting user data and supporting the indexing and retrieval of information.

[0560] "Data" refers to all input information collected from users, including text data and audio data.

[0561] An "information index" is a structure for efficiently searching for information within a database built on data.

[0562] "Users" refer to individuals or end-users who utilize this system, and information is provided to them according to their feelings and requests.

[0563] "Natural language" refers to the forms of language that people use on a daily basis, and is expressed in forms such as text and audio.

[0564] "Related terms" are important words and phrases included in natural language input from users, and are key elements in information retrieval.

[0565] "Methods of searching" refers to the process of systematically finding relevant information using an index.

[0566] "Means of analyzing emotions" refer to technologies used to identify a user's emotional state and reflect it in the information provided.

[0567] "Means of providing content" refers to the process of presenting information and materials in a format that is appropriate to the user's emotions.

[0568] This invention is a system for optimizing information provision based on user emotions using an information processing device. The server collects user data from terminals such as smartphones and smart glasses and creates an information index. Questions and feedback entered by the user in natural language are analyzed using a natural language processing engine and relevant words are extracted. Based on the extracted words, the server searches the index for relevant information and then scrutinizes and selects that information.

[0569] In addition, the server uses an emotion analysis module to analyze user emotions in real time. Specifically, it leverages the Google Cloud Vision API and Amazon Rekognition to identify user emotions from image and audio data. This emotion analysis is used to adjust the priority of information provision and deliver content accordingly.

[0570] The generated information and content are further refined by an AI model and summarized in a way that is suitable for the user. At this stage, the results of sentiment analysis are incorporated into the summarization algorithm.

[0571] For example, if a user searches for information on their smartphone while at work, and emotional analysis reveals that they are experiencing "stress," the server will prioritize displaying information or entertainment content that has a relaxing effect on that user.

[0572] An example of a prompt to input into the generative AI model might be: "Generate recommendations based on the user's current emotions. The user is feeling stressed." This prompt allows the system to provide optimized information.

[0573] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0574] Step 1:

[0575] The device collects text and voice data from the user. Text and voice input are acquired via the microphone and camera and sent to the server. Here, data preprocessing is performed using the Google Cloud Vision API and Amazon Rekognition.

[0576] Step 2:

[0577] The server analyzes the received data using a natural language processing engine, extracting relevant terms from the input questions and feedback. This analysis serves as a key for referencing the information index. The output is a list of the analyzed terms, which is used to proceed to the next search step.

[0578] Step 3:

[0579] The server uses an emotion analysis module to identify the user's emotional state from text and audio data. The input is the raw data obtained in step 1, and the output is an emotion label (e.g., relaxed, stressed). Here, an emotion analysis algorithm is used to perform data calculations and evaluate the user's emotions.

[0580] Step 4:

[0581] The server uses the extracted list of terms to search for related information from the information index. The input is the list of terms obtained in step 2, and the output is a list of related information and content. In this step, the existing database is searched and the information is prioritized.

[0582] Step 5:

[0583] The server scrutinizes the search results and generates a summary using a generative AI model. The input for this step is the information list output in step 4 and the sentiment labels output in step 3. The output is summary information adjusted according to the user's sentiment. Here, a summary generation algorithm is used to create a concise summary of the information.

[0584] Step 6:

[0585] The server provides the generated summary to the user's terminal, and the user utilizes the presented information. The input is the summary information generated in step 5, and the output is the final display on the user interface. In this step, information is provided according to the user's situation, improving the user experience.

[0586] 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.

[0587] 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.

[0588] 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.

[0589] [Fourth Embodiment]

[0590] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0591] 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.

[0592] 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).

[0593] 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.

[0594] 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.

[0595] 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).

[0596] 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.

[0597] 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.

[0598] 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.

[0599] 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.

[0600] 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.

[0601] 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.

[0602] 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".

[0603] This invention relates to an information system configured to efficiently manage and retrieve information within a company, enabling users to quickly obtain the information they need. This system includes multiple information processing devices, each playing a role in data collection, analysis, retrieval, summarization, and presentation.

[0604] Users connect to the system via a terminal and input questions in natural language. The server receives these questions, analyzes their intent using natural language processing technology, and extracts relevant keywords. Based on this, the server searches for relevant information from an indexed information database within the company. The search results are refined by an AI model, and information deemed highly relevant is selected. The selected information is summarized and presented to the user via the terminal. At this stage, users can obtain the necessary information and utilize it in their work.

[0605] For example, if a user asks, "Please tell me about past success stories in new product development," the server will extract the keywords "new product," "development," and "success stories." Based on this, it will search for past project reports and documents that detail success stories, summarize the information indicating the factors behind those successes, and present it to the user.

[0606] Furthermore, through user feedback, the server utilizes an automated learning algorithm to improve the accuracy of the information. When users gain new insights, they can add them to the shared information base, resulting in the efficient use of knowledge throughout the organization. In this way, information becomes siloed and operational efficiency is improved.

[0607] The following describes the processing flow.

[0608] Step 1:

[0609] Users connect to the system using a terminal and input questions in natural language. These questions might take the form of, for example, "I would like to know about past success stories in new product development."

[0610] Step 2:

[0611] The server passes the user's question to a natural language processing engine, which analyzes the intent of the question. Important keywords and phrases are extracted, and these are used to prepare the data necessary for information retrieval.

[0612] Step 3:

[0613] The server uses a pre-built information index to search for related documents and data based on extracted keywords. The information index is designed to efficiently search the vast amount of data within the company.

[0614] Step 4:

[0615] The server analyzes the relevant information collected through searches and uses an AI model to evaluate the importance and relevance of the information. Based on the evaluation results, it selects the most relevant information.

[0616] Step 5:

[0617] The server uses a summarization algorithm to summarize the selected information and format it in a way that is easy for the user to understand. This summary includes key points and insights.

[0618] Step 6:

[0619] The server sends the generated summary information to the terminal and presents it to the user. The user reviews this summary and uses the necessary parts for their work.

[0620] Step 7:

[0621] Users can provide feedback based on the information presented. The server receives this feedback and uses an automated learning algorithm to improve the system's accuracy, which is then reflected in future searches and summaries.

[0622] Step 8:

[0623] By registering newly acquired knowledge and information in a shared information base, users can accumulate and share information. Other users can also search this data, promoting efficient knowledge utilization throughout the organization.

[0624] (Example 1)

[0625] 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".

[0626] Modern businesses generate vast amounts of information daily, requiring efficient management and rapid retrieval. Furthermore, extracting the information users need and providing it in a format usable for their work is challenging. Preventing information from becoming tied to specific individuals and sharing knowledge across the entire organization to improve operational efficiency are also key issues.

[0627] 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.

[0628] In this invention, the server includes means for collecting data from an information processing device and creating indicators based thereon, means for analyzing natural language inquiries from users and extracting relevant expressions, and means for scrutinizing and selecting relevant information from search results using a generative AI model. This enables users to efficiently search for information and quickly obtain appropriately summarized information.

[0629] An "information processing device" refers to a computing device that has the function of collecting, managing, and analyzing data.

[0630] An "indicator" refers to a structured dataset designed to effectively search and utilize information.

[0631] "Natural language" refers to the language that humans use on a daily basis, and the text that is input into a computer system in a format that can be analyzed.

[0632] A "generative AI model" refers to an algorithm used to analyze and select data based on its relevance using artificial intelligence.

[0633] A "summary" refers to a document that extracts the main points from a vast amount of information and expresses them concisely.

[0634] "Feedback" refers to evaluations and opinions provided by users after using the system, and the system is improved based on this feedback.

[0635] A "shared knowledge base" refers to a collection of information and know-how that is managed to be easily accessible to members within an organization.

[0636] The information management system of the present invention aims to efficiently collect, analyze, search, and present to users the large amount of information generated within a company. The embodiments for carrying out the invention are described in detail below.

[0637] User operation

[0638] Users input questions into the system using natural language via their own devices. For example, they might ask, "Please tell me about the progress of new product development." At this stage, no specific hardware is required, but general-purpose computers and smart devices are expected.

[0639] Server Processing

[0640] The server receives natural language input sent by the user. For text analysis, the server utilizes natural language processing libraries such as SpaCy and NLTK to analyze the intent of the input sentence. Through this analysis, important terms such as "new product," "development," and "progress" are extracted.

[0641] The server then uses a database management system such as PostgreSQL or MySQL to search the company's information database. Based on the extracted terms, it generates queries and retrieves relevant information.

[0642] The server passes the search results to a generating AI model. This model utilizes advanced AI technologies such as GPT and BERT to select information that is highly relevant to the user's question.

[0643] The selected information is summarized concisely based on a summarization algorithm. The summarized information is formatted to allow users to quickly understand it, by removing excessive details.

[0644] Presentation of results

[0645] Ultimately, the server sends the summarized information to the terminal, providing it to the user. This allows the user to utilize the information they were looking for in their work.

[0646] Using Feedback

[0647] Users can provide feedback on the accuracy and satisfaction level of the information provided. This feedback is received by the server using an automated learning algorithm and used to continuously improve the accuracy of the information provided. In addition, if users gain new insights, they can add them to the shared knowledge base and update it so that it can be effectively used throughout the organization.

[0648] Presentation of specific examples

[0649] Example prompt: "Please provide a report on the results of your marketing campaigns over the past year."

[0650] In this specific example, the server uses keywords such as "marketing campaign," "results," and "past year" to consolidate, summarize, and present relevant information.

[0651] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0652] Step 1:

[0653] The user uses a terminal to input a question in natural language. For example, they might type, "I'd like to know the progress of the new product development." This input is sent to the server as a prompt to the system.

[0654] Step 2:

[0655] The server receives prompt messages sent from the terminal and performs analysis using a natural language processing engine. Specifically, it uses libraries such as SpaCy and NLTK to identify the subject, predicate, and object from the input sentence and extract keywords such as "new product," "development," and "progress." This analysis converts the data into a structured format.

[0656] Step 3:

[0657] The server uses the extracted keywords to issue queries to a database management system (e.g., PostgreSQL or MySQL). The database searches for relevant documents and information and returns the search results to the server. Here, the information collected by the database is structured as key-data pairs.

[0658] Step 4:

[0659] The server uses a generative AI model (e.g., BERT or GPT) to scrutinize the search results obtained from the database. The generative AI model analyzes a large amount of text data and selects the information most relevant to the user's prompt. In doing so, it performs natural language processing with contextual understanding and highlights the selected information.

[0660] Step 5:

[0661] The server processes highly relevant information using a summarization algorithm. The summarization process employs rule-based or neural network-based techniques to eliminate redundancy while preserving key points. As a result, a concise summary is generated.

[0662] Step 6:

[0663] The server sends the generated summary information to the terminal and presents it to the user. The user can easily understand the provided information and use it as a reference for their work. This final output completes the information provision cycle for the entire system.

[0664] (Application Example 1)

[0665] 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".

[0666] In autonomous vehicles, obtaining real-time optimal route information remains a challenge. In particular, it is essential to accurately analyze the intent behind user inquiries in natural language and combine historical traffic data with real-time information to select the optimal route. To address this challenge, a system is needed that can rapidly process voice input and perform highly accurate route selection using both past and present data.

[0667] 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.

[0668] In this invention, the server includes means for converting natural language voice input into text, means for selecting the optimal route based on past traffic data and real-time traffic information, and means for presenting the selected route information via voice and visual means. This makes it possible to quickly analyze a user's voice inquiry in natural language and provide the optimal travel route.

[0669] An "information processing device" is a device that performs information processing such as data collection, analysis, retrieval, summarization, and presentation.

[0670] "Natural language" refers to the language that humans use on a daily basis, and includes forms of expression such as text and speech.

[0671] An "index" is a categorized and organized reference list used to efficiently search for information.

[0672] "Traffic data" refers to data that includes travel information such as past and present traffic flow, congestion status, and accident information.

[0673] "Real-time information" refers to the latest information based on events currently in progress, and is acquired in real time.

[0674] A "route" refers to the path taken from the starting point to the destination, and is selected to ensure efficient travel.

[0675] "Voice input" refers to a method in which users provide instructions or information to an information system using their voice.

[0676] "Visual presentation" refers to a means of providing information visually through a screen or display.

[0677] This document describes an embodiment for carrying out the invention. The system that realizes an application example of this invention is designed to be incorporated into the navigation system of an autonomous vehicle. The server converts the user's speech into text using speech recognition software. In this process, speech recognition technologies such as Google Speech API are utilized. The transcribed questions are analyzed using natural language processing algorithms (e.g., spaCy or GPT models) to clarify the intent of the questions.

[0678] Based on the keywords extracted, the server selects the optimal route by combining historical traffic databases and real-time traffic information. SQL databases and the Google Maps API are used for data retrieval and analysis, and an AI model using TensorFlow selects the most relevant routes.

[0679] The selected route is displayed on the terminal's screen and also provided as audio guidance. This combination of visual presentation and audio guidance supports users in managing their travel.

[0680] As a concrete example, consider a scenario where a user voice-inputs, "Tell me the fastest route home that avoids congestion," during a busy weekend. In this case, the system analyzes past congestion data and current traffic conditions to select the optimal route. An example of a prompt to the generated AI model would be, "Design a system that calculates and presents the shortest route based on the driver's current location and destination, which are entered in natural language."

[0681] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0682] Step 1:

[0683] The user provides voice input. The device acquires this voice data and converts it to text using the Google Speech API. In this step, the input is voice data, and the output is the corresponding text data.

[0684] Step 2:

[0685] The server analyzes the transcribed questions using natural language processing algorithms. It employs tools like spaCy and the GPT model to extract important keywords and phrases. The input is text data, and the output is a list of extracted keywords.

[0686] Step 3:

[0687] The server searches a historical traffic database using SQL queries based on the extracted keywords, and also retrieves real-time traffic information using the Google Maps API. The input for this step is a list of keywords, and the output is historical and real-time traffic data.

[0688] Step 4:

[0689] The server uses an AI model powered by TensorFlow to select the optimal route based on the acquired traffic data. This process calculates an efficient route by processing a large amount of data, including past trends and current conditions. The input is traffic data, and the output is optimal route information.

[0690] Step 5:

[0691] The server transmits selected route information to the terminal, which then provides this information to the user in the form of visual and audio presentations. The input is optimal route information, and the output is a route presentation in a format that the user can understand.

[0692] 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.

[0693] This invention relates to an information management system that recognizes user emotions and utilizes that information to optimize information provision. This system includes an information processing device and operates by combining multiple emotion engines.

[0694] Users access the system via a terminal and input questions and feedback in natural language. The server receives this information and first analyzes the content of the questions and feedback using a natural language processing engine. Simultaneously, an emotion engine recognizes the user's emotions from the input text and audio data and analyzes their emotional state.

[0695] The server uses keywords extracted through natural language processing to search for relevant documents and data by referring to an information index. Information obtained from the sentiment engine is used to evaluate the search results. In other words, the prioritization of information is adjusted according to the user's emotional state; for example, if the user is anxious, a more concise and direct answer is provided.

[0696] Once the information is identified, the server uses an AI model to examine it and a summary generation algorithm creates an appropriate summary. During this process, sentiment data from the sentiment engine is used to adjust the content and presentation of the summary, ensuring that the information is presented in a way that suits the user's current psychological state.

[0697] As a concrete example, when a user types "I want to know the latest progress on Project X," the server analyzes the question, and the emotion engine determines the user's current emotions (e.g., frustration, anxiety). Based on this information, the server searches for relevant progress reports, generates a summary tailored to the user's emotions (e.g., including encouragement and positive elements), and presents it to the terminal.

[0698] Furthermore, the system analyzes user sentiment data and feedback using an automated learning algorithm to improve the accuracy of information retrieval and summarization processes. This allows the system to continuously improve over time, enabling more personalized information delivery. Additionally, users can register newly acquired information in a shared information base, making it searchable by other users and strengthening knowledge sharing within the company. This approach prevents information from becoming tied to specific individuals and improves operational efficiency.

[0699] The following describes the processing flow.

[0700] Step 1:

[0701] The user connects to the system via a terminal and inputs questions in natural language. At the same time, voice data and text data, including the user's emotions, are also sent to the system.

[0702] Step 2:

[0703] The server receives input data from the user and first uses a natural language processing engine to analyze the content of the question. Here, it extracts important keywords and phrases to understand the context and intent of the question.

[0704] Step 3:

[0705] Simultaneously, the server activates an emotion engine to analyze the user's emotional state from the input voice and text. For example, it can determine emotions such as impatience, anxiety, or joy from voice tone and context.

[0706] Step 4:

[0707] The server uses information obtained from natural language processing and sentiment analysis to refer to an information index and search for documents and information in relevant databases. Here, sentiment information is used to evaluate the importance of the search results.

[0708] Step 5:

[0709] The server evaluates the searched information and uses an AI model to refine its content. It adjusts the importance and display order of information according to the user's emotional state to determine which information is most useful.

[0710] Step 6:

[0711] The server utilizes a summarization algorithm to summarize information in a way that takes the user's emotions into consideration. For example, if the information is deemed urgent, it will choose clearer and more direct language.

[0712] Step 7:

[0713] The server sends the generated summary to the terminal and presents it to the user. The user receives this information and uses it for their work as needed.

[0714] Step 8:

[0715] Users may provide feedback on the information they provide, and the server analyzes this feedback to improve processing accuracy through an automated learning algorithm. This learning process improves the accuracy of the system's information provision and user satisfaction.

[0716] (Example 2)

[0717] 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".

[0718] In modern information systems, users are required to quickly and appropriately retrieve necessary information from a vast amount of data. However, information provision that responds to users' emotional states is insufficient. For example, users may become confused by too much information, or their stress may increase due to irrelevant information. Furthermore, system improvements over time are limited, meaning that the information users receive is not always optimal.

[0719] 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.

[0720] In this invention, the server includes means for receiving natural language data input from a user via a terminal, natural language processing means for analyzing the received natural language data and extracting themes and important words, and emotion recognition means for recognizing emotions from the user's input data and using that emotion information for information evaluation. This makes it possible to provide information according to the user's emotional state, enabling more personalized services. Furthermore, by using an automated learning algorithm, continuous system improvement can be achieved.

[0721] A "natural language processing method" is a processing method that analyzes natural language data received from a user and extracts themes and important words.

[0722] An "emotion recognition method" is a processing method that recognizes the emotional state from the user's input data and evaluates the information provided based on that information.

[0723] A "generative AI model" is an artificial intelligence model used to analyze information and generate summaries while considering the user's emotional state.

[0724] An "automatic learning algorithm" is a data processing method used to analyze user feedback and sentiment data to improve the accuracy of system information scrutiny and selection methods.

[0725] A "shared information base" is a database system that allows users to register newly acquired information and make it available to other users as searchable information.

[0726] This invention relates to an information management system that recognizes and utilizes user emotions in order to optimize information delivery. This system operates by combining a server, terminals, and software such as an emotion recognition engine and a natural language processing engine.

[0727] The server is responsible for receiving natural language data entered by the user through their device. The received data is analyzed by a natural language processing engine, and the main topic and important words are extracted. At the same time, the server uses an emotion recognition engine to recognize the user's emotional state from the input data. Based on this emotional information, the server evaluates the relevant information and generates a summary. In summary generation using a generative AI model, appropriate expressions are provided according to the user's current emotions.

[0728] As a concrete example, consider a scenario where a user inputs "I want to know the latest progress on Project X" through a terminal. In this case, the server analyzes the question and evaluates the emotions the user may be feeling (e.g., frustration or anxiety). Based on this information, the server searches for relevant progress reports and generates a summary that takes the user's emotions into account, providing more positive and easy-to-understand information.

[0729] An example of a prompt might be, "Please tell me about the project's progress. I'm feeling a little anxious right now." Based on this prompt, the system will provide information tailored to the user's emotions.

[0730] In this way, by combining servers, terminals, and advanced AI technology to recognize user emotions and utilize that information, the accuracy of information provision is improved, and a more personalized user experience is realized.

[0731] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0732] Step 1:

[0733] The server receives natural language data entered by the user from the terminal. This input data is in text or audio format and contains the information the user is seeking. The server treats this data as initial input and prepares it for subsequent processing steps.

[0734] Step 2:

[0735] The server sends the received natural language data to the natural language processing engine. The natural language processing engine analyzes the text or audio data to extract the subject and important keywords. Techniques such as morphological analysis and syntactic analysis are used for this data processing, and ultimately the relevant keywords are output.

[0736] Step 3:

[0737] The server sends the extracted keywords to the emotion recognition engine. The emotion recognition engine infers emotions from the user's input and uses the results to analyze the emotional state. This process evaluates word selection and context to obtain emotional data as output. This information is used later for information evaluation and summarization.

[0738] Step 4:

[0739] The server searches for relevant information by referencing the system's information index based on keywords extracted through natural language processing and sentiment information obtained through sentiment recognition. This search process evaluates relevant documents and information within the database and retrieves the information that best suits the user's needs as output.

[0740] Step 5:

[0741] The server sends the acquired relevant information to the generating AI model. The generating AI model examines this information and generates a summary. Here, information is selected and its expression is adjusted according to the user's emotional state. The output of the AI ​​model is a summary processed in a way that is optimal for the user.

[0742] Step 6:

[0743] The server sends the generated summary to the terminal and presents it to the user. This allows the user to receive information in a way that takes their emotions into consideration, enabling them to enjoy a more meaningful information experience.

[0744] (Application Example 2)

[0745] 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".

[0746] In modern society, information overload makes it difficult for users to access necessary information quickly and appropriately, sometimes leading to emotional burdens. Furthermore, because the information provided is not optimized for users' emotions, there is a challenge in that users are unable to effectively utilize that information.

[0747] 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.

[0748] In this invention, the server includes means for collecting data from an information processing device and creating an information index based thereon, means for analyzing the user's emotions and adaptively adjusting the information provided based on that analysis, and means for providing content based on the user's emotions. This enables the user to quickly access information and content that is appropriate to their emotional state.

[0749] An "information processing device" is a device that includes hardware and software for collecting user data and supporting the indexing and retrieval of information.

[0750] "Data" refers to all input information collected from users, including text data and audio data.

[0751] An "information index" is a structure for efficiently searching for information within a database built on data.

[0752] "Users" refer to individuals or end-users who utilize this system, and information is provided to them according to their feelings and requests.

[0753] "Natural language" refers to the forms of language that people use on a daily basis, and is expressed in forms such as text and audio.

[0754] "Related terms" are important words and phrases included in natural language input from users, and are key elements in information retrieval.

[0755] "Methods of searching" refers to the process of systematically finding relevant information using an index.

[0756] "Means of analyzing emotions" refer to technologies used to identify a user's emotional state and reflect it in the information provided.

[0757] "Means of providing content" refers to the process of presenting information and materials in a format that is appropriate to the user's emotions.

[0758] This invention is a system for optimizing information provision based on user emotions using an information processing device. The server collects user data from terminals such as smartphones and smart glasses and creates an information index. Questions and feedback entered by the user in natural language are analyzed using a natural language processing engine and relevant words are extracted. Based on the extracted words, the server searches the index for relevant information and then scrutinizes and selects that information.

[0759] In addition, the server uses an emotion analysis module to analyze user emotions in real time. Specifically, it leverages the Google Cloud Vision API and Amazon Rekognition to identify user emotions from image and audio data. This emotion analysis is used to adjust the priority of information provision and deliver content accordingly.

[0760] The generated information and content are further refined by an AI model and summarized in a way that is suitable for the user. At this stage, the results of sentiment analysis are incorporated into the summarization algorithm.

[0761] For example, if a user searches for information on their smartphone while at work, and emotional analysis reveals that they are experiencing "stress," the server will prioritize displaying information or entertainment content that has a relaxing effect on that user.

[0762] An example of a prompt to input into the generative AI model might be: "Generate recommendations based on the user's current emotions. The user is feeling stressed." This prompt allows the system to provide optimized information.

[0763] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0764] Step 1:

[0765] The device collects text and voice data from the user. Text and voice input are acquired via the microphone and camera and sent to the server. Here, data preprocessing is performed using the Google Cloud Vision API and Amazon Rekognition.

[0766] Step 2:

[0767] The server analyzes the received data using a natural language processing engine, extracting relevant terms from the input questions and feedback. This analysis serves as a key for referencing the information index. The output is a list of the analyzed terms, which is used to proceed to the next search step.

[0768] Step 3:

[0769] The server uses an emotion analysis module to identify the user's emotional state from text and audio data. The input is the raw data obtained in step 1, and the output is an emotion label (e.g., relaxed, stressed). Here, an emotion analysis algorithm is used to perform data calculations and evaluate the user's emotions.

[0770] Step 4:

[0771] The server uses the extracted list of terms to search for related information from the information index. The input is the list of terms obtained in step 2, and the output is a list of related information and content. In this step, the existing database is searched and the information is prioritized.

[0772] Step 5:

[0773] The server scrutinizes the search results and generates a summary using a generative AI model. The input for this step is the information list output in step 4 and the sentiment labels output in step 3. The output is summary information adjusted according to the user's sentiment. Here, a summary generation algorithm is used to create a concise summary of the information.

[0774] Step 6:

[0775] The server provides the generated summary to the user's terminal, and the user utilizes the presented information. The input is the summary information generated in step 5, and the output is the final display on the user interface. In this step, information is provided according to the user's situation, improving the user experience.

[0776] 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.

[0777] 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.

[0778] 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.

[0779] 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.

[0780] 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.

[0781] 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.

[0782] 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.

[0783] 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.

[0784] 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."

[0785] 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.

[0786] 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.

[0787] 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.

[0788] 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.

[0789] 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.

[0790] 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.

[0791] 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.

[0792] 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.

[0793] 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.

[0794] 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.

[0795] 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.

[0796] 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.

[0797] The following is further disclosed regarding the embodiments described above.

[0798] (Claim 1)

[0799] A means for collecting data from an information processing device and creating an information index based on that data,

[0800] A means for analyzing natural language questions from users and extracting relevant words and phrases,

[0801] A means of searching for related information from an index based on extracted terms,

[0802] A means of scrutinizing and selecting relevant information from search results and generating a summary,

[0803] A means of providing the generated summary information to the user's terminal,

[0804] A system that includes this.

[0805] (Claim 2)

[0806] The system according to claim 1, which uses an automated learning algorithm to analyze user feedback and improve the accuracy of information scrutiny and selection means.

[0807] (Claim 3)

[0808] The system according to claim 1, comprising means for registering newly acquired information by a user in a shared information base and updating it as searchable information.

[0809] "Example 1"

[0810] (Claim 1)

[0811] A means for collecting data from an information processing device and creating indicators based on that data,

[0812] A means of analyzing natural language questions from users and extracting relevant expressions,

[0813] A means for searching for relevant information from indicators based on extracted expressions,

[0814] A method for scrutinizing and selecting relevant information from search results using a generative AI model,

[0815] A means of summarizing the selected relevant information and providing it to the user's terminal,

[0816] A system that includes this.

[0817] (Claim 2)

[0818] The system according to claim 1, which uses an automated learning algorithm to analyze user feedback and improve the accuracy of information scrutiny and selection means.

[0819] (Claim 3)

[0820] The system according to claim 1, comprising means for registering newly acquired information by a user in a shared knowledge base and updating it as searchable information.

[0821] "Application Example 1"

[0822] (Claim 1)

[0823] A means for collecting data from an information processing device and creating an information index based on that data,

[0824] A means for analyzing natural language questions from users and extracting relevant words and phrases,

[0825] A means of searching for related information from an index based on extracted terms,

[0826] A means of scrutinizing and selecting relevant information from search results and generating a summary,

[0827] A means of providing the generated summary information to the user's terminal,

[0828] A means of converting natural language speech input into text,

[0829] A method for selecting the optimal route based on past traffic data and real-time traffic information,

[0830] A means for presenting selected route information via voice and visual means,

[0831] A system that includes this.

[0832] (Claim 2)

[0833] The system according to claim 1, which uses an automated learning algorithm to analyze user feedback and improve the accuracy of information scrutiny and selection means.

[0834] (Claim 3)

[0835] The system according to claim 1, comprising means for registering newly acquired information by a user in a shared information base and updating it as searchable information.

[0836] "Example 2 of combining an emotion engine"

[0837] (Claim 1)

[0838] A means for receiving natural language data entered by a user via a terminal,

[0839] A natural language processing tool that analyzes received natural language data and extracts themes and important words,

[0840] A means of searching for related information by referring to an information index based on extracted terms,

[0841] An emotion recognition method that recognizes emotions from user input data and uses that emotional information for information evaluation,

[0842] A means of scrutinizing search results using a generative AI model and generating summaries that reflect the user's emotional state,

[0843] A system including means for providing generated summary information to a terminal.

[0844] (Claim 2)

[0845] The system according to claim 1, which uses an automated learning algorithm to analyze user feedback and emotional data and improve the accuracy of information scrutiny and selection methods.

[0846] (Claim 3)

[0847] The system according to claim 1, comprising means for registering newly acquired information by a user in a shared information base and updating it as information that can be searched by other users.

[0848] "Application example 2 when combining with an emotional engine"

[0849] (Claim 1)

[0850] A means for collecting data from an information processing device and creating an information index based on that data,

[0851] A means for analyzing natural language questions from users and extracting relevant words and phrases,

[0852] A means of searching for related information from an index based on extracted terms,

[0853] A means of scrutinizing and selecting relevant information from search results and generating a summary,

[0854] A means of providing the generated summary information to the user's terminal,

[0855] A means of analyzing users' emotions and adaptively adjusting information provision based on that analysis,

[0856] Means of providing content based on user emotions,

[0857] A system that includes this.

[0858] (Claim 2)

[0859] The system according to claim 1, which uses an automated learning algorithm to analyze user feedback and improve the accuracy of information scrutiny and selection means.

[0860] (Claim 3)

[0861] The system according to claim 1, comprising means for registering newly acquired information by a user in a shared information base and updating it as searchable information. [Explanation of Symbols]

[0862] 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 for collecting data from an information processing device and creating an information index based on that data, A means for analyzing natural language questions from users and extracting relevant words and phrases, A means of searching for related information from an index based on extracted terms, A means of scrutinizing and selecting relevant information from search results and generating a summary, A means of providing the generated summary information to the user's terminal, A means of converting natural language speech input into text, A method for selecting the optimal route based on past traffic data and real-time traffic information, A means for presenting selected route information via voice and visual means, A system that includes this.

2. The system according to claim 1, which uses an automated learning algorithm to analyze user feedback and improve the accuracy of information scrutiny and selection means.

3. The system according to claim 1, comprising means for registering newly acquired information by a user in a shared information base and updating it as searchable information.

Images