system

Abstract

We provide the system. [Solution] An autonomous data acquisition method capable of collecting information, A means of analyzing acquired information using a natural language processing engine, A means of evaluating the reliability of information based on the analysis results, A means of visualizing and displaying the evaluated information, A means of notifying system users of the analysis results, A means of proposing business strategies based on user-defined conditions, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In small and medium-sized enterprises, it is required to quickly catch up with the latest technologies and industry trends and reflect them in appropriate business strategies. However, it takes a great deal of time and cost to collect and analyze information, and it is also difficult to judge the reliability of information. The present invention aims to solve these problems, improve the efficiency of information collection and analysis, and provide a system that enables the proposal of a business strategy based on highly reliable information.

Means for Solving the Problems

[0005] This invention efficiently collects information from around the world using autonomous data acquisition means capable of information gathering, and evaluates the relevance of the information by analyzing the acquired information with a natural language processing engine. Subsequently, the reliability of the information is calculated based on the agreement rate and the presence or absence of primary information based on the analysis results, thereby improving reliability. Furthermore, the evaluated information can be visualized and displayed in graph and chart format. In addition, the invention solves the problem by providing a means to notify system users of the analysis results and propose specific business strategies based on the user's set conditions.

[0006] "Information gathering" is the process of collecting data and information from various sources.

[0007] An "autonomous data acquisition means" is a device or program that has the function of automatically acquiring data without receiving specific instructions.

[0008] A "natural language processing engine" is a set of technologies or software that enable computers to understand and analyze human language.

[0009] "Analysis results" refer to conclusions and information obtained through data analysis.

[0010] "Means for evaluating reliability" refers to methods or systems for determining the accuracy and truthfulness of information.

[0011] "Visualization" is a technique or process for visually displaying information or data in the form of graphs, charts, and other visual media.

[0012] "Means of notification" refers to methods or devices for delivering information to users.

[0013] "Means of proposing business strategies" refers to methods or systems that allow companies and organizations to present concrete plans based on information in order to determine their course of action in the market. [Brief explanation of the drawing]

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

MODE 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 number (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 number 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 number 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] The system for implementing this invention mainly consists of three components: a server, a terminal, and a user. The server plays a central role in information gathering, analysis, reliability evaluation, and business strategy proposals.

[0036] First, the server has the means to autonomously collect information from the internet. Through this means, the server crawls digital information sources such as websites, news feeds, and social media, and retrieves relevant information based on pre-configured keywords and areas of interest. Furthermore, the server can also collaborate with information sources such as databases from specific industry-specific organizations to acquire new data.

[0037] Next, the server analyzes the acquired information using a natural language processing engine. Specifically, the server tokenizes the information, analyzes its text structure, and extracts the topics and importance associated with each piece of information. This allows the server to determine not only the relevance of the information but also its value and importance.

[0038] Furthermore, the server evaluates the reliability of the analyzed information based on the agreement rate and the presence or absence of primary sources. Based on this evaluation, highly reliable information is selected. The server has components for visualizing the accumulated information, and presents the information visually as graphs and charts on the dashboard.

[0039] The terminal plays the role of displaying information through the user interface. Through this terminal, users can receive information delivered from the server and easily view the visualized data.

[0040] Ultimately, the server proposes a customized business strategy based on the user's settings. This strategic proposal is generated from reliable collected information and is provided as a concrete and actionable plan tailored to the user's needs. The server also periodically notifies the user of the analysis results, ensuring that they receive this information in a timely manner.

[0041] For example, if a small or medium-sized manufacturer is considering entering the market with a new technology, this system can be used to quickly obtain reliable information on the latest technological trends in the industry, the actions of competitors, and changes in regulations. As a result, strategic decision-making based on that information becomes possible.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The server autonomously collects information from the internet and affiliated databases using pre-configured keywords and plugins. The server also launches a web crawler to visit specified URLs and news feeds and extract relevant data.

[0045] Step 2:

[0046] The server analyzes the collected information using a natural language processing engine. Specifically, it tokenizes the text and performs morphological analysis to identify keywords and topics within the text. In this process, it classifies the relevance and themes of the information.

[0047] Step 3:

[0048] The server evaluates the reliability of the information based on the analysis results. The server uses a matching algorithm to verify whether the same information is provided by multiple reliable sources. It calculates a reliability score and determines the truthfulness of the information.

[0049] Step 4:

[0050] The server transforms data into graphs and charts to visualize information whose reliability has been assessed. The server uses a visualization engine to visually format the information in formats such as bar graphs and pie charts.

[0051] Step 5:

[0052] The terminal displays visualization data received from the server to the user. The terminal provides real-time visual information through a web-based dashboard, making it accessible to the user.

[0053] Step 6:

[0054] The server proposes specific business strategies based on the user's settings. The server combines existing analytical data with the user's past decision-making history to generate a recommended action plan, which is then sent to the terminal.

[0055] Step 7:

[0056] Users make business decisions based on the information and suggestions displayed on their devices. They consider strategic proposals and make judgments to take appropriate action.

[0057] (Example 1)

[0058] 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."

[0059] In today's information society, it is crucial to quickly and accurately collect and analyze useful information from diverse sources. However, evaluating the reliability of information and formulating actionable strategies through the visualization of complex data is not easy. In particular, it is difficult to determine the degree of reliability of each piece of information and to present it in a visually understandable manner. The present invention aims to provide a system that enables the consistent streamlining of the process from information collection to reliability evaluation, visualization, and strategy proposal.

[0060] 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.

[0061] In this invention, the server includes an autonomous data acquisition means for collecting information, a natural language processing means for processing the acquired information, and a means for determining the reliability of the information based on the processing results. This makes it possible to quickly collect useful information, evaluate its reliability, visualize it, and provide it to the user in an easily understandable format.

[0062] An "autonomous data acquisition means for collecting information" is a function that automatically searches for and acquires relevant information from multiple information sources existing on a network.

[0063] "Natural language processing methods" are technologies that linguistically analyze acquired information and grasp its meaning and structure from text.

[0064] "Means for determining the reliability of information" refers to a system for evaluating the accuracy and validity of collected information and selecting information with a high degree of reliability.

[0065] "Visualization display means" refers to a function that represents data as shapes or charts in order to present the analyzed information in an easy-to-understand manner for the user.

[0066] "Means of notifying users of analysis results" refers to a process that provides users with timely information on the analysis and evaluation results from the server to support their decision-making.

[0067] "Means of proposing business plans" refers to methods for presenting optimal action plans and strategies based on reliable information, tailored to the individual user's settings and needs.

[0068] The system of this invention consists of three components: a server, a terminal, and a user. The server primarily handles information collection, analysis, reliability evaluation, and business strategy proposals. Specifically, the server autonomously obtains target data from internet information sources by performing web scraping using Python or other programming languages. Furthermore, a natural language processing engine, such as TENSORFLOW® or NLTK, analyzes the collected information. In this analysis process, the server tokenizes the information, grasps its grammatical structure, and extracts useful topics and key points.

[0069] To evaluate the reliability of information generated by the server, statistical algorithms are used to examine the accuracy of the information sources. Based on methods such as PageRank, the reliability of each piece of information is scored, and information with high reliability is prioritized.

[0070] The device provides this information as a user interface. Specifically, the visualized data is converted into graphs and charts using Matplotlib and D3.js, and displayed in a format that is easy for the user to understand. Through this interface, users can instantly access reliable information and use it for daily decision-making.

[0071] The server ultimately presents a customized business strategy based on the user's settings and collected information. This process generates specific and actionable strategic proposals tailored to the user's particular needs and conditions. These strategic proposals can be used to inform management decisions and market entry planning.

[0072] For example, when a manufacturer plans to enter the market with a new technology, the server can quickly provide information on industry technological trends, competitor activities, and regulations, helping the company formulate an appropriate business strategy based on that information.

[0073] Example of a prompt:

[0074] "Please suggest ways to use AI to track real-time technological trends."

[0075] "Could you recommend an AI tool to streamline strategic proposals based on market analysis?"

[0076] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0077] Step 1:

[0078] The server collects information. It is given pre-configured keywords and topics as input. Based on this, the server automatically retrieves data from internet sources using web scraping tools and APIs. During this process, it collects real-time information from multiple databases and news sites via EDI (Electronic Data Interchange) and web APIs. The collected raw data is stored in a database as output.

[0079] Step 2:

[0080] The server analyzes the collected information using a natural language processing engine. The input is the raw data collected in step 1. The server tokenizes this data using a natural language processing tool (e.g., NLTK or spaCy) and analyzes its grammatical structure. Specifically, the text data is divided into tokens and tagged with parts of speech. This analysis extracts the main points and related topics of the information, and structured data is generated as output.

[0081] Step 3:

[0082] The server evaluates the reliability of the analyzed information. The input is the analysis results from step 2. The server verifies the reliability of the information sources using an evaluation algorithm. In this process, mathematical methods and machine learning models are used to calculate the agreement rate and the reliability score of the information sources. As output, filtered information with evaluated reliability is provided.

[0083] Step 4:

[0084] The server prepares to visualize the evaluated information. The input is the reliability-evaluated data from step 3. The server uses visualization tools (Matplotlib or D3.js) to convert this data into graphs and charts. Specifically, the data is converted into a diagrammatic format, and visual content is created using an easy-to-read layout. As output, the visualized data is prepared for the dashboard.

[0085] Step 5:

[0086] The terminal presents visualized information to the user. The input is the visualized data from step 4. The terminal displays this data in a user interface, making it easy for the user to understand the information. Specifically, through an interactive dashboard, the user can filter, compare, and visually analyze each piece of information. As output, the information presented to the user provides a foundation to support decision-making.

[0087] Step 6:

[0088] The server proposes customized business strategies based on user settings. Input consists of reliable information and user-defined conditions. Based on this, the server uses a generative AI model to create strategic proposals for decision-making support. This model combines collected data with user needs to construct an optimal action plan. The output provides the user with a concrete and actionable strategy.

[0089] (Application Example 1)

[0090] 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."

[0091] In today's world, the sheer volume of information makes it difficult to efficiently collect, evaluate the reliability of, and accurately present the information that users with specific interests need. Furthermore, systems that provide information optimized for individual users in real time are still insufficient. As a result, users struggle to obtain the information necessary for strategic decision-making in a timely and reliable manner.

[0092] 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.

[0093] In this invention, the server includes an autonomous data acquisition means capable of collecting information, a means for analyzing the acquired information using a natural language processing engine, a means for evaluating the reliability of the information based on the analysis results, a means for visualizing and displaying the evaluated information, a means for notifying system users of the analysis results, a means for proposing business strategies based on user-defined conditions, and an application means for personalized delivery of information based on user-specified interests. As a result, users can efficiently acquire the latest and most reliable information tailored to their interests and needs, enabling rapid decision-making.

[0094] An "autonomous data acquisition means" is a device that has the function of automatically collecting information from digital information sources such as websites and social media on the internet.

[0095] A "natural language processing engine" is software that tokenizes acquired information, analyzes its text structure, and extracts topics and importance.

[0096] "Means for evaluating the reliability of information" refers to devices or software that have the function of quantifying the accuracy and reliability of collected information based on the information's consistency rate and the presence or absence of primary sources.

[0097] "Means for visualizing and displaying information" refers to devices or software that display analyzed information in the form of graphs or charts, making it easy for users to understand.

[0098] "Means for notifying analysis results" refers to devices or software that have the function of informing system users of the results of the collected and analyzed information in real time or periodically.

[0099] A "means for proposing business strategies" refers to a device or software that has the function of formulating individual strategies based on reliable information collected according to the user's set conditions and presenting them to the user.

[0100] "Personalized information delivery methods" refer to devices or software that have the function of selecting content based on the user's specified interests and topics and providing it in an individually optimized format.

[0101] The system implementing this invention mainly consists of three components: a server, a terminal, and a user. The server autonomously collects data from various digital information sources on the internet using web crawling technologies such as BeautifulSoup and Scrapy for information gathering. The collected data is tokenized, text-analyzed, and topic-extracted using natural language processing libraries such as NLTK and spaCy, and the relevance and importance of the information are evaluated. Next, the reliability of the information is quantified based on the agreement rate of the collected data and the presence or absence of primary information, and highly reliable information is selected.

[0102] The server visualizes this data as graphs and charts using Matplotlib and Plotly, and provides it to the user in a dashboard format. Based on the analysis results, it also generates and notifies the user of business strategy suggestions that take into account the user's specified conditions. The device, through a mobile application built with React Native, provides the user with an intuitive interface and displays the visualized information and strategic suggestions.

[0103] For example, if a user expresses interest in "environmental issues," this system collects and analyzes the latest news and topics related to the environment, providing the user with reliable information in a personalized format. This allows the user to efficiently make strategic decisions regarding their chosen area of ​​interest.

[0104] Examples of prompts for the generated AI model include, "Please tell me about the latest industry trends regarding environmental issues," and "Please explain the major environmental technology innovations this week." In this way, through the collection and analysis process, users can obtain the necessary insights in real time.

[0105] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0106] Step 1:

[0107] The server collects data from internet sources. It takes a list of website and social media URLs as input and crawls them using tools like BeautifulSoup and Scrapy. As output, it retrieves raw HTML data and JSON data obtained from APIs. This allows for the collection of relevant information based on specific keywords.

[0108] Step 2:

[0109] The server analyzes the collected data using a natural language processing engine. The input is the raw data obtained in step 1. The data is tokenized using NLTK or spaCy, and part-of-speech tagging and entity recognition are performed. The output provides structural information and topics of the analyzed text data.

[0110] Step 3:

[0111] The server evaluates the reliability of the information based on the analysis results. The input is the text information analyzed in step 2. Reliability is calculated using a scoring method, with the agreement rate and the presence or absence of primary information used as evaluation criteria. A set of highly reliable information is generated as output.

[0112] Step 4:

[0113] The server visualizes reliable information and displays it on a dashboard. The input is the information evaluated in step 3. It is converted into graphs and charts using Matplotlib or Plotly. The output provides a user-friendly visual presentation.

[0114] Step 5:

[0115] The server notifies the user of the analysis results. The input is the data visualized in step 4. Updates are sent to the user periodically using email or push notifications. As output, the information becomes immediately accessible on the user's device.

[0116] Step 6:

[0117] The server proposes business strategies based on the user's settings. The input consists of the information filtered in step 3 and the conditions set by the user. Using an AI model, it generates customized strategic advice. The output provides a strategic plan tailored to the user's specific needs.

[0118] Step 7:

[0119] The device delivers personalized information based on the user's specified interests. The inputs are the business strategy generated in step 6 and the information visualized in step 4. React Native is used to display the information on an intuitive UI. The output provides the user with individually optimized information.

[0120] 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.

[0121] In one embodiment of this invention, the system consists of three components: a server, a terminal, and a user. The server plays a central role in processing functions such as information gathering, analysis, reliability evaluation, sentiment recognition, and business strategy proposals in a unified manner.

[0122] First, the server has an information gathering function, using a web crawler to collect information from the internet and databases based on specified keywords and topics. The collected information is then analyzed by a natural language processing engine. Specifically, the server performs syntactic analysis of the information and efficiently extracts topics and keywords of interest.

[0123] Next, the server evaluates the reliability of the information based on the analysis results. Here, it uses a consistency algorithm to check the consistency of data from multiple sources and calculates a reliability score. Furthermore, this system integrates an emotion engine to recognize the user's emotions. The server analyzes the user's input and past behavioral data to determine the user's emotional state.

[0124] The server dynamically adjusts the presentation of information based on the user's emotions. Therefore, during the visualization phase of the analysis results, the user's emotional state, as recognized by the emotion engine, is taken into consideration, and the design and colors of graphs and charts are adjusted accordingly.

[0125] The device displays visualized information and suggestions to the user through a user interface. Users can review the information displayed through the dashboard on the device and receive data presented in a way that suits their emotions.

[0126] Ultimately, the server adjusts the proposed business strategy based on the analysis results of the emotion engine. This strategic proposal is optimized for the user's emotional state and sent as a customized, specific action plan.

[0127] For example, if a user is feeling anxious about the progress of a new project, this system will provide information in a supportive tone and offer positive strategic suggestions. This allows the user to make optimal business decisions while also satisfying their emotional needs.

[0128] The following describes the processing flow.

[0129] Step 1:

[0130] The server activates a web crawler based on pre-configured keywords and topics. The server automatically visits specific websites and news feeds, collecting relevant data and information.

[0131] Step 2:

[0132] The server analyzes the collected information using a natural language processing engine. This analysis involves text tokenization and morphological analysis to extract interesting topics and key modules. The server then converts the obtained data into structured data and stores it in a database.

[0133] Step 3:

[0134] The server applies a concordance algorithm to evaluate the reliability of the information using the analysis results. The server calculates the degree of concordance of information obtained from multiple reliable data sources and derives a reliability score.

[0135] Step 4:

[0136] The server recognizes the user's emotional state using an emotion engine. The server analyzes user input and past behavioral data to identify emotional patterns. Based on this, the server determines the user's current emotional state.

[0137] Step 5:

[0138] The server adjusts the information visualization format based on the emotional state obtained. Using a visualization engine, it changes the color and design of graphs and charts to match the user's emotions, optimizing the visual presentation.

[0139] Step 6:

[0140] The terminal displays visualization data and coordinated information sent from the server on a user interface. The terminal provides a user-friendly and intuitive dashboard, making it easy to understand the information.

[0141] Step 7:

[0142] The server generates business strategy proposals based on analysis by the emotion engine. The server adjusts the proposals considering the user's emotions, and builds a strategy that is specific and instills confidence.

[0143] Step 8:

[0144] Users make necessary business decisions based on the information and strategic suggestions displayed on their devices. They formulate action plans, taking into account the presented data and their own emotional state.

[0145] (Example 2)

[0146] 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".

[0147] In today's world, vast amounts of data exist on the internet, making it difficult to efficiently collect and analyze important information. Furthermore, evaluating the reliability of information is challenging, and there is a need to provide data to users in an appropriate format. Additionally, there is a demand for developing and presenting business strategies that take into account users' emotional states, but existing technologies are unable to adequately meet this requirement, posing a significant challenge.

[0148] 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.

[0149] In this invention, the server includes means for autonomously acquiring data, means for analyzing the acquired data using natural language processing technology, and means for evaluating the reliability of the data based on the analysis results. This enables the efficient collection and analysis of highly reliable information from vast amounts of data. Furthermore, by integrating an emotion engine and adjusting data visualization according to the user's emotions, data can be provided in a form suitable for the user. In addition, it becomes possible to propose business strategies based on recognized emotions, allowing users to make optimal decisions according to their emotions.

[0150] "Means of autonomous data acquisition" refers to technologies that automatically collect necessary information from the internet or databases based on specific keywords or topics.

[0151] "Natural language processing technology" refers to computer programs that enable machines to understand, analyze, and process human language appropriately.

[0152] "Methods for evaluating data reliability" refer to methods for quantifying the reliability of collected information and determining the consistency and trustworthiness of that information.

[0153] An "emotion engine" is a system that analyzes user input and past behavioral data to recognize and judge the user's emotions.

[0154] "Means of adjusting data visualization" refers to methods for optimizing the display format of information based on analysis results and the emotional state of users, making it visually easier to understand.

[0155] "Means of proposing business strategies" refers to methods of presenting appropriate action plans and strategies to users based on analyzed data and recognized emotional states.

[0156] The system of the present invention consists of three main components: a server, a terminal, and a user. The server is responsible for sequentially executing the following functions: information gathering, natural language processing, data reliability evaluation, sentiment recognition, and business strategy proposal.

[0157] First, the server autonomously collects data. Using technologies such as web crawlers, it gathers information from websites and data stores based on specified keywords and topics. This ensures that relevant information is retrieved efficiently.

[0158] Next, the server applies natural language processing techniques to the collected data. Specifically, it uses a natural language processing engine to parse the data and extract important topics and keywords. This step involves tokenizing and tagging the data by part of speech.

[0159] Next, the server evaluates the reliability of the data. Using a concordance algorithm, it calculates the consistency of data obtained from multiple sources and generates a reliability score. This process allows users to obtain information about the reliability of the data.

[0160] Furthermore, the server uses an emotion engine to recognize the user's emotions. By analyzing the user's input and past behavioral data, the user's emotional state is determined. This allows information to be provided in a way that is more appropriate to the user.

[0161] The device presents the user with adjusted information through its user interface. Here, the design and color scheme of graphs and charts are dynamically adjusted based on the results of the emotion engine. For example, if the user is feeling stressed, the data will be displayed with a calmer design.

[0162] Ultimately, the server proposes a business strategy tailored to the user's emotions. This strategy is then communicated to the user via their device as a customized, specific action plan. For example, if a project manager is seeking new measures to mitigate risks, the server uses a generative AI model to propose a positive and effective strategy.

[0163] As part of this process, an example of a prompt for the generated AI model is, "Please propose risk assessments and countermeasures for the progress of a new project from a positive perspective." This system helps users make emotionally driven decisions and improves work efficiency.

[0164] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0165] Step 1:

[0166] The server collects information using a web crawler. The crawler automatically retrieves data from the internet and data stores based on specified keywords or topics. The input to this process is keywords or topics, and the output is a collection of related web pages and article data.

[0167] Step 2:

[0168] The server analyzes the collected data using a natural language processing engine. Specifically, it tokenizes the data and extracts topics and keywords through part-of-speech tagging and syntactic analysis. The input for this step is web page or article data, and the output is a list of extracted topics and keywords.

[0169] Step 3:

[0170] The server evaluates the reliability of the data using a consistency rate algorithm. It generates a reliability score by comparing data from multiple sources and calculating consistency. The input is an extracted list of topics and keywords, and the output is the corresponding reliability score.

[0171] Step 4:

[0172] The server uses an emotion engine to recognize the user's emotional state. It analyzes the user's input text and past behavioral data and assigns an emotion label (e.g., positive, negative, neutral). The input is user text data or behavioral data, and the output is an emotion label.

[0173] Step 5:

[0174] The server adjusts the visualization of information based on the user's emotions. It dynamically changes the design and color scheme of graphs and charts using the results of the emotion engine. Inputs are confidence scores and emotion labels, and the output is an optimized graph or chart.

[0175] Step 6:

[0176] The terminal presents the user with optimized information. Through the user interface, it displays optimized graphs and charts and provides direct feedback. The input in this step is the optimized graphs and charts, and the output is the information provided to the user.

[0177] Step 7:

[0178] The server proposes business strategies based on the user's emotional state. Using a generative AI model based on data and emotional labels, it develops specific action plans, converts them into prompt messages, and sends them as notifications. Inputs are emotional labels and analyzed data, and output is a customized business proposal.

[0179] (Application Example 2)

[0180] 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".

[0181] There is a growing need for highly personalized information presentation based on information reliability assessment and sentiment analysis. In particular, conventional systems lack the ability to visualize information and propose business strategies that take into account the user's emotional state, which makes it difficult to make optimal decisions that meet the user's needs.

[0182] 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.

[0183] In this invention, the server includes an autonomous data acquisition means capable of collecting information, a means for analyzing the acquired information using a natural language processing engine, a means for evaluating the reliability of the information based on the analysis results, a means for dynamically visualizing and displaying the evaluated information according to the user's emotional state, and a means for proposing a business strategy based on the user's set conditions and emotional state. This makes it possible to provide information and business strategies optimized for the user's emotions.

[0184] "Autonomous data acquisition means capable of information gathering" refers to automated functions that can efficiently collect information from the internet and databases.

[0185] A "natural language processing engine" includes software technologies that analyze acquired text information and understand its syntax and meaning.

[0186] "Means for evaluating reliability" refers to algorithms or processes for numerically or qualitatively evaluating the accuracy and reliability of collected information.

[0187] "Means of dynamically visualizing and displaying information according to emotional state" refers to a function that adjusts the format and design of the displayed information in real time based on the user's emotional recognition.

[0188] "Means of proposing business strategies" refers to a function that provides optimal business responses and action plans according to the user's emotional state and set conditions.

[0189] In an embodiment for implementing the present invention, the system consists of three basic components: a server, a terminal, and a user. The server plays a central role in collecting, analyzing, evaluating the reliability of, and recognizing sentiments from a broad information space, and ultimately proposing business strategies. Specifically, the server uses a web crawler as an autonomous data acquisition means to collect information from the internet and databases based on specified keywords and topics. The collected information is analyzed in detail using a natural language processing engine (e.g., SpaCy or NLTK). This analysis makes it possible to understand the syntax and meaning of the information and extract topics and keywords of interest.

[0190] In the next stage of analysis, the server performs a reliability assessment. It verifies the consistency of data from multiple sources using a consistency algorithm and calculates a reliability score. The server also integrates an emotion engine (e.g., Google Cloud Natural Language API) to analyze user input and past behavioral data to determine the user's emotional state. This allows the server to dynamically adjust the presentation of information and deliver it in a way that suits the user's emotions. For example, if the server determines that the user is seeking relaxation, it will display a screen with calming colors.

[0191] The device visualizes and presents these analysis results to the user in graph and chart format via a user interface. From the dashboard on the device, the user can review the received information and receive data in a way that aligns with their emotions. This allows the user to receive customized business strategies and make decisions optimized for their emotional state.

[0192] To give a concrete example, if a user is considering purchasing a new interior item, this system will recognize through sentiment analysis that the user is feeling anxious about the purchase. In this case, the system will provide advice in a reassuring tone and present the best options. An example of a prompt based on the generative AI model would be, "What emotions is this user experiencing while shopping at a furniture store? Please tell me how to recommend products that match those emotions."

[0193] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0194] Step 1:

[0195] The server uses a web crawler based on specified keywords and topics to collect information from the internet and databases. In this process, the server obtains a large amount of text data. The input is the search query and related internet sources, and the output is raw text data.

[0196] Step 2:

[0197] The server performs syntactic analysis on the acquired text data using a natural language processing engine (e.g., SpaCy or NLTK). The input is raw text data, and the output is the analyzed syntactic information and a set of important keywords. Based on these analysis results, the server performs data processing to extract important topics.

[0198] Step 3:

[0199] The server evaluates the reliability of the information using a reliability evaluation algorithm based on the analysis results. The input is the analyzed syntactic information and a list of information sources, and the output is a reliability score. The server calculates the agreement rate and performs data calculations to integrate the reliability of various information sources.

[0200] Step 4:

[0201] The server integrates an emotion engine (e.g., Google Cloud Natural Language API) to analyze the user's emotional state from their input data and past behavior history. The input is the user's input data and behavior history, and the output is the user's emotional state. Through emotion analysis, the server identifies the user's current emotion.

[0202] Step 5:

[0203] The terminal designs appropriate graphs and charts based on the analysis results and emotional state received from the server, and displays visualization information with dynamically adjusted colors. The input is the analysis results and emotional state, and the output is the visualization information presented to the user. The terminal dynamically changes based on the user and generates optimized visual information.

[0204] Step 6:

[0205] The server proposes a business strategy based on the analyzed emotional state. This proposal is an action plan customized to the user's emotional state. The input is the user's emotional state and settings, and the output is the proposed business strategy. The server integrates the information and builds an emotion-based strategy.

[0206] Step 7:

[0207] Users receive visualized information and suggestions through the device's dashboard and use them to inform their decision-making. The input is the presented visualized information and strategic suggestions, while the output is the action chosen by the user. Users can evaluate the information and make the choice that best suits them.

[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] The system for implementing this invention mainly consists of three components: a server, a terminal, and a user. The server plays a central role in information gathering, analysis, reliability evaluation, and business strategy proposals.

[0225] First, the server has the means to autonomously collect information from the internet. Through this means, the server crawls digital information sources such as websites, news feeds, and social media, and retrieves relevant information based on pre-configured keywords and areas of interest. Furthermore, the server can also collaborate with information sources such as databases from specific industry-specific organizations to acquire new data.

[0226] Next, the server analyzes the acquired information using a natural language processing engine. Specifically, the server tokenizes the information, analyzes its text structure, and extracts the topics and importance associated with each piece of information. This allows the server to determine not only the relevance of the information but also its value and importance.

[0227] Furthermore, the server evaluates the reliability of the analyzed information based on the agreement rate and the presence or absence of primary sources. Based on this evaluation, highly reliable information is selected. The server has components for visualizing the accumulated information, and presents the information visually as graphs and charts on the dashboard.

[0228] The terminal plays the role of displaying information through the user interface. Through this terminal, users can receive information delivered from the server and easily view the visualized data.

[0229] Ultimately, the server proposes a customized business strategy based on the user's settings. This strategic proposal is generated from reliable collected information and is provided as a concrete and actionable plan tailored to the user's needs. The server also periodically notifies the user of the analysis results, ensuring that they receive this information in a timely manner.

[0230] For example, if a small or medium-sized manufacturer is considering entering the market with a new technology, this system can be used to quickly obtain reliable information on the latest technological trends in the industry, the actions of competitors, and changes in regulations. As a result, strategic decision-making based on that information becomes possible.

[0231] The following describes the processing flow.

[0232] Step 1:

[0233] The server autonomously collects information from the internet and affiliated databases using pre-configured keywords and plugins. The server also launches a web crawler to visit specified URLs and news feeds and extract relevant data.

[0234] Step 2:

[0235] The server analyzes the collected information using a natural language processing engine. Specifically, it tokenizes the text and performs morphological analysis to identify keywords and topics within the text. In this process, it classifies the relevance and themes of the information.

[0236] Step 3:

[0237] The server evaluates the reliability of the information based on the analysis results. The server uses a matching algorithm to verify whether the same information is provided by multiple reliable sources. It calculates a reliability score and determines the truthfulness of the information.

[0238] Step 4:

[0239] The server transforms data into graphs and charts to visualize information whose reliability has been assessed. The server uses a visualization engine to visually format the information in formats such as bar graphs and pie charts.

[0240] Step 5:

[0241] The terminal displays visualization data received from the server to the user. The terminal provides real-time visual information through a web-based dashboard, making it accessible to the user.

[0242] Step 6:

[0243] The server proposes specific business strategies based on the user's settings. The server combines existing analytical data with the user's past decision-making history to generate a recommended action plan, which is then sent to the terminal.

[0244] Step 7:

[0245] Users make business decisions based on the information and suggestions displayed on their devices. They consider strategic proposals and make judgments to take appropriate action.

[0246] (Example 1)

[0247] 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."

[0248] In today's information society, it is crucial to quickly and accurately collect and analyze useful information from diverse sources. However, evaluating the reliability of information and formulating actionable strategies through the visualization of complex data is not easy. In particular, it is difficult to determine the degree of reliability of each piece of information and to present it in a visually understandable manner. The present invention aims to provide a system that enables the consistent streamlining of the process from information collection to reliability evaluation, visualization, and strategy proposal.

[0249] 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.

[0250] In this invention, the server includes an autonomous data acquisition means for collecting information, a natural language processing means for processing the acquired information, and a means for determining the reliability of the information based on the processing results. This makes it possible to quickly collect useful information, evaluate its reliability, visualize it, and provide it to the user in an easily understandable format.

[0251] An "autonomous data acquisition means for collecting information" is a function that automatically searches for and acquires relevant information from multiple information sources existing on a network.

[0252] "Natural language processing methods" are technologies that linguistically analyze acquired information and grasp its meaning and structure from text.

[0253] "Means for determining the reliability of information" refers to a system for evaluating the accuracy and validity of collected information and selecting information with a high degree of reliability.

[0254] "Visualization display means" refers to a function that represents data as shapes or charts in order to present the analyzed information in an easy-to-understand manner for the user.

[0255] "Means of notifying users of analysis results" refers to a process that provides users with timely information on the analysis and evaluation results from the server to support their decision-making.

[0256] "Means of proposing business plans" refers to methods for presenting optimal action plans and strategies based on reliable information, tailored to the individual user's settings and needs.

[0257] The system of this invention consists of three components: a server, a terminal, and a user. The server primarily handles information collection, analysis, reliability evaluation, and business strategy proposals. Specifically, the server autonomously obtains target data from internet information sources by performing web scraping using Python or other programming languages. Furthermore, a natural language processing engine, such as TensorFlow or NLTK, analyzes the collected information. In this analysis process, the server tokenizes the information, grasps its grammatical structure, and extracts useful topics and key points.

[0258] To evaluate the reliability of information generated by the server, statistical algorithms are used to examine the accuracy of the information sources. Based on methods such as PageRank, the reliability of each piece of information is scored, and information with high reliability is prioritized.

[0259] The device provides this information as a user interface. Specifically, the visualized data is converted into graphs and charts using Matplotlib and D3.js, and displayed in a format that is easy for the user to understand. Through this interface, users can instantly access reliable information and use it for daily decision-making.

[0260] The server ultimately presents a customized business strategy based on the user's settings and collected information. This process generates specific and actionable strategic proposals tailored to the user's particular needs and conditions. These strategic proposals can be used to inform management decisions and market entry planning.

[0261] For example, when a manufacturer plans to enter the market with a new technology, the server can quickly provide information on industry technological trends, competitor activities, and regulations, helping the company formulate an appropriate business strategy based on that information.

[0262] Example of a prompt:

[0263] "Please suggest ways to use AI to track real-time technological trends."

[0264] "Could you recommend an AI tool to streamline strategic proposals based on market analysis?"

[0265] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0266] Step 1:

[0267] The server collects information. It is given pre-configured keywords and topics as input. Based on this, the server automatically retrieves data from internet sources using web scraping tools and APIs. During this process, it collects real-time information from multiple databases and news sites via EDI (Electronic Data Interchange) and web APIs. The collected raw data is stored in a database as output.

[0268] Step 2:

[0269] The server analyzes the collected information using a natural language processing engine. The input is the raw data collected in step 1. The server tokenizes this data using a natural language processing tool (e.g., NLTK or spaCy) and analyzes its grammatical structure. Specifically, the text data is divided into tokens and tagged with parts of speech. This analysis extracts the main points and related topics of the information, and structured data is generated as output.

[0270] Step 3:

[0271] The server evaluates the reliability of the analyzed information. The input is the analysis results from step 2. The server verifies the reliability of the information sources using an evaluation algorithm. In this process, mathematical methods and machine learning models are used to calculate the agreement rate and the reliability score of the information sources. As output, filtered information with evaluated reliability is provided.

[0272] Step 4:

[0273] The server prepares to visualize the evaluated information. The input is the reliability-evaluated data from step 3. The server uses visualization tools (Matplotlib or D3.js) to convert this data into graphs and charts. Specifically, the data is converted into a diagrammatic format, and visual content is created using an easy-to-read layout. As output, the visualized data is prepared for the dashboard.

[0274] Step 5:

[0275] The terminal presents visualized information to the user. The input is the visualized data from step 4. The terminal displays this data in a user interface, making it easy for the user to understand the information. Specifically, through an interactive dashboard, the user can filter, compare, and visually analyze each piece of information. As output, the information presented to the user provides a foundation to support decision-making.

[0276] Step 6:

[0277] The server proposes customized business strategies based on user settings. Input consists of reliable information and user-defined conditions. Based on this, the server uses a generative AI model to create strategic proposals for decision-making support. This model combines collected data with user needs to construct an optimal action plan. The output provides the user with a concrete and actionable strategy.

[0278] (Application Example 1)

[0279] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0280] In today's world, the sheer volume of information makes it difficult to efficiently collect, evaluate the reliability of, and accurately present the information that users with specific interests need. Furthermore, systems that provide information optimized for individual users in real time are still insufficient. As a result, users struggle to obtain the information necessary for strategic decision-making in a timely and reliable manner.

[0281] 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.

[0282] In this invention, the server includes a self-governing data acquisition means capable of collecting information, a means for analyzing the acquired information by a natural language processing engine, a means for evaluating the reliability of the information based on the analysis result, a means for visualizing and displaying the evaluated information, a means for notifying the system user of the analysis result, a means for proposing a business strategy based on the user's set conditions, and an application means for personalizing and distributing information based on the interests specified by the user. Thereby, the user can efficiently obtain the latest and reliable information according to their own interests and needs, enabling rapid decision-making.

[0283] The "self-governing data acquisition means" is a device having a function of automatically collecting information from digital information sources such as websites on the Internet and social media.

[0284] The "natural language processing engine" is software that tokenizes the acquired information, analyzes the text structure, and extracts topics and importance.

[0285] The "means for evaluating the reliability of the information" is a device or software having a function of quantifying the accuracy and reliability of the collected information based on the coincidence rate of the information and the presence or absence of primary information.

[0286] The "means for visualizing and displaying the information" is a device or software for displaying the analyzed information in the form of graphs or charts so that it can be easily understood by the user.

[0287] The "means for notifying the analysis result" is a device or software having a function of notifying the system user of the result of the collected and analyzed information in real time or periodically.

[0288] The "means for proposing a business strategy" is a device or software having a function of formulating an individual strategy based on the highly reliable information collected based on the user's set conditions and presenting it to the user.

[0289] "Personalized information delivery methods" refer to devices or software that have the function of selecting content based on the user's specified interests and topics and providing it in an individually optimized format.

[0290] The system implementing this invention mainly consists of three components: a server, a terminal, and a user. The server autonomously collects data from various digital information sources on the internet using web crawling technologies such as BeautifulSoup and Scrapy for information gathering. The collected data is tokenized, text-analyzed, and topic-extracted using natural language processing libraries such as NLTK and spaCy, and the relevance and importance of the information are evaluated. Next, the reliability of the information is quantified based on the agreement rate of the collected data and the presence or absence of primary information, and highly reliable information is selected.

[0291] The server visualizes this data as graphs and charts using Matplotlib and Plotly, and provides it to the user in a dashboard format. Based on the analysis results, it also generates and notifies the user of business strategy suggestions that take into account the user's specified conditions. The device, through a mobile application built with React Native, provides the user with an intuitive interface and displays the visualized information and strategic suggestions.

[0292] For example, if a user expresses interest in "environmental issues," this system collects and analyzes the latest news and topics related to the environment, providing the user with reliable information in a personalized format. This allows the user to efficiently make strategic decisions regarding their chosen area of ​​interest.

[0293] Examples of prompts for the generated AI model include, "Please tell me about the latest industry trends regarding environmental issues," and "Please explain the major environmental technology innovations this week." In this way, through the collection and analysis process, users can obtain the necessary insights in real time.

[0294] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0295] Step 1:

[0296] The server collects data from internet sources. It takes a list of website and social media URLs as input and crawls them using tools like BeautifulSoup and Scrapy. As output, it retrieves raw HTML data and JSON data obtained from APIs. This allows for the collection of relevant information based on specific keywords.

[0297] Step 2:

[0298] The server analyzes the collected data using a natural language processing engine. The input is the raw data obtained in step 1. The data is tokenized using NLTK or spaCy, and part-of-speech tagging and entity recognition are performed. The output provides structural information and topics of the analyzed text data.

[0299] Step 3:

[0300] The server evaluates the reliability of the information based on the analysis results. The input is the text information analyzed in step 2. Reliability is calculated using a scoring method, with the agreement rate and the presence or absence of primary information used as evaluation criteria. A set of highly reliable information is generated as output.

[0301] Step 4:

[0302] The server visualizes reliable information and displays it on a dashboard. The input is the information evaluated in step 3. It is converted into graphs and charts using Matplotlib or Plotly. The output provides a user-friendly visual presentation.

[0303] Step 5:

[0304] The server notifies the user of the analysis results. The input is the data visualized in Step 4. Using functions such as email and push notifications, updated information is sent to the user regularly. As an output, the information becomes immediately accessible on the user's terminal.

[0305] Step 6:

[0306] The server proposes a business strategy based on the user's settings. The input is the information selected in Step 3 and the conditions set by the user. Using an AI model, customized strategic advice is generated. As an output, a strategic plan corresponding to the specific needs of the user is provided.

[0307] Step 7:

[0308] The terminal personalizes and distributes information based on the interests specified by the user. The input is the business strategy generated in Step 6 and the information visualized in Step 4. Using React Native, the information is displayed on an intuitive UI. As an output, individually optimized information is provided to the user.

[0309] Furthermore, an emotion engine for estimating the user's emotions may be combined. That is, the specific processing unit 290 may estimate the user's emotions using the emotion recognition model 59 and perform specific processing using the user's emotions.

[0310] As one form of implementing this invention, the system consists of three components: a server, a terminal, and a user. The server plays a central role in collectively processing functions such as information collection, analysis, reliability evaluation, emotion recognition, and business strategy proposal.

[0311] First, the server has an information gathering function, using a web crawler to collect information from the internet and databases based on specified keywords and topics. The collected information is then analyzed by a natural language processing engine. Specifically, the server performs syntactic analysis of the information and efficiently extracts topics and keywords of interest.

[0312] Next, the server evaluates the reliability of the information based on the analysis results. Here, it uses a consistency algorithm to check the consistency of data from multiple sources and calculates a reliability score. Furthermore, this system integrates an emotion engine to recognize the user's emotions. The server analyzes the user's input and past behavioral data to determine the user's emotional state.

[0313] The server dynamically adjusts the presentation of information based on the user's emotions. Therefore, during the visualization phase of the analysis results, the user's emotional state, as recognized by the emotion engine, is taken into consideration, and the design and colors of graphs and charts are adjusted accordingly.

[0314] The device displays visualized information and suggestions to the user through a user interface. Users can review the information displayed through the dashboard on the device and receive data presented in a way that suits their emotions.

[0315] Ultimately, the server adjusts the proposed business strategy based on the analysis results of the emotion engine. This strategic proposal is optimized for the user's emotional state and sent as a customized, specific action plan.

[0316] For example, if a user is feeling anxious about the progress of a new project, this system will provide information in a supportive tone and offer positive strategic suggestions. This allows the user to make optimal business decisions while also satisfying their emotional needs.

[0317] The following describes the processing flow.

[0318] Step 1:

[0319] The server activates a web crawler based on pre-configured keywords and topics. The server automatically visits specific websites and news feeds, collecting relevant data and information.

[0320] Step 2:

[0321] The server analyzes the collected information using a natural language processing engine. This analysis involves text tokenization and morphological analysis to extract interesting topics and key modules. The server then converts the obtained data into structured data and stores it in a database.

[0322] Step 3:

[0323] The server applies a concordance algorithm to evaluate the reliability of the information using the analysis results. The server calculates the degree of concordance of information obtained from multiple reliable data sources and derives a reliability score.

[0324] Step 4:

[0325] The server recognizes the user's emotional state using an emotion engine. The server analyzes user input and past behavioral data to identify emotional patterns. Based on this, the server determines the user's current emotional state.

[0326] Step 5:

[0327] The server adjusts the information visualization format based on the emotional state obtained. Using a visualization engine, it changes the color and design of graphs and charts to match the user's emotions, optimizing the visual presentation.

[0328] Step 6:

[0329] The terminal displays visualization data and coordinated information sent from the server on a user interface. The terminal provides a user-friendly and intuitive dashboard, making it easy to understand the information.

[0330] Step 7:

[0331] The server generates business strategy proposals based on analysis by the emotion engine. The server adjusts the proposals considering the user's emotions, and builds a strategy that is specific and instills confidence.

[0332] Step 8:

[0333] Users make necessary business decisions based on the information and strategic suggestions displayed on their devices. They formulate action plans, taking into account the presented data and their own emotional state.

[0334] (Example 2)

[0335] 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".

[0336] In today's world, vast amounts of data exist on the internet, making it difficult to efficiently collect and analyze important information. Furthermore, evaluating the reliability of information is challenging, and there is a need to provide data to users in an appropriate format. Additionally, there is a demand for developing and presenting business strategies that take into account users' emotional states, but existing technologies are unable to adequately meet this requirement, posing a significant challenge.

[0337] 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.

[0338] In this invention, the server includes means for autonomously acquiring data, means for analyzing the acquired data using natural language processing technology, and means for evaluating the reliability of the data based on the analysis results. This enables the efficient collection and analysis of highly reliable information from vast amounts of data. Furthermore, by integrating an emotion engine and adjusting data visualization according to the user's emotions, data can be provided in a form suitable for the user. In addition, it becomes possible to propose business strategies based on recognized emotions, allowing users to make optimal decisions according to their emotions.

[0339] "Means of autonomous data acquisition" refers to technologies that automatically collect necessary information from the internet or databases based on specific keywords or topics.

[0340] "Natural language processing technology" refers to computer programs that enable machines to understand, analyze, and process human language appropriately.

[0341] "Methods for evaluating data reliability" refer to methods for quantifying the reliability of collected information and determining the consistency and trustworthiness of that information.

[0342] An "emotion engine" is a system that analyzes user input and past behavioral data to recognize and judge the user's emotions.

[0343] "Means of adjusting data visualization" refers to methods for optimizing the display format of information based on analysis results and the emotional state of users, making it visually easier to understand.

[0344] "Means of proposing business strategies" refers to methods of presenting appropriate action plans and strategies to users based on analyzed data and recognized emotional states.

[0345] The system of the present invention consists of three main components: a server, a terminal, and a user. The server is responsible for sequentially executing the following functions: information gathering, natural language processing, data reliability evaluation, sentiment recognition, and business strategy proposal.

[0346] First, the server autonomously collects data. Using technologies such as web crawlers, it gathers information from websites and data stores based on specified keywords and topics. This ensures that relevant information is retrieved efficiently.

[0347] Next, the server applies natural language processing techniques to the collected data. Specifically, it uses a natural language processing engine to parse the data and extract important topics and keywords. This step involves tokenizing and tagging the data by part of speech.

[0348] Next, the server evaluates the reliability of the data. Using a concordance algorithm, it calculates the consistency of data obtained from multiple sources and generates a reliability score. This process allows users to obtain information about the reliability of the data.

[0349] Furthermore, the server uses an emotion engine to recognize the user's emotions. By analyzing the user's input and past behavioral data, the user's emotional state is determined. This allows information to be provided in a way that is more appropriate to the user.

[0350] The device presents the user with adjusted information through its user interface. Here, the design and color scheme of graphs and charts are dynamically adjusted based on the results of the emotion engine. For example, if the user is feeling stressed, the data will be displayed with a calmer design.

[0351] Ultimately, the server proposes a business strategy tailored to the user's emotions. This strategy is then communicated to the user via their device as a customized, specific action plan. For example, if a project manager is seeking new measures to mitigate risks, the server uses a generative AI model to propose a positive and effective strategy.

[0352] As part of this process, an example of a prompt for the generated AI model is, "Please propose risk assessments and countermeasures for the progress of a new project from a positive perspective." This system helps users make emotionally driven decisions and improves work efficiency.

[0353] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0354] Step 1:

[0355] The server collects information using a web crawler. The crawler automatically retrieves data from the internet and data stores based on specified keywords or topics. The input to this process is keywords or topics, and the output is a collection of related web pages and article data.

[0356] Step 2:

[0357] The server analyzes the collected data using a natural language processing engine. Specifically, it tokenizes the data and extracts topics and keywords through part-of-speech tagging and syntactic analysis. The input for this step is web page or article data, and the output is a list of extracted topics and keywords.

[0358] Step 3:

[0359] The server evaluates the reliability of the data using a consistency rate algorithm. It generates a reliability score by comparing data from multiple sources and calculating consistency. The input is an extracted list of topics and keywords, and the output is the corresponding reliability score.

[0360] Step 4:

[0361] The server uses an emotion engine to recognize the user's emotional state. It analyzes the user's input text and past behavioral data and assigns an emotion label (e.g., positive, negative, neutral). The input is user text data or behavioral data, and the output is an emotion label.

[0362] Step 5:

[0363] The server adjusts the visualization of information based on the user's emotions. It dynamically changes the design and color scheme of graphs and charts using the results of the emotion engine. Inputs are confidence scores and emotion labels, and the output is an optimized graph or chart.

[0364] Step 6:

[0365] The terminal presents the user with optimized information. Through the user interface, it displays optimized graphs and charts and provides direct feedback. The input in this step is the optimized graphs and charts, and the output is the information provided to the user.

[0366] Step 7:

[0367] The server proposes business strategies based on the user's emotional state. Using a generative AI model based on data and emotional labels, it develops specific action plans, converts them into prompt messages, and sends them as notifications. Inputs are emotional labels and analyzed data, and output is a customized business proposal.

[0368] (Application Example 2)

[0369] 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."

[0370] There is a growing need for highly personalized information presentation based on information reliability assessment and sentiment analysis. In particular, conventional systems lack the ability to visualize information and propose business strategies that take into account the user's emotional state, which makes it difficult to make optimal decisions that meet the user's needs.

[0371] 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.

[0372] In this invention, the server includes an autonomous data acquisition means capable of collecting information, a means for analyzing the acquired information using a natural language processing engine, a means for evaluating the reliability of the information based on the analysis results, a means for dynamically visualizing and displaying the evaluated information according to the user's emotional state, and a means for proposing a business strategy based on the user's set conditions and emotional state. This makes it possible to provide information and business strategies optimized for the user's emotions.

[0373] "Autonomous data acquisition means capable of information gathering" refers to automated functions that can efficiently collect information from the internet and databases.

[0374] A "natural language processing engine" includes software technologies that analyze acquired text information and understand its syntax and meaning.

[0375] "Means for evaluating reliability" refers to algorithms or processes for numerically or qualitatively evaluating the accuracy and reliability of collected information.

[0376] "Means of dynamically visualizing and displaying information according to emotional state" refers to a function that adjusts the format and design of the displayed information in real time based on the user's emotional recognition.

[0377] "Means of proposing business strategies" refers to a function that provides optimal business responses and action plans according to the user's emotional state and set conditions.

[0378] In an embodiment for implementing the present invention, the system consists of three basic components: a server, a terminal, and a user. The server plays a central role in collecting, analyzing, evaluating the reliability of, and recognizing sentiments from a broad information space, and ultimately proposing business strategies. Specifically, the server uses a web crawler as an autonomous data acquisition means to collect information from the internet and databases based on specified keywords and topics. The collected information is analyzed in detail using a natural language processing engine (e.g., SpaCy or NLTK). This analysis makes it possible to understand the syntax and meaning of the information and extract topics and keywords of interest.

[0379] In the next stage of analysis, the server performs a reliability assessment. It verifies the consistency of data from multiple sources using a consistency algorithm and calculates a reliability score. The server also integrates an emotion engine (e.g., Google Cloud Natural Language API) to analyze user input and past behavioral data to determine the user's emotional state. This allows the server to dynamically adjust the presentation of information, delivering it in a way that suits the user's emotions. For example, if the server determines the user is seeking relaxation, it will display a screen with calming colors.

[0380] The device visualizes and presents these analysis results to the user in graph and chart format via a user interface. From the dashboard on the device, the user can review the received information and receive data in a way that aligns with their emotions. This allows the user to receive customized business strategies and make decisions optimized for their emotional state.

[0381] To give a concrete example, if a user is considering purchasing a new interior item, this system will recognize through sentiment analysis that the user is feeling anxious about the purchase. In this case, the system will provide advice in a reassuring tone and present the best options. An example of a prompt based on the generative AI model would be, "What emotions is this user experiencing while shopping at a furniture store? Please tell me how to recommend products that match those emotions."

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

[0383] Step 1:

[0384] The server uses a web crawler based on specified keywords and topics to collect information from the internet and databases. In this process, the server obtains a large amount of text data. The input is the search query and related internet sources, and the output is raw text data.

[0385] Step 2:

[0386] The server performs syntactic analysis on the acquired text data using a natural language processing engine (e.g., SpaCy or NLTK). The input is raw text data, and the output is the analyzed syntactic information and a set of important keywords. Based on these analysis results, the server performs data processing to extract important topics.

[0387] Step 3:

[0388] The server evaluates the reliability of the information using a reliability evaluation algorithm based on the analysis results. The input is the analyzed syntactic information and a list of information sources, and the output is a reliability score. The server calculates the agreement rate and performs data calculations to integrate the reliability of various information sources.

[0389] Step 4:

[0390] The server integrates an emotion engine (e.g., Google Cloud Natural Language API) to analyze the user's emotional state from their input data and past behavior history. The input is the user's input data and behavior history, and the output is the user's emotional state. Through emotion analysis, the server identifies the user's current emotion.

[0391] Step 5:

[0392] The terminal designs appropriate graphs and charts based on the analysis results and emotional state received from the server, and displays visualization information with dynamically adjusted colors. The input is the analysis results and emotional state, and the output is the visualization information presented to the user. The terminal dynamically changes based on the user and generates optimized visual information.

[0393] Step 6:

[0394] The server proposes a business strategy based on the analyzed emotional state. This proposal is an action plan customized to the user's emotional state. The input is the user's emotional state and settings, and the output is the proposed business strategy. The server integrates the information and builds an emotion-based strategy.

[0395] Step 7:

[0396] Users receive visualized information and suggestions through the device's dashboard and use them to inform their decision-making. The input is the presented visualized information and strategic suggestions, while the output is the action chosen by the user. Users can evaluate the information and make the choice that best suits them.

[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] The system for implementing this invention mainly consists of three components: a server, a terminal, and a user. The server plays a central role in information gathering, analysis, reliability evaluation, and business strategy proposals.

[0414] First, the server has the means to autonomously collect information from the internet. Through this means, the server crawls digital information sources such as websites, news feeds, and social media, and retrieves relevant information based on pre-configured keywords and areas of interest. Furthermore, the server can also collaborate with information sources such as databases from specific industry-specific organizations to acquire new data.

[0415] Next, the server analyzes the acquired information using a natural language processing engine. Specifically, the server tokenizes the information, analyzes its text structure, and extracts the topics and importance associated with each piece of information. This allows the server to determine not only the relevance of the information but also its value and importance.

[0416] Furthermore, the server evaluates the reliability of the analyzed information based on the agreement rate and the presence or absence of primary sources. Based on this evaluation, highly reliable information is selected. The server has components for visualizing the accumulated information, and presents the information visually as graphs and charts on the dashboard.

[0417] The terminal plays the role of displaying information through the user interface. Through this terminal, users can receive information delivered from the server and easily view the visualized data.

[0418] Ultimately, the server proposes a customized business strategy based on the user's settings. This strategic proposal is generated from reliable collected information and is provided as a concrete and actionable plan tailored to the user's needs. The server also periodically notifies the user of the analysis results, ensuring that they receive this information in a timely manner.

[0419] For example, if a small or medium-sized manufacturer is considering entering the market with a new technology, this system can be used to quickly obtain reliable information on the latest technological trends in the industry, the actions of competitors, and changes in regulations. As a result, strategic decision-making based on that information becomes possible.

[0420] The following describes the processing flow.

[0421] Step 1:

[0422] The server autonomously collects information from the internet and affiliated databases using pre-configured keywords and plugins. The server also launches a web crawler to visit specified URLs and news feeds and extract relevant data.

[0423] Step 2:

[0424] The server analyzes the collected information using a natural language processing engine. Specifically, it tokenizes the text and performs morphological analysis to identify keywords and topics within the text. In this process, it classifies the relevance and themes of the information.

[0425] Step 3:

[0426] The server evaluates the reliability of the information based on the analysis results. The server uses a matching algorithm to verify whether the same information is provided by multiple reliable sources. It calculates a reliability score and determines the truthfulness of the information.

[0427] Step 4:

[0428] The server transforms data into graphs and charts to visualize information whose reliability has been assessed. The server uses a visualization engine to visually format the information in formats such as bar graphs and pie charts.

[0429] Step 5:

[0430] The terminal displays visualization data received from the server to the user. The terminal provides real-time visual information through a web-based dashboard, making it accessible to the user.

[0431] Step 6:

[0432] The server proposes specific business strategies based on the user's settings. The server combines existing analytical data with the user's past decision-making history to generate a recommended action plan, which is then sent to the terminal.

[0433] Step 7:

[0434] Users make business decisions based on the information and suggestions displayed on their devices. They consider strategic proposals and make judgments to take appropriate action.

[0435] (Example 1)

[0436] 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."

[0437] In today's information society, it is crucial to quickly and accurately collect and analyze useful information from diverse sources. However, evaluating the reliability of information and formulating actionable strategies through the visualization of complex data is not easy. In particular, it is difficult to determine the degree of reliability of each piece of information and to present it in a visually understandable manner. The present invention aims to provide a system that enables the consistent streamlining of the process from information collection to reliability evaluation, visualization, and strategy proposal.

[0438] 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.

[0439] In this invention, the server includes an autonomous data acquisition means for collecting information, a natural language processing means for processing the acquired information, and a means for determining the reliability of the information based on the processing results. This makes it possible to quickly collect useful information, evaluate its reliability, visualize it, and provide it to the user in an easily understandable format.

[0440] An "autonomous data acquisition means for collecting information" is a function that automatically searches for and acquires relevant information from multiple information sources existing on a network.

[0441] "Natural language processing methods" are technologies that linguistically analyze acquired information and grasp its meaning and structure from text.

[0442] "Means for determining the reliability of information" refers to a system for evaluating the accuracy and validity of collected information and selecting information with a high degree of reliability.

[0443] "Visualization display means" refers to a function that represents data as shapes or charts in order to present the analyzed information in an easy-to-understand manner for the user.

[0444] "Means of notifying users of analysis results" refers to a process that provides users with timely information on the analysis and evaluation results from the server to support their decision-making.

[0445] "Means of proposing business plans" refers to methods for presenting optimal action plans and strategies based on reliable information, tailored to the individual user's settings and needs.

[0446] The system of this invention consists of three components: a server, a terminal, and a user. The server primarily handles information collection, analysis, reliability evaluation, and business strategy proposals. Specifically, the server autonomously obtains target data from internet information sources by performing web scraping using Python or other programming languages. Furthermore, a natural language processing engine, such as TensorFlow or NLTK, analyzes the collected information. In this analysis process, the server tokenizes the information, grasps its grammatical structure, and extracts useful topics and key points.

[0447] To evaluate the reliability of information generated by the server, statistical algorithms are used to examine the accuracy of the information sources. Based on methods such as PageRank, the reliability of each piece of information is scored, and information with high reliability is prioritized.

[0448] The device provides this information as a user interface. Specifically, the visualized data is converted into graphs and charts using Matplotlib and D3.js, and displayed in a format that is easy for the user to understand. Through this interface, users can instantly access reliable information and use it for daily decision-making.

[0449] The server ultimately presents a customized business strategy based on the user's settings and collected information. This process generates specific and actionable strategic proposals tailored to the user's particular needs and conditions. These strategic proposals can be used to inform management decisions and market entry planning.

[0450] For example, when a manufacturer plans to enter the market with a new technology, the server can quickly provide information on industry technological trends, competitor activities, and regulations, helping the company formulate an appropriate business strategy based on that information.

[0451] Example of a prompt:

[0452] "Please suggest ways to use AI to track real-time technological trends."

[0453] "Could you recommend an AI tool to streamline strategic proposals based on market analysis?"

[0454] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0455] Step 1:

[0456] The server collects information. It is given pre-configured keywords and topics as input. Based on this, the server automatically retrieves data from internet sources using web scraping tools and APIs. During this process, it collects real-time information from multiple databases and news sites via EDI (Electronic Data Interchange) and web APIs. The collected raw data is stored in a database as output.

[0457] Step 2:

[0458] The server analyzes the collected information using a natural language processing engine. The input is the raw data collected in step 1. The server tokenizes this data using a natural language processing tool (e.g., NLTK or spaCy) and analyzes its grammatical structure. Specifically, the text data is divided into tokens and tagged with parts of speech. This analysis extracts the main points and related topics of the information, and structured data is generated as output.

[0459] Step 3:

[0460] The server evaluates the reliability of the analyzed information. The input is the analysis results from step 2. The server verifies the reliability of the information sources using an evaluation algorithm. In this process, mathematical methods and machine learning models are used to calculate the agreement rate and the reliability score of the information sources. As output, filtered information with evaluated reliability is provided.

[0461] Step 4:

[0462] The server prepares to visualize the evaluated information. The input is the reliability-evaluated data from step 3. The server uses visualization tools (Matplotlib or D3.js) to convert this data into graphs and charts. Specifically, the data is converted into a diagrammatic format, and visual content is created using an easy-to-read layout. As output, the visualized data is prepared for the dashboard.

[0463] Step 5:

[0464] The terminal presents visualized information to the user. The input is the visualized data from step 4. The terminal displays this data in a user interface, making it easy for the user to understand the information. Specifically, through an interactive dashboard, the user can filter, compare, and visually analyze each piece of information. As output, the information presented to the user provides a foundation to support decision-making.

[0465] Step 6:

[0466] The server proposes customized business strategies based on user settings. Input consists of reliable information and user-defined conditions. Based on this, the server uses a generative AI model to create strategic proposals for decision-making support. This model combines collected data with user needs to construct an optimal action plan. The output provides the user with a concrete and actionable strategy.

[0467] (Application Example 1)

[0468] 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."

[0469] In today's world, the sheer volume of information makes it difficult to efficiently collect, evaluate the reliability of, and accurately present the information that users with specific interests need. Furthermore, systems that provide information optimized for individual users in real time are still insufficient. As a result, users struggle to obtain the information necessary for strategic decision-making in a timely and reliable manner.

[0470] 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.

[0471] In this invention, the server includes an autonomous data acquisition means capable of collecting information, a means for analyzing the acquired information using a natural language processing engine, a means for evaluating the reliability of the information based on the analysis results, a means for visualizing and displaying the evaluated information, a means for notifying system users of the analysis results, a means for proposing business strategies based on user-defined conditions, and an application means for personalized delivery of information based on user-specified interests. As a result, users can efficiently acquire the latest and most reliable information tailored to their interests and needs, enabling rapid decision-making.

[0472] An "autonomous data acquisition means" is a device that has the function of automatically collecting information from digital information sources such as websites and social media on the internet.

[0473] A "natural language processing engine" is software that tokenizes acquired information, analyzes its text structure, and extracts topics and importance.

[0474] "Means for evaluating the reliability of information" refers to devices or software that have the function of quantifying the accuracy and reliability of collected information based on the information's consistency rate and the presence or absence of primary sources.

[0475] "Means for visualizing and displaying information" refers to devices or software that display analyzed information in the form of graphs or charts, making it easy for users to understand.

[0476] "Means for notifying analysis results" refers to devices or software that have the function of informing system users of the results of the collected and analyzed information in real time or periodically.

[0477] A "means for proposing business strategies" refers to a device or software that has the function of formulating individual strategies based on reliable information collected according to the user's set conditions and presenting them to the user.

[0478] "Personalized information delivery methods" refer to devices or software that have the function of selecting content based on the user's specified interests and topics and providing it in an individually optimized format.

[0479] The system implementing this invention mainly consists of three components: a server, a terminal, and a user. The server autonomously collects data from various digital information sources on the internet using web crawling technologies such as BeautifulSoup and Scrapy for information gathering. The collected data is tokenized, text-analyzed, and topic-extracted using natural language processing libraries such as NLTK and spaCy, and the relevance and importance of the information are evaluated. Next, the reliability of the information is quantified based on the agreement rate of the collected data and the presence or absence of primary information, and highly reliable information is selected.

[0480] The server visualizes this data as graphs and charts using Matplotlib and Plotly, and provides it to the user in a dashboard format. Based on the analysis results, it also generates and notifies the user of business strategy suggestions that take into account the user's specified conditions. The device, through a mobile application built with React Native, provides the user with an intuitive interface and displays the visualized information and strategic suggestions.

[0481] For example, if a user expresses interest in "environmental issues," this system collects and analyzes the latest news and topics related to the environment, providing the user with reliable information in a personalized format. This allows the user to efficiently make strategic decisions regarding their chosen area of ​​interest.

[0482] Examples of prompts for the generated AI model include, "Please tell me about the latest industry trends regarding environmental issues," and "Please explain the major environmental technology innovations this week." In this way, through the collection and analysis process, users can obtain the necessary insights in real time.

[0483] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0484] Step 1:

[0485] The server collects data from internet sources. It takes a list of website and social media URLs as input and crawls them using tools like BeautifulSoup and Scrapy. As output, it retrieves raw HTML data and JSON data obtained from APIs. This allows for the collection of relevant information based on specific keywords.

[0486] Step 2:

[0487] The server analyzes the collected data using a natural language processing engine. The input is the raw data obtained in step 1. The data is tokenized using NLTK or spaCy, and part-of-speech tagging and entity recognition are performed. The output provides structural information and topics of the analyzed text data.

[0488] Step 3:

[0489] The server evaluates the reliability of the information based on the analysis results. The input is the text information analyzed in step 2. Reliability is calculated using a scoring method, with the agreement rate and the presence or absence of primary information used as evaluation criteria. A set of highly reliable information is generated as output.

[0490] Step 4:

[0491] The server visualizes reliable information and displays it on a dashboard. The input is the information evaluated in step 3. It is converted into graphs and charts using Matplotlib or Plotly. The output provides a user-friendly visual presentation.

[0492] Step 5:

[0493] The server notifies the user of the analysis results. The input is the data visualized in step 4. Updates are sent to the user periodically using email or push notifications. As output, the information becomes immediately accessible on the user's device.

[0494] Step 6:

[0495] The server proposes business strategies based on the user's settings. The input consists of the information filtered in step 3 and the conditions set by the user. Using an AI model, it generates customized strategic advice. The output provides a strategic plan tailored to the user's specific needs.

[0496] Step 7:

[0497] The device delivers personalized information based on the user's specified interests. The inputs are the business strategy generated in step 6 and the information visualized in step 4. React Native is used to display the information on an intuitive UI. The output provides the user with individually optimized information.

[0498] 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.

[0499] In one embodiment of this invention, the system consists of three components: a server, a terminal, and a user. The server plays a central role in processing functions such as information gathering, analysis, reliability evaluation, sentiment recognition, and business strategy proposals in a unified manner.

[0500] First, the server has an information gathering function, using a web crawler to collect information from the internet and databases based on specified keywords and topics. The collected information is then analyzed by a natural language processing engine. Specifically, the server performs syntactic analysis of the information and efficiently extracts topics and keywords of interest.

[0501] Next, the server evaluates the reliability of the information based on the analysis results. Here, it uses a consistency algorithm to check the consistency of data from multiple sources and calculates a reliability score. Furthermore, this system integrates an emotion engine to recognize the user's emotions. The server analyzes the user's input and past behavioral data to determine the user's emotional state.

[0502] The server dynamically adjusts the presentation of information based on the user's emotions. Therefore, during the visualization phase of the analysis results, the user's emotional state, as recognized by the emotion engine, is taken into consideration, and the design and colors of graphs and charts are adjusted accordingly.

[0503] The device displays visualized information and suggestions to the user through a user interface. Users can review the information displayed through the dashboard on the device and receive data presented in a way that suits their emotions.

[0504] Ultimately, the server adjusts the proposed business strategy based on the analysis results of the emotion engine. This strategic proposal is optimized for the user's emotional state and sent as a customized, specific action plan.

[0505] For example, if a user is feeling anxious about the progress of a new project, this system will provide information in a supportive tone and offer positive strategic suggestions. This allows the user to make optimal business decisions while also satisfying their emotional needs.

[0506] The following describes the processing flow.

[0507] Step 1:

[0508] The server activates a web crawler based on pre-configured keywords and topics. The server automatically visits specific websites and news feeds, collecting relevant data and information.

[0509] Step 2:

[0510] The server analyzes the collected information using a natural language processing engine. This analysis involves text tokenization and morphological analysis to extract interesting topics and key modules. The server then converts the obtained data into structured data and stores it in a database.

[0511] Step 3:

[0512] The server applies a concordance algorithm to evaluate the reliability of the information using the analysis results. The server calculates the degree of concordance of information obtained from multiple reliable data sources and derives a reliability score.

[0513] Step 4:

[0514] The server recognizes the user's emotional state using an emotion engine. The server analyzes user input and past behavioral data to identify emotional patterns. Based on this, the server determines the user's current emotional state.

[0515] Step 5:

[0516] The server adjusts the information visualization format based on the emotional state obtained. Using a visualization engine, it changes the color and design of graphs and charts to match the user's emotions, optimizing the visual presentation.

[0517] Step 6:

[0518] The terminal displays visualization data and coordinated information sent from the server on a user interface. The terminal provides a user-friendly and intuitive dashboard, making it easy to understand the information.

[0519] Step 7:

[0520] The server generates business strategy proposals based on analysis by the emotion engine. The server adjusts the proposals considering the user's emotions, and builds a strategy that is specific and instills confidence.

[0521] Step 8:

[0522] Users make necessary business decisions based on the information and strategic suggestions displayed on their devices. They formulate action plans, taking into account the presented data and their own emotional state.

[0523] (Example 2)

[0524] 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."

[0525] In today's world, vast amounts of data exist on the internet, making it difficult to efficiently collect and analyze important information. Furthermore, evaluating the reliability of information is challenging, and there is a need to provide data to users in an appropriate format. Additionally, there is a demand for developing and presenting business strategies that take into account users' emotional states, but existing technologies are unable to adequately meet this requirement, posing a significant challenge.

[0526] 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.

[0527] In this invention, the server includes means for autonomously acquiring data, means for analyzing the acquired data using natural language processing technology, and means for evaluating the reliability of the data based on the analysis results. This enables the efficient collection and analysis of highly reliable information from vast amounts of data. Furthermore, by integrating an emotion engine and adjusting data visualization according to the user's emotions, data can be provided in a form suitable for the user. In addition, it becomes possible to propose business strategies based on recognized emotions, allowing users to make optimal decisions according to their emotions.

[0528] "Means of autonomous data acquisition" refers to technologies that automatically collect necessary information from the internet or databases based on specific keywords or topics.

[0529] "Natural language processing technology" refers to computer programs that enable machines to understand, analyze, and process human language appropriately.

[0530] "Methods for evaluating data reliability" refer to methods for quantifying the reliability of collected information and determining the consistency and trustworthiness of that information.

[0531] An "emotion engine" is a system that analyzes user input and past behavioral data to recognize and judge the user's emotions.

[0532] "Means of adjusting data visualization" refers to methods for optimizing the display format of information based on analysis results and the emotional state of users, making it visually easier to understand.

[0533] "Means of proposing business strategies" refers to methods of presenting appropriate action plans and strategies to users based on analyzed data and recognized emotional states.

[0534] The system of the present invention consists of three main components: a server, a terminal, and a user. The server is responsible for sequentially executing the following functions: information gathering, natural language processing, data reliability evaluation, sentiment recognition, and business strategy proposal.

[0535] First, the server autonomously collects data. Using technologies such as web crawlers, it gathers information from websites and data stores based on specified keywords and topics. This ensures that relevant information is retrieved efficiently.

[0536] Next, the server applies natural language processing techniques to the collected data. Specifically, it uses a natural language processing engine to parse the data and extract important topics and keywords. This step involves tokenizing and tagging the data by part of speech.

[0537] Next, the server evaluates the reliability of the data. Using a concordance algorithm, it calculates the consistency of data obtained from multiple sources and generates a reliability score. This process allows users to obtain information about the reliability of the data.

[0538] Furthermore, the server uses an emotion engine to recognize the user's emotions. By analyzing the user's input and past behavioral data, the user's emotional state is determined. This allows information to be provided in a way that is more appropriate to the user.

[0539] The device presents the user with adjusted information through its user interface. Here, the design and color scheme of graphs and charts are dynamically adjusted based on the results of the emotion engine. For example, if the user is feeling stressed, the data will be displayed with a calmer design.

[0540] Ultimately, the server proposes a business strategy tailored to the user's emotions. This strategy is then communicated to the user via their device as a customized, specific action plan. For example, if a project manager is seeking new measures to mitigate risks, the server uses a generative AI model to propose a positive and effective strategy.

[0541] As part of this process, an example of a prompt for the generated AI model is, "Please propose risk assessments and countermeasures for the progress of a new project from a positive perspective." This system helps users make emotionally driven decisions and improves work efficiency.

[0542] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0543] Step 1:

[0544] The server collects information using a web crawler. The crawler automatically retrieves data from the internet and data stores based on specified keywords or topics. The input to this process is keywords or topics, and the output is a collection of related web pages and article data.

[0545] Step 2:

[0546] The server analyzes the collected data using a natural language processing engine. Specifically, it tokenizes the data and extracts topics and keywords through part-of-speech tagging and syntactic analysis. The input for this step is web page or article data, and the output is a list of extracted topics and keywords.

[0547] Step 3:

[0548] The server evaluates the reliability of the data using a consistency rate algorithm. It generates a reliability score by comparing data from multiple sources and calculating consistency. The input is an extracted list of topics and keywords, and the output is the corresponding reliability score.

[0549] Step 4:

[0550] The server uses an emotion engine to recognize the user's emotional state. It analyzes the user's input text and past behavioral data and assigns an emotion label (e.g., positive, negative, neutral). The input is user text data or behavioral data, and the output is an emotion label.

[0551] Step 5:

[0552] The server adjusts the visualization of information based on the user's emotions. It dynamically changes the design and color scheme of graphs and charts using the results of the emotion engine. Inputs are confidence scores and emotion labels, and the output is an optimized graph or chart.

[0553] Step 6:

[0554] The terminal presents the user with optimized information. Through the user interface, it displays optimized graphs and charts and provides direct feedback. The input in this step is the optimized graphs and charts, and the output is the information provided to the user.

[0555] Step 7:

[0556] The server proposes business strategies based on the user's emotional state. Using a generative AI model based on data and emotional labels, it develops specific action plans, converts them into prompt messages, and sends them as notifications. Inputs are emotional labels and analyzed data, and output is a customized business proposal.

[0557] (Application Example 2)

[0558] 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."

[0559] There is a growing need for highly personalized information presentation based on information reliability assessment and sentiment analysis. In particular, conventional systems lack the ability to visualize information and propose business strategies that take into account the user's emotional state, which makes it difficult to make optimal decisions that meet the user's needs.

[0560] 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.

[0561] In this invention, the server includes an autonomous data acquisition means capable of collecting information, a means for analyzing the acquired information using a natural language processing engine, a means for evaluating the reliability of the information based on the analysis results, a means for dynamically visualizing and displaying the evaluated information according to the user's emotional state, and a means for proposing a business strategy based on the user's set conditions and emotional state. This makes it possible to provide information and business strategies optimized for the user's emotions.

[0562] "Autonomous data acquisition means capable of information gathering" refers to automated functions that can efficiently collect information from the internet and databases.

[0563] A "natural language processing engine" includes software technologies that analyze acquired text information and understand its syntax and meaning.

[0564] "Means for evaluating reliability" refers to algorithms or processes for numerically or qualitatively evaluating the accuracy and reliability of collected information.

[0565] "Means of dynamically visualizing and displaying information according to emotional state" refers to a function that adjusts the format and design of the displayed information in real time based on the user's emotional recognition.

[0566] "Means of proposing business strategies" refers to a function that provides optimal business responses and action plans according to the user's emotional state and set conditions.

[0567] In an embodiment for implementing the present invention, the system consists of three basic components: a server, a terminal, and a user. The server plays a central role in collecting, analyzing, evaluating the reliability of, and recognizing sentiments from a broad information space, and ultimately proposing business strategies. Specifically, the server uses a web crawler as an autonomous data acquisition means to collect information from the internet and databases based on specified keywords and topics. The collected information is analyzed in detail using a natural language processing engine (e.g., SpaCy or NLTK). This analysis makes it possible to understand the syntax and meaning of the information and extract topics and keywords of interest.

[0568] In the next stage of analysis, the server performs a reliability assessment. It verifies the consistency of data from multiple sources using a consistency algorithm and calculates a reliability score. The server also integrates an emotion engine (e.g., Google Cloud Natural Language API) to analyze user input and past behavioral data to determine the user's emotional state. This allows the server to dynamically adjust the presentation of information, delivering it in a way that suits the user's emotions. For example, if the server determines the user is seeking relaxation, it will display a screen with calming colors.

[0569] The device visualizes and presents these analysis results to the user in graph and chart format via a user interface. From the dashboard on the device, the user can review the received information and receive data in a way that aligns with their emotions. This allows the user to receive customized business strategies and make decisions optimized for their emotional state.

[0570] To give a concrete example, if a user is considering purchasing a new interior item, this system will recognize through sentiment analysis that the user is feeling anxious about the purchase. In this case, the system will provide advice in a reassuring tone and present the best options. An example of a prompt based on the generative AI model would be, "What emotions is this user experiencing while shopping at a furniture store? Please tell me how to recommend products that match those emotions."

[0571] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0572] Step 1:

[0573] The server uses a web crawler based on specified keywords and topics to collect information from the internet and databases. In this process, the server obtains a large amount of text data. The input is the search query and related internet sources, and the output is raw text data.

[0574] Step 2:

[0575] The server performs syntactic analysis on the acquired text data using a natural language processing engine (e.g., SpaCy or NLTK). The input is raw text data, and the output is the analyzed syntactic information and a set of important keywords. Based on these analysis results, the server performs data processing to extract important topics.

[0576] Step 3:

[0577] The server evaluates the reliability of the information using a reliability evaluation algorithm based on the analysis results. The input is the analyzed syntactic information and a list of information sources, and the output is a reliability score. The server calculates the agreement rate and performs data calculations to integrate the reliability of various information sources.

[0578] Step 4:

[0579] The server integrates an emotion engine (e.g., Google Cloud Natural Language API) to analyze the user's emotional state from their input data and past behavior history. The input is the user's input data and behavior history, and the output is the user's emotional state. Through emotion analysis, the server identifies the user's current emotion.

[0580] Step 5:

[0581] The terminal designs appropriate graphs and charts based on the analysis results and emotional state received from the server, and displays visualization information with dynamically adjusted colors. The input is the analysis results and emotional state, and the output is the visualization information presented to the user. The terminal dynamically changes based on the user and generates optimized visual information.

[0582] Step 6:

[0583] The server proposes a business strategy based on the analyzed emotional state. This proposal is an action plan customized to the user's emotional state. The input is the user's emotional state and settings, and the output is the proposed business strategy. The server integrates the information and builds an emotion-based strategy.

[0584] Step 7:

[0585] Users receive visualized information and suggestions through the device's dashboard and use them to inform their decision-making. The input is the presented visualized information and strategic suggestions, while the output is the action chosen by the user. Users can evaluate the information and make the choice that best suits them.

[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] The system for implementing this invention mainly consists of three components: a server, a terminal, and a user. The server plays a central role in information gathering, analysis, reliability evaluation, and business strategy proposals.

[0604] First, the server has the means to autonomously collect information from the internet. Through this means, the server crawls digital information sources such as websites, news feeds, and social media, and retrieves relevant information based on pre-configured keywords and areas of interest. Furthermore, the server can also collaborate with information sources such as databases from specific industry-specific organizations to acquire new data.

[0605] Next, the server analyzes the acquired information using a natural language processing engine. Specifically, the server tokenizes the information, analyzes its text structure, and extracts the topics and importance associated with each piece of information. This allows the server to determine not only the relevance of the information but also its value and importance.

[0606] Furthermore, the server evaluates the reliability of the analyzed information based on the agreement rate and the presence or absence of primary sources. Based on this evaluation, highly reliable information is selected. The server has components for visualizing the accumulated information, and presents the information visually as graphs and charts on the dashboard.

[0607] The terminal plays the role of displaying information through the user interface. Through this terminal, users can receive information delivered from the server and easily view the visualized data.

[0608] Ultimately, the server proposes a customized business strategy based on the user's settings. This strategic proposal is generated from reliable collected information and is provided as a concrete and actionable plan tailored to the user's needs. The server also periodically notifies the user of the analysis results, ensuring that they receive this information in a timely manner.

[0609] For example, if a small or medium-sized manufacturer is considering entering the market with a new technology, this system can be used to quickly obtain reliable information on the latest technological trends in the industry, the actions of competitors, and changes in regulations. As a result, strategic decision-making based on that information becomes possible.

[0610] The following describes the processing flow.

[0611] Step 1:

[0612] The server autonomously collects information from the internet and affiliated databases using pre-configured keywords and plugins. The server also launches a web crawler to visit specified URLs and news feeds and extract relevant data.

[0613] Step 2:

[0614] The server analyzes the collected information using a natural language processing engine. Specifically, it tokenizes the text and performs morphological analysis to identify keywords and topics within the text. In this process, it classifies the relevance and themes of the information.

[0615] Step 3:

[0616] The server evaluates the reliability of the information based on the analysis results. The server uses a matching algorithm to verify whether the same information is provided by multiple reliable sources. It calculates a reliability score and determines the truthfulness of the information.

[0617] Step 4:

[0618] The server transforms data into graphs and charts to visualize information whose reliability has been assessed. The server uses a visualization engine to visually format the information in formats such as bar graphs and pie charts.

[0619] Step 5:

[0620] The terminal displays visualization data received from the server to the user. The terminal provides real-time visual information through a web-based dashboard, making it accessible to the user.

[0621] Step 6:

[0622] The server proposes specific business strategies based on the user's settings. The server combines existing analytical data with the user's past decision-making history to generate a recommended action plan, which is then sent to the terminal.

[0623] Step 7:

[0624] Users make business decisions based on the information and suggestions displayed on their devices. They consider strategic proposals and make judgments to take appropriate action.

[0625] (Example 1)

[0626] 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".

[0627] In today's information society, it is crucial to quickly and accurately collect and analyze useful information from diverse sources. However, evaluating the reliability of information and formulating actionable strategies through the visualization of complex data is not easy. In particular, it is difficult to determine the degree of reliability of each piece of information and to present it in a visually understandable manner. The present invention aims to provide a system that enables the consistent streamlining of the process from information collection to reliability evaluation, visualization, and strategy proposal.

[0628] 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.

[0629] In this invention, the server includes an autonomous data acquisition means for collecting information, a natural language processing means for processing the acquired information, and a means for determining the reliability of the information based on the processing results. This makes it possible to quickly collect useful information, evaluate its reliability, visualize it, and provide it to the user in an easily understandable format.

[0630] An "autonomous data acquisition means for collecting information" is a function that automatically searches for and acquires relevant information from multiple information sources existing on a network.

[0631] "Natural language processing methods" are technologies that linguistically analyze acquired information and grasp its meaning and structure from text.

[0632] "Means for determining the reliability of information" refers to a system for evaluating the accuracy and validity of collected information and selecting information with a high degree of reliability.

[0633] "Visualization display means" refers to a function that represents data as shapes or charts in order to present the analyzed information in an easy-to-understand manner for the user.

[0634] "Means of notifying users of analysis results" refers to a process that provides users with timely information on the analysis and evaluation results from the server to support their decision-making.

[0635] "Means of proposing business plans" refers to methods for presenting optimal action plans and strategies based on reliable information, tailored to the individual user's settings and needs.

[0636] The system of this invention consists of three components: a server, a terminal, and a user. The server primarily handles information collection, analysis, reliability evaluation, and business strategy proposals. Specifically, the server autonomously obtains target data from internet information sources by performing web scraping using Python or other programming languages. Furthermore, a natural language processing engine, such as TensorFlow or NLTK, analyzes the collected information. In this analysis process, the server tokenizes the information, grasps its grammatical structure, and extracts useful topics and key points.

[0637] To evaluate the reliability of information generated by the server, statistical algorithms are used to examine the accuracy of the information sources. Based on methods such as PageRank, the reliability of each piece of information is scored, and information with high reliability is prioritized.

[0638] The device provides this information as a user interface. Specifically, the visualized data is converted into graphs and charts using Matplotlib and D3.js, and displayed in a format that is easy for the user to understand. Through this interface, users can instantly access reliable information and use it for daily decision-making.

[0639] The server ultimately presents a customized business strategy based on the user's settings and collected information. This process generates specific and actionable strategic proposals tailored to the user's particular needs and conditions. These strategic proposals can be used to inform management decisions and market entry planning.

[0640] For example, when a manufacturer plans to enter the market with a new technology, the server can quickly provide information on industry technological trends, competitor activities, and regulations, helping the company formulate an appropriate business strategy based on that information.

[0641] Example of a prompt:

[0642] "Please suggest ways to use AI to track real-time technological trends."

[0643] "Could you recommend an AI tool to streamline strategic proposals based on market analysis?"

[0644] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0645] Step 1:

[0646] The server collects information. It is given pre-configured keywords and topics as input. Based on this, the server automatically retrieves data from internet sources using web scraping tools and APIs. During this process, it collects real-time information from multiple databases and news sites via EDI (Electronic Data Interchange) and web APIs. The collected raw data is stored in a database as output.

[0647] Step 2:

[0648] The server analyzes the collected information using a natural language processing engine. The input is the raw data collected in step 1. The server tokenizes this data using a natural language processing tool (e.g., NLTK or spaCy) and analyzes its grammatical structure. Specifically, the text data is divided into tokens and tagged with parts of speech. This analysis extracts the main points and related topics of the information, and structured data is generated as output.

[0649] Step 3:

[0650] The server evaluates the reliability of the analyzed information. The input is the analysis results from step 2. The server verifies the reliability of the information sources using an evaluation algorithm. In this process, mathematical methods and machine learning models are used to calculate the agreement rate and the reliability score of the information sources. As output, filtered information with evaluated reliability is provided.

[0651] Step 4:

[0652] The server prepares to visualize the evaluated information. The input is the reliability-evaluated data from step 3. The server uses visualization tools (Matplotlib or D3.js) to convert this data into graphs and charts. Specifically, the data is converted into a diagrammatic format, and visual content is created using an easy-to-read layout. As output, the visualized data is prepared for the dashboard.

[0653] Step 5:

[0654] The terminal presents visualized information to the user. The input is the visualized data from step 4. The terminal displays this data in a user interface, making it easy for the user to understand the information. Specifically, through an interactive dashboard, the user can filter, compare, and visually analyze each piece of information. As output, the information presented to the user provides a foundation to support decision-making.

[0655] Step 6:

[0656] The server proposes customized business strategies based on user settings. Input consists of reliable information and user-defined conditions. Based on this, the server uses a generative AI model to create strategic proposals for decision-making support. This model combines collected data with user needs to construct an optimal action plan. The output provides the user with a concrete and actionable strategy.

[0657] (Application Example 1)

[0658] 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".

[0659] In today's world, the sheer volume of information makes it difficult to efficiently collect, evaluate the reliability of, and accurately present the information that users with specific interests need. Furthermore, systems that provide information optimized for individual users in real time are still insufficient. As a result, users struggle to obtain the information necessary for strategic decision-making in a timely and reliable manner.

[0660] 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.

[0661] In this invention, the server includes an autonomous data acquisition means capable of collecting information, a means for analyzing the acquired information using a natural language processing engine, a means for evaluating the reliability of the information based on the analysis results, a means for visualizing and displaying the evaluated information, a means for notifying system users of the analysis results, a means for proposing business strategies based on user-defined conditions, and an application means for personalized delivery of information based on user-specified interests. As a result, users can efficiently acquire the latest and most reliable information tailored to their interests and needs, enabling rapid decision-making.

[0662] An "autonomous data acquisition means" is a device that has the function of automatically collecting information from digital information sources such as websites and social media on the internet.

[0663] A "natural language processing engine" is software that tokenizes acquired information, analyzes its text structure, and extracts topics and importance.

[0664] "Means for evaluating the reliability of information" refers to devices or software that have the function of quantifying the accuracy and reliability of collected information based on the information's consistency rate and the presence or absence of primary sources.

[0665] "Means for visualizing and displaying information" refers to devices or software that display analyzed information in the form of graphs or charts, making it easy for users to understand.

[0666] "Means for notifying analysis results" refers to devices or software that have the function of informing system users of the results of the collected and analyzed information in real time or periodically.

[0667] A "means for proposing business strategies" refers to a device or software that has the function of formulating individual strategies based on reliable information collected according to the user's set conditions and presenting them to the user.

[0668] "Personalized information delivery methods" refer to devices or software that have the function of selecting content based on the user's specified interests and topics and providing it in an individually optimized format.

[0669] The system implementing this invention mainly consists of three components: a server, a terminal, and a user. The server autonomously collects data from various digital information sources on the internet using web crawling technologies such as BeautifulSoup and Scrapy for information gathering. The collected data is tokenized, text-analyzed, and topic-extracted using natural language processing libraries such as NLTK and spaCy, and the relevance and importance of the information are evaluated. Next, the reliability of the information is quantified based on the agreement rate of the collected data and the presence or absence of primary information, and highly reliable information is selected.

[0670] The server visualizes this data as graphs and charts using Matplotlib and Plotly, and provides it to the user in a dashboard format. Based on the analysis results, it also generates and notifies the user of business strategy suggestions that take into account the user's specified conditions. The device, through a mobile application built with React Native, provides the user with an intuitive interface and displays the visualized information and strategic suggestions.

[0671] For example, if a user expresses interest in "environmental issues," this system collects and analyzes the latest news and topics related to the environment, providing the user with reliable information in a personalized format. This allows the user to efficiently make strategic decisions regarding their chosen area of ​​interest.

[0672] Examples of prompts for the generated AI model include, "Please tell me about the latest industry trends regarding environmental issues," and "Please explain the major environmental technology innovations this week." In this way, through the collection and analysis process, users can obtain the necessary insights in real time.

[0673] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0674] Step 1:

[0675] The server collects data from internet sources. It takes a list of website and social media URLs as input and crawls them using tools like BeautifulSoup and Scrapy. As output, it retrieves raw HTML data and JSON data obtained from APIs. This allows for the collection of relevant information based on specific keywords.

[0676] Step 2:

[0677] The server analyzes the collected data using a natural language processing engine. The input is the raw data obtained in step 1. The data is tokenized using NLTK or spaCy, and part-of-speech tagging and entity recognition are performed. The output provides structural information and topics of the analyzed text data.

[0678] Step 3:

[0679] The server evaluates the reliability of the information based on the analysis results. The input is the text information analyzed in step 2. Reliability is calculated using a scoring method, with the agreement rate and the presence or absence of primary information used as evaluation criteria. A set of highly reliable information is generated as output.

[0680] Step 4:

[0681] The server visualizes reliable information and displays it on a dashboard. The input is the information evaluated in step 3. It is converted into graphs and charts using Matplotlib or Plotly. The output provides a user-friendly visual presentation.

[0682] Step 5:

[0683] The server notifies the user of the analysis results. The input is the data visualized in step 4. Updates are sent to the user periodically using email or push notifications. As output, the information becomes immediately accessible on the user's device.

[0684] Step 6:

[0685] The server proposes business strategies based on the user's settings. The input consists of the information filtered in step 3 and the conditions set by the user. Using an AI model, it generates customized strategic advice. The output provides a strategic plan tailored to the user's specific needs.

[0686] Step 7:

[0687] The device delivers personalized information based on the user's specified interests. The inputs are the business strategy generated in step 6 and the information visualized in step 4. React Native is used to display the information on an intuitive UI. The output provides the user with individually optimized information.

[0688] 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.

[0689] In one embodiment of this invention, the system consists of three components: a server, a terminal, and a user. The server plays a central role in processing functions such as information gathering, analysis, reliability evaluation, sentiment recognition, and business strategy proposals in a unified manner.

[0690] First, the server has an information gathering function, using a web crawler to collect information from the internet and databases based on specified keywords and topics. The collected information is then analyzed by a natural language processing engine. Specifically, the server performs syntactic analysis of the information and efficiently extracts topics and keywords of interest.

[0691] Next, the server evaluates the reliability of the information based on the analysis results. Here, it uses a consistency algorithm to check the consistency of data from multiple sources and calculates a reliability score. Furthermore, this system integrates an emotion engine to recognize the user's emotions. The server analyzes the user's input and past behavioral data to determine the user's emotional state.

[0692] The server dynamically adjusts the presentation of information based on the user's emotions. Therefore, during the visualization phase of the analysis results, the user's emotional state, as recognized by the emotion engine, is taken into consideration, and the design and colors of graphs and charts are adjusted accordingly.

[0693] The device displays visualized information and suggestions to the user through a user interface. Users can review the information displayed through the dashboard on the device and receive data presented in a way that suits their emotions.

[0694] Ultimately, the server adjusts the proposed business strategy based on the analysis results of the emotion engine. This strategic proposal is optimized for the user's emotional state and sent as a customized, specific action plan.

[0695] For example, if a user is feeling anxious about the progress of a new project, this system will provide information in a supportive tone and offer positive strategic suggestions. This allows the user to make optimal business decisions while also satisfying their emotional needs.

[0696] The following describes the processing flow.

[0697] Step 1:

[0698] The server activates a web crawler based on pre-configured keywords and topics. The server automatically visits specific websites and news feeds, collecting relevant data and information.

[0699] Step 2:

[0700] The server analyzes the collected information using a natural language processing engine. This analysis involves text tokenization and morphological analysis to extract interesting topics and key modules. The server then converts the obtained data into structured data and stores it in a database.

[0701] Step 3:

[0702] The server applies a concordance algorithm to evaluate the reliability of the information using the analysis results. The server calculates the degree of concordance of information obtained from multiple reliable data sources and derives a reliability score.

[0703] Step 4:

[0704] The server recognizes the user's emotional state using an emotion engine. The server analyzes user input and past behavioral data to identify emotional patterns. Based on this, the server determines the user's current emotional state.

[0705] Step 5:

[0706] The server adjusts the information visualization format based on the emotional state obtained. Using a visualization engine, it changes the color and design of graphs and charts to match the user's emotions, optimizing the visual presentation.

[0707] Step 6:

[0708] The terminal displays visualization data and coordinated information sent from the server on a user interface. The terminal provides a user-friendly and intuitive dashboard, making it easy to understand the information.

[0709] Step 7:

[0710] The server generates business strategy proposals based on analysis by the emotion engine. The server adjusts the proposals considering the user's emotions, and builds a strategy that is specific and instills confidence.

[0711] Step 8:

[0712] Users make necessary business decisions based on the information and strategic suggestions displayed on their devices. They formulate action plans, taking into account the presented data and their own emotional state.

[0713] (Example 2)

[0714] 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".

[0715] In today's world, vast amounts of data exist on the internet, making it difficult to efficiently collect and analyze important information. Furthermore, evaluating the reliability of information is challenging, and there is a need to provide data to users in an appropriate format. Additionally, there is a demand for developing and presenting business strategies that take into account users' emotional states, but existing technologies are unable to adequately meet this requirement, posing a significant challenge.

[0716] 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.

[0717] In this invention, the server includes means for autonomously acquiring data, means for analyzing the acquired data using natural language processing technology, and means for evaluating the reliability of the data based on the analysis results. This enables the efficient collection and analysis of highly reliable information from vast amounts of data. Furthermore, by integrating an emotion engine and adjusting data visualization according to the user's emotions, data can be provided in a form suitable for the user. In addition, it becomes possible to propose business strategies based on recognized emotions, allowing users to make optimal decisions according to their emotions.

[0718] "Means of autonomous data acquisition" refers to technologies that automatically collect necessary information from the internet or databases based on specific keywords or topics.

[0719] "Natural language processing technology" refers to computer programs that enable machines to understand, analyze, and process human language appropriately.

[0720] "Methods for evaluating data reliability" refer to methods for quantifying the reliability of collected information and determining the consistency and trustworthiness of that information.

[0721] An "emotion engine" is a system that analyzes user input and past behavioral data to recognize and judge the user's emotions.

[0722] "Means of adjusting data visualization" refers to methods for optimizing the display format of information based on analysis results and the emotional state of users, making it visually easier to understand.

[0723] "Means of proposing business strategies" refers to methods of presenting appropriate action plans and strategies to users based on analyzed data and recognized emotional states.

[0724] The system of the present invention consists of three main components: a server, a terminal, and a user. The server is responsible for sequentially executing the following functions: information gathering, natural language processing, data reliability evaluation, sentiment recognition, and business strategy proposal.

[0725] First, the server autonomously collects data. Using technologies such as web crawlers, it gathers information from websites and data stores based on specified keywords and topics. This ensures that relevant information is retrieved efficiently.

[0726] Next, the server applies natural language processing techniques to the collected data. Specifically, it uses a natural language processing engine to parse the data and extract important topics and keywords. This step involves tokenizing and tagging the data by part of speech.

[0727] Next, the server evaluates the reliability of the data. Using a concordance algorithm, it calculates the consistency of data obtained from multiple sources and generates a reliability score. This process allows users to obtain information about the reliability of the data.

[0728] Furthermore, the server uses an emotion engine to recognize the user's emotions. By analyzing the user's input and past behavioral data, the user's emotional state is determined. This allows information to be provided in a way that is more appropriate to the user.

[0729] The device presents the user with adjusted information through its user interface. Here, the design and color scheme of graphs and charts are dynamically adjusted based on the results of the emotion engine. For example, if the user is feeling stressed, the data will be displayed with a calmer design.

[0730] Ultimately, the server proposes a business strategy tailored to the user's emotions. This strategy is then communicated to the user via their device as a customized, specific action plan. For example, if a project manager is seeking new measures to mitigate risks, the server uses a generative AI model to propose a positive and effective strategy.

[0731] As part of this process, an example of a prompt for the generated AI model is, "Please propose risk assessments and countermeasures for the progress of a new project from a positive perspective." This system helps users make emotionally driven decisions and improves work efficiency.

[0732] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0733] Step 1:

[0734] The server collects information using a web crawler. The crawler automatically retrieves data from the internet and data stores based on specified keywords or topics. The input to this process is keywords or topics, and the output is a collection of related web pages and article data.

[0735] Step 2:

[0736] The server analyzes the collected data using a natural language processing engine. Specifically, it tokenizes the data and extracts topics and keywords through part-of-speech tagging and syntactic analysis. The input for this step is web page or article data, and the output is a list of extracted topics and keywords.

[0737] Step 3:

[0738] The server evaluates the reliability of the data using a consistency rate algorithm. It generates a reliability score by comparing data from multiple sources and calculating consistency. The input is an extracted list of topics and keywords, and the output is the corresponding reliability score.

[0739] Step 4:

[0740] The server uses an emotion engine to recognize the user's emotional state. It analyzes the user's input text and past behavioral data and assigns an emotion label (e.g., positive, negative, neutral). The input is user text data or behavioral data, and the output is an emotion label.

[0741] Step 5:

[0742] The server adjusts the visualization of information based on the user's emotions. It dynamically changes the design and color scheme of graphs and charts using the results of the emotion engine. Inputs are confidence scores and emotion labels, and the output is an optimized graph or chart.

[0743] Step 6:

[0744] The terminal presents the user with optimized information. Through the user interface, it displays optimized graphs and charts and provides direct feedback. The input in this step is the optimized graphs and charts, and the output is the information provided to the user.

[0745] Step 7:

[0746] The server proposes business strategies based on the user's emotional state. Using a generative AI model based on data and emotional labels, it develops specific action plans, converts them into prompt messages, and sends them as notifications. Inputs are emotional labels and analyzed data, and output is a customized business proposal.

[0747] (Application Example 2)

[0748] 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".

[0749] There is a growing need for highly personalized information presentation based on information reliability assessment and sentiment analysis. In particular, conventional systems lack the ability to visualize information and propose business strategies that take into account the user's emotional state, which makes it difficult to make optimal decisions that meet the user's needs.

[0750] 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.

[0751] In this invention, the server includes an autonomous data acquisition means capable of collecting information, a means for analyzing the acquired information using a natural language processing engine, a means for evaluating the reliability of the information based on the analysis results, a means for dynamically visualizing and displaying the evaluated information according to the user's emotional state, and a means for proposing a business strategy based on the user's set conditions and emotional state. This makes it possible to provide information and business strategies optimized for the user's emotions.

[0752] "Autonomous data acquisition means capable of information gathering" refers to automated functions that can efficiently collect information from the internet and databases.

[0753] A "natural language processing engine" includes software technologies that analyze acquired text information and understand its syntax and meaning.

[0754] "Means for evaluating reliability" refers to algorithms or processes for numerically or qualitatively evaluating the accuracy and reliability of collected information.

[0755] "Means of dynamically visualizing and displaying information according to emotional state" refers to a function that adjusts the format and design of the displayed information in real time based on the user's emotional recognition.

[0756] "Means of proposing business strategies" refers to a function that provides optimal business responses and action plans according to the user's emotional state and set conditions.

[0757] In an embodiment for implementing the present invention, the system consists of three basic components: a server, a terminal, and a user. The server plays a central role in collecting, analyzing, evaluating the reliability of, and recognizing sentiments from a broad information space, and ultimately proposing business strategies. Specifically, the server uses a web crawler as an autonomous data acquisition means to collect information from the internet and databases based on specified keywords and topics. The collected information is analyzed in detail using a natural language processing engine (e.g., SpaCy or NLTK). This analysis makes it possible to understand the syntax and meaning of the information and extract topics and keywords of interest.

[0758] In the next stage of analysis, the server performs a reliability assessment. It verifies the consistency of data from multiple sources using a consistency algorithm and calculates a reliability score. The server also integrates an emotion engine (e.g., Google Cloud Natural Language API) to analyze user input and past behavioral data to determine the user's emotional state. This allows the server to dynamically adjust the presentation of information, delivering it in a way that suits the user's emotions. For example, if the server determines the user is seeking relaxation, it will display a screen with calming colors.

[0759] The device visualizes and presents these analysis results to the user in graph and chart format via a user interface. From the dashboard on the device, the user can review the received information and receive data in a way that aligns with their emotions. This allows the user to receive customized business strategies and make decisions optimized for their emotional state.

[0760] To give a concrete example, if a user is considering purchasing a new interior item, this system will recognize through sentiment analysis that the user is feeling anxious about the purchase. In this case, the system will provide advice in a reassuring tone and present the best options. An example of a prompt based on the generative AI model would be, "What emotions is this user experiencing while shopping at a furniture store? Please tell me how to recommend products that match those emotions."

[0761] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0762] Step 1:

[0763] The server uses a web crawler based on specified keywords and topics to collect information from the internet and databases. In this process, the server obtains a large amount of text data. The input is the search query and related internet sources, and the output is raw text data.

[0764] Step 2:

[0765] The server performs syntactic analysis on the acquired text data using a natural language processing engine (e.g., SpaCy or NLTK). The input is raw text data, and the output is the analyzed syntactic information and a set of important keywords. Based on these analysis results, the server performs data processing to extract important topics.

[0766] Step 3:

[0767] The server evaluates the reliability of the information using a reliability evaluation algorithm based on the analysis results. The input is the analyzed syntactic information and a list of information sources, and the output is a reliability score. The server calculates the agreement rate and performs data calculations to integrate the reliability of various information sources.

[0768] Step 4:

[0769] The server integrates an emotion engine (e.g., Google Cloud Natural Language API) to analyze the user's emotional state from their input data and past behavior history. The input is the user's input data and behavior history, and the output is the user's emotional state. Through emotion analysis, the server identifies the user's current emotion.

[0770] Step 5:

[0771] The terminal designs appropriate graphs and charts based on the analysis results and emotional state received from the server, and displays visualization information with dynamically adjusted colors. The input is the analysis results and emotional state, and the output is the visualization information presented to the user. The terminal dynamically changes based on the user and generates optimized visual information.

[0772] Step 6:

[0773] The server proposes a business strategy based on the analyzed emotional state. This proposal is an action plan customized to the user's emotional state. The input is the user's emotional state and settings, and the output is the proposed business strategy. The server integrates the information and builds an emotion-based strategy.

[0774] Step 7:

[0775] Users receive visualized information and suggestions through the device's dashboard and use them to inform their decision-making. The input is the presented visualized information and strategic suggestions, while the output is the action chosen by the user. Users can evaluate the information and make the choice that best suits them.

[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] An autonomous data acquisition method capable of collecting information,

[0800] A means of analyzing acquired information using a natural language processing engine,

[0801] A means of evaluating the reliability of information based on the analysis results,

[0802] A means of visualizing and displaying the evaluated information,

[0803] A means of notifying system users of the analysis results,

[0804] A means of proposing business strategies based on user-defined conditions,

[0805] A system that includes this.

[0806] (Claim 2)

[0807] The system according to claim 1, comprising means for calculating the reliability of information based on the agreement rate and the presence or absence of primary information.

[0808] (Claim 3)

[0809] The system according to claim 1, comprising means for providing a display in graph or chart format in the visualization of information.

[0810] "Example 1"

[0811] (Claim 1)

[0812] An autonomous data acquisition means for collecting information,

[0813] A natural language processing means for processing acquired information,

[0814] A means for determining the reliability of information based on the processing results,

[0815] A display means for visualizing highly reliable information,

[0816] A means of notifying the user of the analysis results,

[0817] A means of proposing a business plan based on the user's settings,

[0818] A system that includes this.

[0819] (Claim 2)

[0820] The system according to claim 1, comprising means for calculating the reliability of information based on weighting and the presence or absence of first-time information.

[0821] (Claim 3)

[0822] The system according to claim 1, comprising means for providing a display in graphic or chart format in the visualization of information.

[0823] "Application Example 1"

[0824] (Claim 1)

[0825] An autonomous data acquisition method capable of collecting information,

[0826] A means of analyzing acquired information using a natural language processing engine,

[0827] A means of evaluating the reliability of information based on the analysis results,

[0828] A means of visualizing and displaying the evaluated information,

[0829] A means of notifying system users of the analysis results,

[0830] A means of proposing business strategies based on user-defined conditions,

[0831] An application method for delivering personalized information based on user-specified interests,

[0832] A system that includes this.

[0833] (Claim 2)

[0834] The system according to claim 1, comprising means for calculating the reliability of information based on the agreement rate and the presence or absence of primary information.

[0835] (Claim 3)

[0836] The system according to claim 1, comprising means for providing a display in graph or chart format in the visualization of information.

[0837] "Example 2 of combining an emotion engine"

[0838] (Claim 1)

[0839] Means for autonomously acquiring data,

[0840] A method for analyzing acquired data using natural language processing technology,

[0841] A means for evaluating the reliability of data based on the analysis results,

[0842] A means of calculating reliability using the agreement rate,

[0843] A means of recognizing and analyzing the emotional state of users,

[0844] A means of dynamically adjusting data visualization in response to recognized emotions,

[0845] A means of proposing a business strategy tailored to the user based on the notified results,

[0846] A system that includes this.

[0847] (Claim 2)

[0848] The system according to claim 1, comprising an emotion engine and means for adjusting data visualization according to the user's emotions.

[0849] (Claim 3)

[0850] The system according to claim 1, comprising means for optimizing the display design based on the user's emotional state in the visualization of information in graph or chart format.

[0851] "Application example 2 when combining with an emotional engine"

[0852] (Claim 1)

[0853] An autonomous data acquisition method capable of collecting information,

[0854] A means of analyzing acquired information using a natural language processing engine,

[0855] A means of evaluating the reliability of information based on the analysis results,

[0856] A means of dynamically visualizing and displaying the evaluated information according to the user's emotional state,

[0857] A means of notifying system users of the analysis results,

[0858] A means of proposing business strategies based on the user's set conditions and emotional state,

[0859] A system that includes this.

[0860] (Claim 2)

[0861] The system according to claim 1, comprising means for calculating the reliability of information based on the agreement rate and the presence or absence of primary information.

[0862] (Claim 3)

[0863] The system according to claim 1, comprising means for providing a display that adjusts the design and color scheme of a graph or chart in response to the emotional state of the user in the visualization of information. [Explanation of Symbols]

[0864] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. An autonomous data acquisition method capable of collecting information, A means of analyzing acquired information using a natural language processing engine, A means of evaluating the reliability of information based on the analysis results, A means of visualizing and displaying the evaluated information, A means of notifying system users of the analysis results, A means of proposing business strategies based on user-defined conditions, A system that includes this.

2. The system according to claim 1, comprising means for calculating the reliability of information based on the agreement rate and the presence or absence of primary information.

3. The system according to claim 1, comprising means for providing a display in graph or chart format in the visualization of information.

Images