system

Abstract

We provide the system. [Solution] Means of collecting data, Means for analyzing the data and generating metadata, Means for classifying and tagging data based on the metadata, A method for detecting and organizing unnecessary data, A means for visualizing the organized data, A system that includes this.

Description

Technical Field

[0005] ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] With the increase in the amount of data, the tasks of data management and organization require a great deal of time and labor. In particular, when dealing with large-scale data, human errors are likely to occur in the classification and tagging of incorrect data, which is a factor reducing the efficiency of data management. In addition, it is difficult to organize data with a low update frequency or data without owner information, and as a result, data redundancy and inconsistency may occur. Therefore, it is required to automate efficient data collection, analysis, classification, organization, and visualization.

Means for Solving the Problems

[0005] This invention provides a means for collecting data and generating metadata through its analysis. Furthermore, by providing means for classifying and tagging data based on the generated metadata, it reduces human errors caused by incorrect classification. In addition, by combining this with means for detecting and organizing unnecessary data, data redundancy is suppressed. Finally, the organized data can be easily grasped through visualization means, realizing a system that significantly improves the efficiency of data management.

[0006] "Data" refers to a collection of numbers, strings, and other information that is gathered for the purpose of analysis and processing.

[0007] "Metadata" refers to additional data that records information about data, indicating its attributes and related information.

[0008] "Analysis" is the process of classifying, formalizing, and interpreting data, and the work of finding the characteristics and patterns of that data.

[0009] "Classification" is the process of grouping and organizing data based on specific criteria.

[0010] "Tagging" is the process of assigning identifying labels to data to make it easier to recognize and search for that data.

[0011] "Unnecessary data" refers to data that should be deleted or removed from a database due to reasons such as infrequent updates or unclear ownership.

[0012] "Visualization" is the process of presenting organized data in the form of graphs, charts, and other visual aids to understanding the information.

[0013] "Automation" is a technology that enables systems to process information and achieve goals efficiently without human intervention. [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] An example of an embodiment of the system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

[0017] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

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

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

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

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

[0024] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0031] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] The system of the present invention includes a series of processes that automatically perform data collection, analysis, metadata generation, data classification and tagging, detection and removal of unnecessary data, and visualization. This makes it possible to significantly improve the efficiency of data management. The following specifically describes examples of each means and their operation.

[0036] Within this system, the server first collects necessary data from external data sources, such as databases and APIs. The collected data is diverse and includes various types of information related to business activities, such as sales information, customer information, and inventory information. This collected data is temporarily stored and then passed on to the next processing step.

[0037] Next, the server analyzes the collected data. This analysis is performed using natural language processing, machine learning algorithms, or statistical analysis techniques, with the aim of revealing the data's attributes and generating metadata based on them. For example, in the case of sales data, attributes such as the date, customer ID, and sales amount are extracted.

[0038] Furthermore, the device automatically classifies the data using this generated metadata and assigns appropriate tags. This classification is performed according to the purpose of the data's use, and tagging improves the efficiency of data searching and re-editing. For example, sales data is classified into categories such as "Sales," "By Customer," and "By Period."

[0039] The server then detects unnecessary data. Criteria for unnecessary data include infrequent updates and lack of owner information. Detected unnecessary data is automatically deleted or archived to separate storage based on pre-configured policies.

[0040] Finally, users can view the organized data through visualization tools on their devices. This allows them to intuitively obtain the information necessary for business management, such as understanding sales patterns and customer trends through graphs.

[0041] By operating such a system, the accuracy and efficiency of data management will improve, and human resources will be able to be allocated to higher value-added activities.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The server collects data from pre-configured data sources (e.g., databases or APIs). This includes sales information and customer data, which are collected periodically or based on trigger events and stored in temporary storage.

[0045] Step 2:

[0046] The server analyzes the collected data and extracts the attributes of each data point. Natural language processing and machine learning algorithms are used to identify the content of the data and organize it according to its attributes. This clarifies the background information of the data.

[0047] Step 3:

[0048] The server generates metadata based on the analyzed data. This metadata includes data type, generation date and time, and associated tags, and is used for data management and retrieval. The generated metadata is stored in a database.

[0049] Step 4:

[0050] The device uses metadata to categorize relevant data into specific categories. These categories could include "sales," "customer information," and "inventory management." Therefore, appropriate tags are assigned to the data to organize it effectively.

[0051] Step 5:

[0052] The server scans the existing database and detects unnecessary data. Based on criteria such as infrequently updated data or data lacking owner information, it automatically deletes or archives the data, enabling efficient data management.

[0053] Step 6:

[0054] Users can access visualization tools from their devices to view organized data in graphs and charts. This allows them to visually grasp sales trends and customer behavior, supporting data-driven decision-making.

[0055] (Example 1)

[0056] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0057] In modern information management, there is a demand for efficiently processing vast amounts of data and quickly extracting useful information. However, in conventional systems, the processes of data collection, analysis, classification, organization, and visualization are often performed separately, making centralized management difficult. As a result, information leaks and wasteful handling of data occur, leading to a decrease in the efficiency of data management.

[0058] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0059] In this invention, the server includes means for collecting information from a data source, means for analyzing the information to generate attached information, and means for classifying the information and assigning identification labels based on the attached information. This enables centralized management from efficient information collection to analysis, classification, organization, and visualization.

[0060] A "data source" refers to an external infrastructure or mechanism that provides information, and this includes databases and APIs.

[0061] "Means of collecting information" refers to methods and processes for obtaining useful information from data sources, and includes technologies aimed at automating and streamlining information acquisition.

[0062] "Analysis" refers to the process of evaluating collected information and extracting meaningful features and patterns from it, and is carried out using natural language processing and machine learning algorithms.

[0063] "Attached information" refers to attribute data derived from the analyzed information that explains or complements the original information.

[0064] "Classification" is the process of organizing information according to specific criteria and grouping information that has similar characteristics.

[0065] "Means of assigning identification labels" refers to the process of adding labels to classified information to facilitate identification, and plays a role in making searching and filtering more efficient.

[0066] "Information that has not been exchanged" refers to information that has not been updated and shows no signs of activity for a long period of time; deletion or archiving is usually recommended.

[0067] "Visual presentation methods" refer to the process of converting information into visual forms such as graphs and charts, and displaying them in a way that can be intuitively understood.

[0068] The embodiment of this invention aims to centrally perform a series of processes from data collection to analysis, classification, organization, and visualization. This system enables efficient data management through the respective roles of the server, terminal, and user.

[0069] The server collects information from external data sources. The hardware and software used at this stage include corporate databases and third-party APIs. The server accesses these data sources to retrieve a variety of data related to business activities, such as sales information, customer information, and inventory information.

[0070] Next, the server analyzes the collected information. This analysis process utilizes natural language processing tools and machine learning algorithms. Specific software examples include the Python libraries NLTK and TENSORFLOW®. The server uses these tools to extract attributes from the information and generate attached information (metadata).

[0071] The terminal then classifies the information based on the generated attachments and assigns identification labels. This classification and labeling process improves information organization and search efficiency. An example of software used is ElasticSearch®, which enables rapid searching and filtering.

[0072] Finally, users are presented with organized information visually via their devices. Common visualization software such as Tableau and Power BI can be used as visualization tools. By leveraging these tools to graph and chart data, users can quickly obtain the information necessary for strategic decision-making within their business.

[0073] As a concrete example, a server retrieves sales data from an API and identifies customer segments through analysis. Next, the terminal assigns labels such as "high-frequency buyer" and "low-frequency buyer." Finally, the user uses a visualization tool to graph monthly sales trends, allowing them to visually understand when sales peak.

[0074] An example of an input prompt for a generating AI model is, "Visualize the purchase frequency and sales amount for each customer and output it as a graph."

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

[0076] Step 1:

[0077] The server collects information from data sources. The input is raw data obtained from APIs and databases. This data includes company sales, inventory, and customer information. The server periodically retrieves this data and stores it in temporary storage. Specifically, the server sends API requests and saves the received JSON-formatted response data to an SQL database. The output is the stored data in a structured format.

[0078] Step 2:

[0079] The server analyzes the data it has collected. The input is structured data collected and stored in the previous step. The server extracts features from the data using natural language processing tools and machine learning algorithms and generates metadata. Specifically, it processes the dataset using Python's NLTK and TensorFlow to analyze customer purchasing patterns and sales trends. The output is metadata with attribute information attached.

[0080] Step 3:

[0081] The terminal classifies information based on the generated metadata and assigns identification labels. The input is metadata obtained through analysis. The terminal uses a classification algorithm to divide the information into specific categories and assigns identification labels to facilitate searching and management. Specifically, it uses Elasticsearch to tag data into categories such as "Sales," "Monthly," and "Regional." The output is the classified and labeled information.

[0082] Step 4:

[0083] The server detects and organizes information that is not being exchanged. The input is categorized and labeled information. The server identifies unnecessary data based on update frequency and owner identification information, and deletes or archives it according to a pre-configured policy. Specifically, it runs a cleaning script and moves old data to backup storage. The output is organized information.

[0084] Step 5:

[0085] The system presents organized information visually to the user through their device. The input is organized information. The user uses visualization tools to graph the data and obtain information useful for business management. Specifically, it utilizes tools like Tableau and Power BI to visualize sales data as a time-series graph. The output is usable graphical data.

[0086] (Application Example 1)

[0087] 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."

[0088] Current data management systems require the efficient classification and processing of vast amounts of data, but organizing and visualizing that data remains a time-consuming task. Furthermore, there is a lack of means to automatically detect and manage unnecessary information within the data. It is necessary to address these challenges and improve the efficiency and accuracy of data management.

[0089] 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.

[0090] In this invention, the server includes means for acquiring data, means for analyzing the data to generate supplementary data, and means for classifying the data and assigning specific information based on the supplementary data. This enables efficient data management and rapid acquisition of necessary information.

[0091] "Means for acquiring data" refers to a device or method that has the function of gathering necessary information from external sources.

[0092] "Supplemental data" refers to data that is generated by analyzing acquired information, indicating attributes and characteristics that make the original information more useful.

[0093] "Means for classification and assigning specific information" refers to methods for grouping information based on analyzed data and attaching related specific information or tags.

[0094] "Means for detecting and organizing unnecessary data" refers to a process or device for identifying, organizing, or deleting unnecessary information from a given set of pieces of data.

[0095] "Means of visualization" refer to methods and techniques for visually representing organized information, with the aim of promoting understanding.

[0096] A "clustering algorithm" is a mathematical or statistical method for automatically classifying a group of information into different groups.

[0097] "Means for adding specific risk information" refers to a method or apparatus for analyzing information, evaluating the degree of risk, and adding predetermined risk information to each piece of information.

[0098] The system implementing this invention employs a method in which a server, terminal, and user cooperate to efficiently manage information.

[0099] First, the server retrieves information from external sources. These sources include databases and APIs, allowing for the collection of a wide variety of information. The collected information is temporarily stored and then passed on to subsequent processing steps.

[0100] Next, the server analyzes the collected information. This analysis utilizes natural language processing, machine learning algorithms, and statistical analysis techniques, which in turn generates supplementary data. For example, this involves extracting the characteristics of the information and organizing them as metadata.

[0101] Next, the terminal automatically classifies the information based on the generated supplemental data and assigns specific information. This classification is performed using a clustering algorithm, and the information is grouped based on similarity. At the same time, specific information such as risk information is added, improving the efficiency of management.

[0102] Furthermore, the server detects and organizes unnecessary information. Criteria for determining unnecessary information include its update frequency. This information is automatically deleted or archived to separate storage.

[0103] Finally, users can view the organized information through visualization tools on their devices. This allows them to intuitively grasp the necessary information and, for example, visually understand inventory surpluses or shortages in a logistics center.

[0104] As a concrete example, in inventory management at a logistics center, this system can be used to quickly identify which products are in excess inventory and which products have a fast turnover. An example of a prompt to the generated AI model is, "Analyze the inventory data from the logistics center and classify and visualize high-risk and low-risk inventory using clustering."

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

[0106] Step 1:

[0107] The server retrieves data from external sources. It receives information provided by databases and APIs as input. The server sends queries to these sources to collect the necessary information. The resulting dataset is then temporarily stored in storage.

[0108] Step 2:

[0109] The server analyzes the collected data. It uses the dataset obtained in Step 1 as input. The server uses natural language processing and machine learning algorithms to analyze the data's attributes and trends, and generates supplementary data (metadata). The generated metadata is then passed to the next processing step.

[0110] Step 3:

[0111] The terminal classifies the data using the generated supplemental data and assigns specific information to it. The metadata generated in step 2 is used as input. The terminal applies a clustering algorithm to classify the data into groups with similar properties. The output includes the classification results along with specific information assigned to each data point.

[0112] Step 4:

[0113] The server detects and organizes unnecessary data. It uses the data categorized in step 3 as input. The server automatically identifies data deemed unnecessary based on its update frequency and ownership information. As output, identified unnecessary data is deleted or archived, leaving only important data.

[0114] Step 5:

[0115] The user visualizes the organized data on their device. The data organized in step 4 is used as input. Using visualization tools, the user intuitively identifies data patterns and trends. Visual graphs and charts are generated as output, making it easier for the user to understand the data.

[0116] 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.

[0117] This invention is a system that combines an emotion engine with a series of processes from data collection to visualization. This emotion engine analyzes the user's emotional state and uses the results to adjust the data visualization. The main components and examples of its operation are shown below.

[0118] The core function of this system begins with the server collecting data from multiple data sources. For example, this could include sales data and customer information held by a company. This data is temporarily stored on the server as foundational information for subsequent steps.

[0119] The server analyzes this data and generates metadata based on it. This metadata includes data attribute information and related tags, which are used in subsequent classification tasks. Furthermore, terminals use the metadata to classify data into appropriate categories and assign tags, facilitating data management and retrieval.

[0120] A distinctive feature of this invention lies in its emotion engine, which recognizes the user's emotions. While the user is using the system, the emotion engine analyzes the user's input and responses in real time to identify the user's emotional state. This analysis is then reflected in the visualization process. For example, if the user is feeling stressed, the visualization of that data is automatically adjusted to a more intuitive and simpler format.

[0121] The server automatically detects and organizes unnecessary data based on update frequency and owner information. This ensures efficient data management and quick access to important information.

[0122] Finally, users can access visualization tools from their devices to intuitively understand data that has been refined by the emotion engine. For example, when a user reviews sales data, the color palette and graph types are optimized according to the emotion engine's guidelines, improving readability.

[0123] In this way, this system dynamically adapts to user emotions, achieving highly efficient and secure data management, and ultimately increasing user satisfaction.

[0124] The following describes the processing flow.

[0125] Step 1:

[0126] The server collects necessary data from external data sources. For example, it extracts data in various formats, such as sales data and customer information, and stores it in temporary storage.

[0127] Step 2:

[0128] The server analyzes the collected data, calculates its attributes, and generates metadata. Natural language processing and machine learning algorithms are used for the analysis, which identifies the characteristics and structure of the data.

[0129] Step 3:

[0130] The terminal uses the generated metadata to categorize the data and assign appropriate tags. In this process, categories such as "sales," "customer information," and "market trends" are set, and the data is organized and tidied up.

[0131] Step 4:

[0132] The server monitors the data in the database and detects unnecessary data. This includes data that is not frequently updated or whose owner is unknown, and this data is automatically deleted or archived.

[0133] Step 5:

[0134] While the user interacts with the system, the emotion engine analyzes the user's input data and physiological responses in real time. This analysis identifies the user's emotional state, and the results are then reflected in the next process.

[0135] Step 6:

[0136] The device adjusts data visualizations based on the analysis results of the emotion engine. For example, if the user is experiencing stress, it changes the color palette of the graph to provide a simpler and easier-to-understand visualization. This approach facilitates an intuitive understanding of the data.

[0137] Step 7:

[0138] Ultimately, users review the adjusted data through their devices and make decisions based on the visualized information. This leads to faster and more accurate data-driven decision-making, improving work efficiency.

[0139] (Example 2)

[0140] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0141] Traditional data management systems have the drawback of not adequately optimizing the user experience because they visualize data without considering the user's emotional state. Furthermore, the lack of dynamic adjustments in efficient data classification and removal of unnecessary data makes it difficult to quickly understand information and access important data.

[0142] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0143] In this invention, the server includes means for collecting data, means for analyzing the data to generate metadata, and means for analyzing the user's emotional state and adjusting the data visualization based on the generated emotional information. This enables optimal data visualization according to the user's emotions, improving the user experience and allowing quick access to important information.

[0144] "Means of data collection" refers to a function that automatically retrieves necessary information from multiple sources and converts it into a format usable within the system.

[0145] "Methods for analyzing data and generating metadata" refers to the process of analyzing collected information, extracting related attribute information and tags, and generating additional information to facilitate management.

[0146] "Methods for classifying and tagging data based on metadata" refers to methods of using generated additional information to divide data into specific categories and attach sticky notes to streamline management and retrieval.

[0147] "Methods for analyzing a user's emotional state" refer to technologies that analyze a user's input, reactions, facial expressions, etc., to determine their psychological state.

[0148] "Means of adjusting data visualization based on emotional information" refers to a function that changes the method of presenting information and the display style according to the analyzed emotional results, thereby improving user comprehension.

[0149] "Methods for detecting and organizing unnecessary data" refers to a system that automatically identifies and organizes infrequently used information based on its retention period and importance.

[0150] "Methods for visualizing organized data" refer to technologies that present managed information in the form of graphs and charts that users can easily understand.

[0151] This invention relates to a data management system that analyzes the user's emotional state and dynamically adjusts data visualization based on that analysis. This system can optimize the user experience throughout the entire process, from data collection to visualization. The specific implementation of this system is described below.

[0152] The server retrieves data from multiple sources. Specifically, it uses APIs to collect company sales and customer information and temporarily stores it on the server. Next, the server analyzes the collected data using the Python Pandas library and generates metadata, including information attributes and related tags. Using this metadata, the terminal classifies the data into categories and manages it efficiently through an SQL database.

[0153] To analyze the user's emotional state, the system uses a machine learning model (for example, a CNN model using TensorFlow). It analyzes data collected from the user's webcam and input devices to determine emotions from the user's facial expressions and behavior. The results of the analysis are reflected in the data visualization. The server uses the D3.js library to present the data in the most appropriate format corresponding to the user's emotions. For example, a simple bar graph is displayed for users experiencing stress.

[0154] The system also automatically detects and organizes unnecessary data based on its usage frequency and ownership information. This feature enables more efficient data storage and quicker access to important information. Finally, users can easily understand visualized data by interacting with an interactive dashboard via their device.

[0155] For example, when a user views sales data, the emotion engine will change the graph visualization if it detects a stress level. An example prompt would be, "How can I automatically adjust the sales data visualization based on the user's emotions?"

[0156] Thus, the embodiments of the present invention enable data presentation that responds to the user's emotions, providing a system that improves both data management and user experience.

[0157] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0158] Step 1:

[0159] The server collects data from multiple sources via APIs. It sends requests containing API keys and authentication information as input, retrieving company sales data and customer information. The retrieved data is stored in JSON or CSV format. This process ensures that the necessary information is incorporated into the system.

[0160] Step 2:

[0161] The server uses the Pandas library to analyze the collected data. It reads the data stored as input and performs data cleaning, such as checking for missing values ​​and removing duplicate data. As a result of the analysis, metadata is generated based on the data's attribute information and relationships. This metadata streamlines data management.

[0162] Step 3:

[0163] The terminal utilizes the generated metadata to categorize the data. Metadata is used as input, and SQL commands are used to categorize the information in the database. The categorized data is tagged, making it easy to search.

[0164] Step 4:

[0165] The user accesses the system and manipulates data through their terminal. During this process, the emotion engine acquires data from the user's webcam and input devices. Using the user's facial expressions and operation patterns as input, a generative AI model is employed to analyze their emotional state in real time. The user's emotional state is then determined as output.

[0166] Step 5:

[0167] The server uses the D3.js library to visualize data based on the user's emotional state. Input includes analyzed emotional states and classified data, which are used to dynamically adjust the visualization style. The output is data visualized in an optimal format for each emotion.

[0168] Step 6:

[0169] The server periodically detects and organizes unnecessary data based on its usage frequency and owner information. Using access logs and owner metadata from the database as input, it compresses or deletes data that has not been used for a long period. This improves storage efficiency.

[0170] Step 7:

[0171] Users view visualized data through their devices and examine sales data trends as a concrete example. Depending on the sentiment analysis results, information may be presented in a simple display format, making it easier for users to intuitively understand the data.

[0172] (Application Example 2)

[0173] 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".

[0174] Modern information processing systems require the effective management and utilization of vast amounts of data. However, conventional systems lack the flexibility to provide data that responds to user emotions and states, leading to problems such as reduced user experience and operational efficiency. Therefore, there is a need for means to achieve dynamic data visualization and management that takes user emotions into account.

[0175] 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.

[0176] In this invention, the server includes means for collecting data, means for collecting and analyzing emotional data, and means for adaptively adjusting the visualization based on the emotional data. This enables dynamic and effective visualization of data in accordance with the user's emotional state.

[0177] "Means of data collection" refers to the process or function of obtaining necessary information from networks and devices.

[0178] "Means for generating metadata" refers to a process or apparatus for analyzing collected information and generating supplementary information that explicitly indicates its attributes, relationships, etc.

[0179] "Means of classifying and tagging data" refers to the process of dividing information into specific categories and assigning identifiable tags based on the generated metadata.

[0180] "Methods for detecting and organizing unnecessary data" refers to a process of judging the value of information based on its update frequency and attributes, and eliminating and organizing the unnecessary parts.

[0181] "Means of visualization" refers to a process or device for displaying organized information in a format that is easily understandable to the user.

[0182] "Means for collecting and analyzing emotional data" refers to the process or technology of acquiring information from facial expressions and behaviors and analyzing it in order to understand the emotional state of a user.

[0183] "Means of adaptively adjusting visualization" refers to a process that dynamically changes the format and content of the displayed data to suit the individual user's state, based on the analyzed emotional information.

[0184] In realizing this system, the server, terminals, and users each play crucial roles. The server collects data from the network and generates metadata from it. This metadata includes attribute information and tags for each data item and is sent to the terminals for subsequent data classification and tagging tasks.

[0185] The server also collects and analyzes user emotion data. This involves using technologies that capture and analyze the user's emotional state in real time, such as facial recognition cameras and voice analysis systems. This information is analyzed on the server and used to dynamically adjust data visualizations based on the user's emotions. This process utilizes emotion analysis algorithms built in programming languages ​​such as Python, and JavaScript® libraries such as D3.js for data visualization.

[0186] The terminal acts as the interface with the user, displaying visualization results sent from the server. The terminal, such as smart glasses, presents information in a format suitable for the user. In this process, an emotion engine is used to customize the information according to the user's state. For example, if the user is relaxed, detailed product information can be displayed.

[0187] This system is designed for use in physical stores and can analyze customer sentiment in real time as they browse the store, providing them with relevant information on products and sales that interest them. This is expected to improve the user experience.

[0188] As a concrete example, when a user approaches a specific product shelf in a store, information about the products on that shelf is displayed on their smart glasses. When the user is feeling stressed, only simple and intuitive information is displayed, providing a comfortable shopping experience tailored to their emotions.

[0189] Examples of prompts for the generating AI model include, "If the user is relaxed, we will display bonus points information and products with relaxing effects," and "If the user is excited, we will suggest new products that stimulate adrenaline."

[0190] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0191] Step 1:

[0192] The server collects data from the network. It takes information from external data sources as input and generates metadata based on this. This metadata includes data attribute information and related tags, which are used in subsequent processing. The output is metadata that forms the basis for classification and tagging.

[0193] Step 2:

[0194] The server collects and analyzes user emotional data using emotion recognition cameras and voice input devices. It acquires user facial expressions and voice data as input and applies an emotion analysis algorithm built in Python to identify the user's emotional state. The analysis results are output, and this data is used to refine the visualization process.

[0195] Step 3:

[0196] The server classifies and tags data based on the generated metadata. It uses metadata as input, parses it, categorizes and tags it, and organizes it in a manageable format. The output is structured data suitable for searching and visualization.

[0197] Step 4:

[0198] The terminal dynamically adjusts the data visualization based on the sentiment analysis results sent from the server. Here, it receives sentiment data and structured data as input and displays them to the user in a visually understandable format using the JavaScript library D3.js. The output is visualized data adapted to the user's emotional state.

[0199] Step 5:

[0200] Users access in-store information through smart glasses. This action allows them to make decisions based on the data displayed on the device. Visualized information is received as input, which is then used to inform purchasing decisions and product selections. The output is improved user experience and increased satisfaction.

[0201] Step 6:

[0202] The device then sends user feedback and new sentiment data back to the server. This step improves data accuracy by collecting the user's latest sentiment state and operation history as input and transferring it to the server. The output is more accurate sentiment recognition and data visualization.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] [Second Embodiment]

[0207] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0208] 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.

[0209] 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).

[0210] 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.

[0211] 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.

[0212] 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).

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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".

[0219] The system of the present invention includes a series of processes that automatically perform data collection, analysis, metadata generation, data classification and tagging, detection and removal of unnecessary data, and visualization. This makes it possible to significantly improve the efficiency of data management. The following specifically describes examples of each means and their operation.

[0220] Within this system, the server first collects necessary data from external data sources, such as databases and APIs. The collected data is diverse and includes various types of information related to business activities, such as sales information, customer information, and inventory information. This collected data is temporarily stored and then passed on to the next processing step.

[0221] Next, the server analyzes the collected data. This analysis is performed using natural language processing, machine learning algorithms, or statistical analysis techniques, with the aim of revealing the data's attributes and generating metadata based on them. For example, in the case of sales data, attributes such as the date, customer ID, and sales amount are extracted.

[0222] Furthermore, the device automatically classifies the data using this generated metadata and assigns appropriate tags. This classification is performed according to the purpose of the data's use, and tagging improves the efficiency of data searching and re-editing. For example, sales data is classified into categories such as "Sales," "By Customer," and "By Period."

[0223] The server then detects unnecessary data. Criteria for unnecessary data include infrequent updates and lack of owner information. Detected unnecessary data is automatically deleted or archived to separate storage based on pre-configured policies.

[0224] Finally, users can view the organized data through visualization tools on their devices. This allows them to intuitively obtain the information necessary for business management, such as understanding sales patterns and customer trends through graphs.

[0225] By operating such a system, the accuracy and efficiency of data management will improve, and human resources will be able to be allocated to higher value-added activities.

[0226] The following describes the processing flow.

[0227] Step 1:

[0228] The server collects data from pre-configured data sources (e.g., databases or APIs). This includes sales information and customer data, which are collected periodically or based on trigger events and stored in temporary storage.

[0229] Step 2:

[0230] The server analyzes the collected data and extracts the attributes of each data point. Natural language processing and machine learning algorithms are used to identify the content of the data and organize it according to its attributes. This clarifies the background information of the data.

[0231] Step 3:

[0232] The server generates metadata based on the analyzed data. This metadata includes data type, generation date and time, and associated tags, and is used for data management and retrieval. The generated metadata is stored in a database.

[0233] Step 4:

[0234] The device uses metadata to categorize relevant data into specific categories. These categories could include "sales," "customer information," and "inventory management." Therefore, appropriate tags are assigned to the data to organize it effectively.

[0235] Step 5:

[0236] The server scans the existing database and detects unnecessary data. Based on criteria such as infrequently updated data or data lacking owner information, it automatically deletes or archives the data, enabling efficient data management.

[0237] Step 6:

[0238] Users can access visualization tools from their devices to view organized data in graphs and charts. This allows them to visually grasp sales trends and customer behavior, supporting data-driven decision-making.

[0239] (Example 1)

[0240] 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."

[0241] In modern information management, there is a demand for efficiently processing vast amounts of data and quickly extracting useful information. However, in conventional systems, the processes of data collection, analysis, classification, organization, and visualization are often performed separately, making centralized management difficult. As a result, information leaks and wasteful handling of data occur, leading to a decrease in the efficiency of data management.

[0242] 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.

[0243] In this invention, the server includes means for collecting information from a data source, means for analyzing the information to generate attached information, and means for classifying the information and assigning identification labels based on the attached information. This enables centralized management from efficient information collection to analysis, classification, organization, and visualization.

[0244] A "data source" refers to an external infrastructure or mechanism that provides information, and this includes databases and APIs.

[0245] "Means of collecting information" refers to methods and processes for obtaining useful information from data sources, and includes technologies aimed at automating and streamlining information acquisition.

[0246] "Analysis" refers to the process of evaluating collected information and extracting meaningful features and patterns from it, and is carried out using natural language processing and machine learning algorithms.

[0247] "Attached information" refers to attribute data derived from the analyzed information that explains or complements the original information.

[0248] "Classification" is the process of organizing information according to specific criteria and grouping information that has similar characteristics.

[0249] "Means of assigning identification labels" refers to the process of adding labels to classified information to facilitate identification, and plays a role in making searching and filtering more efficient.

[0250] "Information that has not been exchanged" refers to information that has not been updated and shows no signs of activity for a long period of time; deletion or archiving is usually recommended.

[0251] "Visual presentation methods" refer to the process of converting information into visual forms such as graphs and charts, and displaying them in a way that can be intuitively understood.

[0252] The embodiment of this invention aims to centrally perform a series of processes from data collection to analysis, classification, organization, and visualization. This system enables efficient data management through the respective roles of the server, terminal, and user.

[0253] The server collects information from external data sources. The hardware and software used at this stage include corporate databases and third-party APIs. The server accesses these data sources to retrieve a variety of data related to business activities, such as sales information, customer information, and inventory information.

[0254] Next, the server analyzes the collected information. This analysis process utilizes natural language processing tools and machine learning algorithms. Specific software examples include the Python libraries NLTK and TensorFlow. The server uses these tools to extract attributes from the information and generate attached information (metadata).

[0255] Subsequently, the terminal classifies the information based on the generated attachments and assigns identification labels. This classification and labeling process improves information organization and search efficiency. An example of software used is Elasticsearch, which enables rapid searching and filtering.

[0256] Finally, users are presented with organized information visually via their devices. Common visualization software such as Tableau and Power BI can be used as visualization tools. By leveraging these tools to graph and chart data, users can quickly obtain the information necessary for strategic decision-making within their business.

[0257] As a concrete example, a server retrieves sales data from an API and identifies customer segments through analysis. Next, the terminal assigns labels such as "high-frequency buyer" and "low-frequency buyer." Finally, the user uses a visualization tool to graph monthly sales trends, allowing them to visually understand when sales peak.

[0258] An example of an input prompt for a generating AI model is, "Visualize the purchase frequency and sales amount for each customer and output it as a graph."

[0259] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0260] Step 1:

[0261] The server collects information from data sources. The input is raw data obtained from APIs and databases. This data includes company sales, inventory, and customer information. The server periodically retrieves this data and stores it in temporary storage. Specifically, the server sends API requests and saves the received JSON-formatted response data to an SQL database. The output is the stored data in a structured format.

[0262] Step 2:

[0263] The server analyzes the data it has collected. The input is structured data collected and stored in the previous step. The server extracts features from the data using natural language processing tools and machine learning algorithms and generates metadata. Specifically, it processes the dataset using Python's NLTK and TensorFlow to analyze customer purchasing patterns and sales trends. The output is metadata with attribute information attached.

[0264] Step 3:

[0265] The terminal classifies information based on the generated metadata and assigns identification labels. The input is metadata obtained through analysis. The terminal uses a classification algorithm to divide the information into specific categories and assigns identification labels to facilitate searching and management. Specifically, it uses Elasticsearch to tag data into categories such as "Sales," "Monthly," and "Regional." The output is the classified and labeled information.

[0266] Step 4:

[0267] The server detects and organizes information that is not being exchanged. The input is categorized and labeled information. The server identifies unnecessary data based on update frequency and owner identification information, and deletes or archives it according to a pre-configured policy. Specifically, it runs a cleaning script and moves old data to backup storage. The output is organized information.

[0268] Step 5:

[0269] The system presents organized information visually to the user through their device. The input is organized information. The user uses visualization tools to graph the data and obtain information useful for business management. Specifically, it utilizes tools like Tableau and Power BI to visualize sales data as a time-series graph. The output is usable graphical data.

[0270] (Application Example 1)

[0271] 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."

[0272] Current data management systems require the efficient classification and processing of vast amounts of data, but organizing and visualizing that data remains a time-consuming task. Furthermore, there is a lack of means to automatically detect and manage unnecessary information within the data. It is necessary to address these challenges and improve the efficiency and accuracy of data management.

[0273] 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.

[0274] In this invention, the server includes means for acquiring data, means for analyzing the data to generate supplementary data, and means for classifying the data and assigning specific information based on the supplementary data. This enables efficient data management and rapid acquisition of necessary information.

[0275] "Means for acquiring data" refers to a device or method that has the function of gathering necessary information from external sources.

[0276] "Supplemental data" refers to data that is generated by analyzing acquired information, indicating attributes and characteristics that make the original information more useful.

[0277] "Means for classification and assigning specific information" refers to methods for grouping information based on analyzed data and attaching related specific information or tags.

[0278] "Means for detecting and organizing unnecessary data" refers to a process or device for identifying, organizing, or deleting unnecessary information from a given set of pieces of data.

[0279] "Means of visualization" refer to methods and techniques for visually representing organized information, with the aim of promoting understanding.

[0280] A "clustering algorithm" is a mathematical or statistical method for automatically classifying a group of information into different groups.

[0281] "Means for adding specific risk information" refers to a method or apparatus for analyzing information, evaluating the degree of risk, and adding predetermined risk information to each piece of information.

[0282] The system implementing this invention employs a method in which a server, terminal, and user cooperate to efficiently manage information.

[0283] First, the server retrieves information from external sources. These sources include databases and APIs, allowing for the collection of a wide variety of information. The collected information is temporarily stored and then passed on to subsequent processing steps.

[0284] Next, the server analyzes the collected information. Natural language processing, machine learning algorithms, and statistical analysis techniques are used for the analysis, and supplementary data is generated thereby. For example, it is a process of extracting the features of the information and organizing them as metadata.

[0285] Subsequently, based on the generated supplementary data, the terminal automatically classifies the information and assigns specific information. This classification is performed using a clustering algorithm, and the information is grouped based on similarity. At the same time, specific information such as risk information is added to improve management efficiency.

[0286] Furthermore, the server detects and organizes unnecessary information. Criteria for determining what is unnecessary include the update frequency. This information is automatically deleted or archived in another storage.

[0287] Finally, the user can view the organized information through the terminal using a visualization tool. This enables intuitive understanding of the necessary information, for example, visually understanding excess or shortage of inventory in a logistics center.

[0288] As a specific example, in inventory management of a logistics center, by using this system, it is possible to quickly grasp which products have excessive inventory and which products have fast turnover. An example of a prompt for the generation AI model is "Analyze the inventory data of the logistics center, classify and visualize high-risk and low-risk inventory by clustering".

[0289] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0290] Step 1:

[0291] The server acquires data from external information sources. As input, it receives information provided by databases and APIs. The server sends queries to these information sources and collects the necessary information. The resulting data set is temporarily stored in storage.

[0292] Step 2:

[0293] The server analyzes the collected data. It uses the dataset obtained in Step 1 as input. The server uses natural language processing and machine learning algorithms to analyze the data's attributes and trends, and generates supplementary data (metadata). The generated metadata is then passed to the next processing step.

[0294] Step 3:

[0295] The terminal classifies the data using the generated supplemental data and assigns specific information to it. The metadata generated in step 2 is used as input. The terminal applies a clustering algorithm to classify the data into groups with similar properties. The output includes the classification results along with specific information assigned to each data point.

[0296] Step 4:

[0297] The server detects and organizes unnecessary data. It uses the data categorized in step 3 as input. The server automatically identifies data deemed unnecessary based on its update frequency and ownership information. As output, identified unnecessary data is deleted or archived, leaving only important data.

[0298] Step 5:

[0299] The user visualizes the organized data on their device. The data organized in step 4 is used as input. Using visualization tools, the user intuitively identifies data patterns and trends. Visual graphs and charts are generated as output, making it easier for the user to understand the data.

[0300] 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.

[0301] This invention is a system that combines an emotion engine with a series of processes from data collection to visualization. This emotion engine analyzes the user's emotional state and uses the results to adjust the data visualization. The main components and examples of its operation are shown below.

[0302] The core function of this system begins with the server collecting data from multiple data sources. For example, this could include sales data and customer information held by a company. This data is temporarily stored on the server as foundational information for subsequent steps.

[0303] The server analyzes this data and generates metadata based on it. This metadata includes data attribute information and related tags, which are used in subsequent classification tasks. Furthermore, terminals use the metadata to classify data into appropriate categories and assign tags, facilitating data management and retrieval.

[0304] A distinctive feature of this invention lies in its emotion engine, which recognizes the user's emotions. While the user is using the system, the emotion engine analyzes the user's input and responses in real time to identify the user's emotional state. This analysis is then reflected in the visualization process. For example, if the user is feeling stressed, the visualization of that data is automatically adjusted to a more intuitive and simpler format.

[0305] The server automatically detects and organizes unnecessary data based on update frequency and owner information. This ensures efficient data management and quick access to important information.

[0306] Finally, users can access visualization tools from their devices to intuitively understand data that has been refined by the emotion engine. For example, when a user reviews sales data, the color palette and graph types are optimized according to the emotion engine's guidelines, improving readability.

[0307] In this way, the system can achieve highly efficient and secure data management while dynamically adapting to the user's emotions, and ultimately enhance the user's satisfaction.

[0308] The following describes the processing flow.

[0309] Step 1:

[0310] The server collects the necessary data from external data sources. For example, it extracts various forms of data such as sales data and customer information, and stores this in temporary storage.

[0311] Step 2:

[0312] The server analyzes the collected data, calculates the attributes of the data, and generates metadata. Natural language processing and machine learning algorithms are used for the analysis, thereby identifying the characteristics and structure of the data.

[0313] Step 3:

[0314] The terminal uses the generated metadata to classify the data into categories and assign appropriate tags. In this process, categories such as "sales", "customer information", and "market trends" are set to organize and streamline the data.

[0315] Step 4:

[0316] The server monitors the data in the database and detects unnecessary data. Data with low update frequency or unknown owners are targeted, and these data are automatically deleted or archived.

[0317] Step 5:

[0318] While the user operates the system, the emotion engine analyzes the user's input data and physiological reactions in real time. The analysis identifies the user's emotional state, and the results are reflected in the next process.

[0319] Step 6:

[0320] The device adjusts data visualizations based on the analysis results of the emotion engine. For example, if the user is experiencing stress, it changes the color palette of the graph to provide a simpler and easier-to-understand visualization. This approach facilitates an intuitive understanding of the data.

[0321] Step 7:

[0322] Ultimately, users review the adjusted data through their devices and make decisions based on the visualized information. This leads to faster and more accurate data-driven decision-making, improving work efficiency.

[0323] (Example 2)

[0324] 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".

[0325] Traditional data management systems have the drawback of not adequately optimizing the user experience because they visualize data without considering the user's emotional state. Furthermore, the lack of dynamic adjustments in efficient data classification and removal of unnecessary data makes it difficult to quickly understand information and access important data.

[0326] 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.

[0327] In this invention, the server includes means for collecting data, means for analyzing the data to generate metadata, and means for analyzing the user's emotional state and adjusting the data visualization based on the generated emotional information. This enables optimal data visualization according to the user's emotions, improving the user experience and allowing quick access to important information.

[0328] "Means of data collection" refers to a function that automatically retrieves necessary information from multiple sources and converts it into a format usable within the system.

[0329] "Methods for analyzing data and generating metadata" refers to the process of analyzing collected information, extracting related attribute information and tags, and generating additional information to facilitate management.

[0330] "Methods for classifying and tagging data based on metadata" refers to methods of using generated additional information to divide data into specific categories and attach sticky notes to streamline management and retrieval.

[0331] "Methods for analyzing a user's emotional state" refer to technologies that analyze a user's input, reactions, facial expressions, etc., to determine their psychological state.

[0332] "Means of adjusting data visualization based on emotional information" refers to a function that changes the method of presenting information and the display style according to the analyzed emotional results, thereby improving user comprehension.

[0333] "Methods for detecting and organizing unnecessary data" refers to a system that automatically identifies and organizes infrequently used information based on its retention period and importance.

[0334] "Methods for visualizing organized data" refer to technologies that present managed information in the form of graphs and charts that users can easily understand.

[0335] This invention relates to a data management system that analyzes the user's emotional state and dynamically adjusts data visualization based on that analysis. This system can optimize the user experience throughout the entire process, from data collection to visualization. The specific implementation of this system is described below.

[0336] The server retrieves data from multiple sources. Specifically, it uses APIs to collect company sales and customer information and temporarily stores it on the server. Next, the server analyzes the collected data using the Python Pandas library and generates metadata, including information attributes and related tags. Using this metadata, the terminal classifies the data into categories and manages it efficiently through an SQL database.

[0337] To analyze the user's emotional state, the system uses a machine learning model (for example, a CNN model using TensorFlow). It analyzes data collected from the user's webcam and input devices to determine emotions from the user's facial expressions and behavior. The results of the analysis are reflected in the data visualization. The server uses the D3.js library to present the data in the most appropriate format corresponding to the user's emotions. For example, a simple bar graph is displayed for users experiencing stress.

[0338] The system also automatically detects and organizes unnecessary data based on its usage frequency and ownership information. This feature enables more efficient data storage and quicker access to important information. Finally, users can easily understand visualized data by interacting with an interactive dashboard via their device.

[0339] For example, when a user views sales data, the emotion engine will change the graph visualization if it detects a stress level. An example prompt would be, "How can I automatically adjust the sales data visualization based on the user's emotions?"

[0340] Thus, the embodiments of the present invention enable data presentation that responds to the user's emotions, providing a system that improves both data management and user experience.

[0341] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0342] Step 1:

[0343] The server collects data from multiple sources via APIs. It sends requests containing API keys and authentication information as input, retrieving company sales data and customer information. The retrieved data is stored in JSON or CSV format. This process ensures that the necessary information is incorporated into the system.

[0344] Step 2:

[0345] The server uses the Pandas library to analyze the collected data. It reads the data stored as input and performs data cleaning, such as checking for missing values ​​and removing duplicate data. As a result of the analysis, metadata is generated based on the data's attribute information and relationships. This metadata streamlines data management.

[0346] Step 3:

[0347] The terminal utilizes the generated metadata to categorize the data. Metadata is used as input, and SQL commands are used to categorize the information in the database. The categorized data is tagged, making it easy to search.

[0348] Step 4:

[0349] The user accesses the system and manipulates data through their terminal. During this process, the emotion engine acquires data from the user's webcam and input devices. Using the user's facial expressions and operation patterns as input, a generative AI model is employed to analyze their emotional state in real time. The user's emotional state is then determined as output.

[0350] Step 5:

[0351] The server uses the D3.js library to visualize data based on the user's emotional state. Input includes analyzed emotional states and classified data, which are used to dynamically adjust the visualization style. The output is data visualized in an optimal format for each emotion.

[0352] Step 6:

[0353] The server periodically detects and organizes unnecessary data based on its usage frequency and owner information. Using access logs and owner metadata from the database as input, it compresses or deletes data that has not been used for a long period. This improves storage efficiency.

[0354] Step 7:

[0355] Users view visualized data through their devices and examine sales data trends as a concrete example. Depending on the sentiment analysis results, information may be presented in a simple display format, making it easier for users to intuitively understand the data.

[0356] (Application Example 2)

[0357] 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."

[0358] Modern information processing systems require the effective management and utilization of vast amounts of data. However, conventional systems lack the flexibility to provide data that responds to user emotions and states, leading to problems such as reduced user experience and operational efficiency. Therefore, there is a need for means to achieve dynamic data visualization and management that takes user emotions into account.

[0359] 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.

[0360] In this invention, the server includes means for collecting data, means for collecting and analyzing emotional data, and means for adaptively adjusting the visualization based on the emotional data. This enables dynamic and effective visualization of data in accordance with the user's emotional state.

[0361] "Means of data collection" refers to the process or function of obtaining necessary information from networks and devices.

[0362] "Means for generating metadata" refers to a process or apparatus for analyzing collected information and generating supplementary information that explicitly indicates its attributes, relationships, etc.

[0363] "Means of classifying and tagging data" refers to the process of dividing information into specific categories and assigning identifiable tags based on the generated metadata.

[0364] "Methods for detecting and organizing unnecessary data" refers to a process of judging the value of information based on its update frequency and attributes, and eliminating and organizing the unnecessary parts.

[0365] "Means of visualization" refers to a process or device for displaying organized information in a format that is easily understandable to the user.

[0366] "Means for collecting and analyzing emotional data" refers to the process or technology of acquiring information from facial expressions and behaviors and analyzing it in order to understand the emotional state of a user.

[0367] "Means of adaptively adjusting visualization" refers to a process that dynamically changes the format and content of the displayed data to suit the individual user's state, based on the analyzed emotional information.

[0368] In realizing this system, the server, terminals, and users each play crucial roles. The server collects data from the network and generates metadata from it. This metadata includes attribute information and tags for each data item and is sent to the terminals for subsequent data classification and tagging tasks.

[0369] The server also collects and analyzes user emotion data. This involves using technologies that capture and analyze the user's emotional state in real time, such as facial recognition cameras and voice analysis systems. This information is analyzed on the server and used to dynamically adjust data visualizations based on the user's emotions. This process utilizes emotion analysis algorithms built with programming languages ​​such as Python, and JavaScript libraries such as D3.js for data visualization.

[0370] The terminal acts as the interface with the user, displaying visualization results sent from the server. The terminal, such as smart glasses, presents information in a format suitable for the user. In this process, an emotion engine is used to customize the information according to the user's state. For example, if the user is relaxed, detailed product information can be displayed.

[0371] This system is designed for use in physical stores and can analyze customer sentiment in real time as they browse the store, providing them with relevant information on products and sales that interest them. This is expected to improve the user experience.

[0372] As a concrete example, when a user approaches a specific product shelf in a store, information about the products on that shelf is displayed on their smart glasses. When the user is feeling stressed, only simple and intuitive information is displayed, providing a comfortable shopping experience tailored to their emotions.

[0373] Examples of prompts for the generating AI model include, "If the user is relaxed, we will display bonus points information and products with relaxing effects," and "If the user is excited, we will suggest new products that stimulate adrenaline."

[0374] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0375] Step 1:

[0376] The server collects data from the network. It takes information from external data sources as input and generates metadata based on this. This metadata includes data attribute information and related tags, which are used in subsequent processing. The output is metadata that forms the basis for classification and tagging.

[0377] Step 2:

[0378] The server collects and analyzes user emotional data using emotion recognition cameras and voice input devices. It acquires user facial expressions and voice data as input and applies an emotion analysis algorithm built in Python to identify the user's emotional state. The analysis results are output, and this data is used to refine the visualization process.

[0379] Step 3:

[0380] The server classifies and tags data based on the generated metadata. It uses metadata as input, parses it, categorizes and tags it, and organizes it in a manageable format. The output is structured data suitable for searching and visualization.

[0381] Step 4:

[0382] The terminal dynamically adjusts the data visualization based on the sentiment analysis results sent from the server. Here, it receives sentiment data and structured data as input and displays them to the user in a visually understandable format using the JavaScript library D3.js. The output is visualized data adapted to the user's emotional state.

[0383] Step 5:

[0384] Users access in-store information through smart glasses. This action allows them to make decisions based on the data displayed on the device. Visualized information is received as input, which is then used to inform purchasing decisions and product selections. The output is improved user experience and increased satisfaction.

[0385] Step 6:

[0386] The device then sends user feedback and new sentiment data back to the server. This step improves data accuracy by collecting the user's latest sentiment state and operation history as input and transferring it to the server. The output is more accurate sentiment recognition and data visualization.

[0387] 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.

[0388] 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.

[0389] 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.

[0390] [Third Embodiment]

[0391] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0392] 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.

[0393] 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).

[0394] 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.

[0395] 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.

[0396] 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).

[0397] 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.

[0398] 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.

[0399] 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.

[0400] 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.

[0401] 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.

[0402] 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".

[0403] The system of the present invention includes a series of processes that automatically perform data collection, analysis, metadata generation, data classification and tagging, detection and removal of unnecessary data, and visualization. This makes it possible to significantly improve the efficiency of data management. The following specifically describes examples of each means and their operation.

[0404] Within this system, the server first collects necessary data from external data sources, such as databases and APIs. The collected data is diverse and includes various types of information related to business activities, such as sales information, customer information, and inventory information. This collected data is temporarily stored and then passed on to the next processing step.

[0405] Next, the server analyzes the collected data. This analysis is performed using natural language processing, machine learning algorithms, or statistical analysis techniques, with the aim of revealing the data's attributes and generating metadata based on them. For example, in the case of sales data, attributes such as the date, customer ID, and sales amount are extracted.

[0406] Furthermore, the device automatically classifies the data using this generated metadata and assigns appropriate tags. This classification is performed according to the purpose of the data's use, and tagging improves the efficiency of data searching and re-editing. For example, sales data is classified into categories such as "Sales," "By Customer," and "By Period."

[0407] The server then detects unnecessary data. Criteria for unnecessary data include infrequent updates and lack of owner information. Detected unnecessary data is automatically deleted or archived to separate storage based on pre-configured policies.

[0408] Finally, users can view the organized data through visualization tools on their devices. This allows them to intuitively obtain the information necessary for business management, such as understanding sales patterns and customer trends through graphs.

[0409] By operating such a system, the accuracy and efficiency of data management will improve, and human resources will be able to be allocated to higher value-added activities.

[0410] The following describes the processing flow.

[0411] Step 1:

[0412] The server collects data from pre-configured data sources (e.g., databases or APIs). This includes sales information and customer data, which are collected periodically or based on trigger events and stored in temporary storage.

[0413] Step 2:

[0414] The server analyzes the collected data and extracts the attributes of each data point. Natural language processing and machine learning algorithms are used to identify the content of the data and organize it according to its attributes. This clarifies the background information of the data.

[0415] Step 3:

[0416] The server generates metadata based on the analyzed data. This metadata includes data type, generation date and time, and associated tags, and is used for data management and retrieval. The generated metadata is stored in a database.

[0417] Step 4:

[0418] The device uses metadata to categorize relevant data into specific categories. These categories could include "sales," "customer information," and "inventory management." Therefore, appropriate tags are assigned to the data to organize it effectively.

[0419] Step 5:

[0420] The server scans the existing database and detects unnecessary data. Based on criteria such as infrequently updated data or data lacking owner information, it automatically deletes or archives the data, enabling efficient data management.

[0421] Step 6:

[0422] Users can access visualization tools from their devices to view organized data in graphs and charts. This allows them to visually grasp sales trends and customer behavior, supporting data-driven decision-making.

[0423] (Example 1)

[0424] 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."

[0425] In modern information management, there is a demand for efficiently processing vast amounts of data and quickly extracting useful information. However, in conventional systems, the processes of data collection, analysis, classification, organization, and visualization are often performed separately, making centralized management difficult. As a result, information leaks and wasteful handling of data occur, leading to a decrease in the efficiency of data management.

[0426] 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.

[0427] In this invention, the server includes means for collecting information from a data source, means for analyzing the information to generate attached information, and means for classifying the information and assigning identification labels based on the attached information. This enables centralized management from efficient information collection to analysis, classification, organization, and visualization.

[0428] A "data source" refers to an external infrastructure or mechanism that provides information, and this includes databases and APIs.

[0429] "Means of collecting information" refers to methods and processes for obtaining useful information from data sources, and includes technologies aimed at automating and streamlining information acquisition.

[0430] "Analysis" refers to the process of evaluating collected information and extracting meaningful features and patterns from it, and is carried out using natural language processing and machine learning algorithms.

[0431] "Attached information" refers to attribute data derived from the analyzed information that explains or complements the original information.

[0432] "Classification" is the process of organizing information according to specific criteria and grouping information that has similar characteristics.

[0433] "Means of assigning identification labels" refers to the process of adding labels to classified information to facilitate identification, and plays a role in making searching and filtering more efficient.

[0434] "Information that has not been exchanged" refers to information that has not been updated and shows no signs of activity for a long period of time; deletion or archiving is usually recommended.

[0435] "Visual presentation methods" refer to the process of converting information into visual forms such as graphs and charts, and displaying them in a way that can be intuitively understood.

[0436] The embodiment of this invention aims to centrally perform a series of processes from data collection to analysis, classification, organization, and visualization. This system enables efficient data management through the respective roles of the server, terminal, and user.

[0437] The server collects information from external data sources. The hardware and software used at this stage include corporate databases and third-party APIs. The server accesses these data sources to retrieve a variety of data related to business activities, such as sales information, customer information, and inventory information.

[0438] Next, the server analyzes the collected information. This analysis process utilizes natural language processing tools and machine learning algorithms. Specific software examples include the Python libraries NLTK and TensorFlow. The server uses these tools to extract attributes from the information and generate attached information (metadata).

[0439] Subsequently, the terminal classifies the information based on the generated attachments and assigns identification labels. This classification and labeling process improves information organization and search efficiency. An example of software used is Elasticsearch, which enables rapid searching and filtering.

[0440] Finally, users are presented with organized information visually via their devices. Common visualization software such as Tableau and Power BI can be used as visualization tools. By leveraging these tools to graph and chart data, users can quickly obtain the information necessary for strategic decision-making within their business.

[0441] As a concrete example, a server retrieves sales data from an API and identifies customer segments through analysis. Next, the terminal assigns labels such as "high-frequency buyer" and "low-frequency buyer." Finally, the user uses a visualization tool to graph monthly sales trends, allowing them to visually understand when sales peak.

[0442] An example of an input prompt for a generating AI model is, "Visualize the purchase frequency and sales amount for each customer and output it as a graph."

[0443] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0444] Step 1:

[0445] The server collects information from data sources. The input is raw data obtained from APIs and databases. This data includes company sales, inventory, and customer information. The server periodically retrieves this data and stores it in temporary storage. Specifically, the server sends API requests and saves the received JSON-formatted response data to an SQL database. The output is the stored data in a structured format.

[0446] Step 2:

[0447] The server analyzes the data it has collected. The input is structured data collected and stored in the previous step. The server extracts features from the data using natural language processing tools and machine learning algorithms and generates metadata. Specifically, it processes the dataset using Python's NLTK and TensorFlow to analyze customer purchasing patterns and sales trends. The output is metadata with attribute information attached.

[0448] Step 3:

[0449] The terminal classifies information based on the generated metadata and assigns identification labels. The input is metadata obtained through analysis. The terminal uses a classification algorithm to divide the information into specific categories and assigns identification labels to facilitate searching and management. Specifically, it uses Elasticsearch to tag data into categories such as "Sales," "Monthly," and "Regional." The output is the classified and labeled information.

[0450] Step 4:

[0451] The server detects and organizes information that is not being exchanged. The input is categorized and labeled information. The server identifies unnecessary data based on update frequency and owner identification information, and deletes or archives it according to a pre-configured policy. Specifically, it runs a cleaning script and moves old data to backup storage. The output is organized information.

[0452] Step 5:

[0453] The system presents organized information visually to the user through their device. The input is organized information. The user uses visualization tools to graph the data and obtain information useful for business management. Specifically, it utilizes tools like Tableau and Power BI to visualize sales data as a time-series graph. The output is usable graphical data.

[0454] (Application Example 1)

[0455] 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."

[0456] Current data management systems require the efficient classification and processing of vast amounts of data, but organizing and visualizing that data remains a time-consuming task. Furthermore, there is a lack of means to automatically detect and manage unnecessary information within the data. It is necessary to address these challenges and improve the efficiency and accuracy of data management.

[0457] 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.

[0458] In this invention, the server includes means for acquiring data, means for analyzing the data to generate supplementary data, and means for classifying the data and assigning specific information based on the supplementary data. This enables efficient data management and rapid acquisition of necessary information.

[0459] "Means for acquiring data" refers to a device or method that has the function of gathering necessary information from external sources.

[0460] "Supplemental data" refers to data that is generated by analyzing acquired information, indicating attributes and characteristics that make the original information more useful.

[0461] "Means for classification and assigning specific information" refers to methods for grouping information based on analyzed data and attaching related specific information or tags.

[0462] "Means for detecting and organizing unnecessary data" refers to a process or device for identifying, organizing, or deleting unnecessary information from a given set of pieces of data.

[0463] "Means of visualization" refer to methods and techniques for visually representing organized information, with the aim of promoting understanding.

[0464] A "clustering algorithm" is a mathematical or statistical method for automatically classifying a group of information into different groups.

[0465] "Means for adding specific risk information" refers to a method or apparatus for analyzing information, evaluating the degree of risk, and adding predetermined risk information to each piece of information.

[0466] The system implementing this invention employs a method in which a server, terminal, and user cooperate to efficiently manage information.

[0467] First, the server retrieves information from external sources. These sources include databases and APIs, allowing for the collection of a wide variety of information. The collected information is temporarily stored and then passed on to subsequent processing steps.

[0468] Next, the server analyzes the collected information. This analysis utilizes natural language processing, machine learning algorithms, and statistical analysis techniques, which in turn generates supplementary data. For example, this involves extracting the characteristics of the information and organizing them as metadata.

[0469] Next, the terminal automatically classifies the information based on the generated supplemental data and assigns specific information. This classification is performed using a clustering algorithm, and the information is grouped based on similarity. At the same time, specific information such as risk information is added, improving the efficiency of management.

[0470] Furthermore, the server detects and organizes unnecessary information. Criteria for determining unnecessary information include its update frequency. This information is automatically deleted or archived to separate storage.

[0471] Finally, users can view the organized information through visualization tools on their devices. This allows them to intuitively grasp the necessary information and, for example, visually understand inventory surpluses or shortages in a logistics center.

[0472] As a concrete example, in inventory management at a logistics center, this system can be used to quickly identify which products are in excess inventory and which products have a fast turnover. An example of a prompt to the generated AI model is, "Analyze the inventory data from the logistics center and classify and visualize high-risk and low-risk inventory using clustering."

[0473] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0474] Step 1:

[0475] The server retrieves data from external sources. It receives information provided by databases and APIs as input. The server sends queries to these sources to collect the necessary information. The resulting dataset is then temporarily stored in storage.

[0476] Step 2:

[0477] The server analyzes the collected data. It uses the dataset obtained in Step 1 as input. The server uses natural language processing and machine learning algorithms to analyze the data's attributes and trends, and generates supplementary data (metadata). The generated metadata is then passed to the next processing step.

[0478] Step 3:

[0479] The terminal classifies the data using the generated supplemental data and assigns specific information to it. The metadata generated in step 2 is used as input. The terminal applies a clustering algorithm to classify the data into groups with similar properties. The output includes the classification results along with specific information assigned to each data point.

[0480] Step 4:

[0481] The server detects and organizes unnecessary data. It uses the data categorized in step 3 as input. The server automatically identifies data deemed unnecessary based on its update frequency and ownership information. As output, identified unnecessary data is deleted or archived, leaving only important data.

[0482] Step 5:

[0483] The user visualizes the organized data on their device. The data organized in step 4 is used as input. Using visualization tools, the user intuitively identifies data patterns and trends. Visual graphs and charts are generated as output, making it easier for the user to understand the data.

[0484] 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.

[0485] This invention is a system that combines an emotion engine with a series of processes from data collection to visualization. This emotion engine analyzes the user's emotional state and uses the results to adjust the data visualization. The main components and examples of its operation are shown below.

[0486] The core function of this system begins with the server collecting data from multiple data sources. For example, this could include sales data and customer information held by a company. This data is temporarily stored on the server as foundational information for subsequent steps.

[0487] The server analyzes this data and generates metadata based on it. This metadata includes data attribute information and related tags, which are used in subsequent classification tasks. Furthermore, terminals use the metadata to classify data into appropriate categories and assign tags, facilitating data management and retrieval.

[0488] A distinctive feature of this invention lies in its emotion engine, which recognizes the user's emotions. While the user is using the system, the emotion engine analyzes the user's input and responses in real time to identify the user's emotional state. This analysis is then reflected in the visualization process. For example, if the user is feeling stressed, the visualization of that data is automatically adjusted to a more intuitive and simpler format.

[0489] The server automatically detects and organizes unnecessary data based on update frequency and owner information. This ensures efficient data management and quick access to important information.

[0490] Finally, users can access visualization tools from their devices to intuitively understand data that has been refined by the emotion engine. For example, when a user reviews sales data, the color palette and graph types are optimized according to the emotion engine's guidelines, improving readability.

[0491] In this way, this system dynamically adapts to user emotions, achieving highly efficient and secure data management, and ultimately increasing user satisfaction.

[0492] The following describes the processing flow.

[0493] Step 1:

[0494] The server collects necessary data from external data sources. For example, it extracts data in various formats, such as sales data and customer information, and stores it in temporary storage.

[0495] Step 2:

[0496] The server analyzes the collected data, calculates its attributes, and generates metadata. Natural language processing and machine learning algorithms are used for the analysis, which identifies the characteristics and structure of the data.

[0497] Step 3:

[0498] The terminal uses the generated metadata to categorize the data and assign appropriate tags. In this process, categories such as "sales," "customer information," and "market trends" are set, and the data is organized and tidied up.

[0499] Step 4:

[0500] The server monitors the data in the database and detects unnecessary data. This includes data that is not frequently updated or whose owner is unknown, and this data is automatically deleted or archived.

[0501] Step 5:

[0502] While the user interacts with the system, the emotion engine analyzes the user's input data and physiological responses in real time. This analysis identifies the user's emotional state, and the results are then reflected in the next process.

[0503] Step 6:

[0504] The device adjusts data visualizations based on the analysis results of the emotion engine. For example, if the user is experiencing stress, it changes the color palette of the graph to provide a simpler and easier-to-understand visualization. This approach facilitates an intuitive understanding of the data.

[0505] Step 7:

[0506] Ultimately, users review the adjusted data through their devices and make decisions based on the visualized information. This leads to faster and more accurate data-driven decision-making, improving work efficiency.

[0507] (Example 2)

[0508] 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."

[0509] Traditional data management systems have the drawback of not adequately optimizing the user experience because they visualize data without considering the user's emotional state. Furthermore, the lack of dynamic adjustments in efficient data classification and removal of unnecessary data makes it difficult to quickly understand information and access important data.

[0510] 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.

[0511] In this invention, the server includes means for collecting data, means for analyzing the data to generate metadata, and means for analyzing the user's emotional state and adjusting the data visualization based on the generated emotional information. This enables optimal data visualization according to the user's emotions, improving the user experience and allowing quick access to important information.

[0512] "Means of data collection" refers to a function that automatically retrieves necessary information from multiple sources and converts it into a format usable within the system.

[0513] "Methods for analyzing data and generating metadata" refers to the process of analyzing collected information, extracting related attribute information and tags, and generating additional information to facilitate management.

[0514] "Methods for classifying and tagging data based on metadata" refers to methods of using generated additional information to divide data into specific categories and attach sticky notes to streamline management and retrieval.

[0515] "Methods for analyzing a user's emotional state" refer to technologies that analyze a user's input, reactions, facial expressions, etc., to determine their psychological state.

[0516] "Means of adjusting data visualization based on emotional information" refers to a function that changes the method of presenting information and the display style according to the analyzed emotional results, thereby improving user comprehension.

[0517] "Methods for detecting and organizing unnecessary data" refers to a system that automatically identifies and organizes infrequently used information based on its retention period and importance.

[0518] "Methods for visualizing organized data" refer to technologies that present managed information in the form of graphs and charts that users can easily understand.

[0519] This invention relates to a data management system that analyzes the user's emotional state and dynamically adjusts data visualization based on that analysis. This system can optimize the user experience throughout the entire process, from data collection to visualization. The specific implementation of this system is described below.

[0520] The server retrieves data from multiple sources. Specifically, it uses APIs to collect company sales and customer information and temporarily stores it on the server. Next, the server analyzes the collected data using the Python Pandas library and generates metadata, including information attributes and related tags. Using this metadata, the terminal classifies the data into categories and manages it efficiently through an SQL database.

[0521] To analyze the user's emotional state, the system uses a machine learning model (for example, a CNN model using TensorFlow). It analyzes data collected from the user's webcam and input devices to determine emotions from the user's facial expressions and behavior. The results of the analysis are reflected in the data visualization. The server uses the D3.js library to present the data in the most appropriate format corresponding to the user's emotions. For example, a simple bar graph is displayed for users experiencing stress.

[0522] The system also automatically detects and organizes unnecessary data based on its usage frequency and ownership information. This feature enables more efficient data storage and quicker access to important information. Finally, users can easily understand visualized data by interacting with an interactive dashboard via their device.

[0523] For example, when a user views sales data, the emotion engine will change the graph visualization if it detects a stress level. An example prompt would be, "How can I automatically adjust the sales data visualization based on the user's emotions?"

[0524] Thus, the embodiments of the present invention enable data presentation that responds to the user's emotions, providing a system that improves both data management and user experience.

[0525] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0526] Step 1:

[0527] The server collects data from multiple sources via APIs. It sends requests containing API keys and authentication information as input, retrieving company sales data and customer information. The retrieved data is stored in JSON or CSV format. This process ensures that the necessary information is incorporated into the system.

[0528] Step 2:

[0529] The server uses the Pandas library to analyze the collected data. It reads the data stored as input and performs data cleaning, such as checking for missing values ​​and removing duplicate data. As a result of the analysis, metadata is generated based on the data's attribute information and relationships. This metadata streamlines data management.

[0530] Step 3:

[0531] The terminal utilizes the generated metadata to categorize the data. Metadata is used as input, and SQL commands are used to categorize the information in the database. The categorized data is tagged, making it easy to search.

[0532] Step 4:

[0533] The user accesses the system and manipulates data through their terminal. During this process, the emotion engine acquires data from the user's webcam and input devices. Using the user's facial expressions and operation patterns as input, a generative AI model is employed to analyze their emotional state in real time. The user's emotional state is then determined as output.

[0534] Step 5:

[0535] The server uses the D3.js library to visualize data based on the user's emotional state. Input includes analyzed emotional states and classified data, which are used to dynamically adjust the visualization style. The output is data visualized in an optimal format for each emotion.

[0536] Step 6:

[0537] The server periodically detects and organizes unnecessary data based on its usage frequency and owner information. Using access logs and owner metadata from the database as input, it compresses or deletes data that has not been used for a long period. This improves storage efficiency.

[0538] Step 7:

[0539] Users view visualized data through their devices and examine sales data trends as a concrete example. Depending on the sentiment analysis results, information may be presented in a simple display format, making it easier for users to intuitively understand the data.

[0540] (Application Example 2)

[0541] 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."

[0542] Modern information processing systems require the effective management and utilization of vast amounts of data. However, conventional systems lack the flexibility to provide data that responds to user emotions and states, leading to problems such as reduced user experience and operational efficiency. Therefore, there is a need for means to achieve dynamic data visualization and management that takes user emotions into account.

[0543] 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.

[0544] In this invention, the server includes means for collecting data, means for collecting and analyzing emotional data, and means for adaptively adjusting the visualization based on the emotional data. This enables dynamic and effective visualization of data in accordance with the user's emotional state.

[0545] "Means of data collection" refers to the process or function of obtaining necessary information from networks and devices.

[0546] "Means for generating metadata" refers to a process or apparatus for analyzing collected information and generating supplementary information that explicitly indicates its attributes, relationships, etc.

[0547] "Means of classifying and tagging data" refers to the process of dividing information into specific categories and assigning identifiable tags based on the generated metadata.

[0548] "Methods for detecting and organizing unnecessary data" refers to a process of judging the value of information based on its update frequency and attributes, and eliminating and organizing the unnecessary parts.

[0549] "Means of visualization" refers to a process or device for displaying organized information in a format that is easily understandable to the user.

[0550] "Means for collecting and analyzing emotional data" refers to the process or technology of acquiring information from facial expressions and behaviors and analyzing it in order to understand the emotional state of a user.

[0551] "Means of adaptively adjusting visualization" refers to a process that dynamically changes the format and content of the displayed data to suit the individual user's state, based on the analyzed emotional information.

[0552] In realizing this system, the server, terminals, and users each play crucial roles. The server collects data from the network and generates metadata from it. This metadata includes attribute information and tags for each data item and is sent to the terminals for subsequent data classification and tagging tasks.

[0553] The server also collects and analyzes user emotion data. This involves using technologies that capture and analyze the user's emotional state in real time, such as facial recognition cameras and voice analysis systems. This information is analyzed on the server and used to dynamically adjust data visualizations based on the user's emotions. This process utilizes emotion analysis algorithms built with programming languages ​​such as Python, and JavaScript libraries such as D3.js for data visualization.

[0554] The terminal acts as the interface with the user, displaying visualization results sent from the server. The terminal, such as smart glasses, presents information in a format suitable for the user. In this process, an emotion engine is used to customize the information according to the user's state. For example, if the user is relaxed, detailed product information can be displayed.

[0555] This system is designed for use in physical stores and can analyze customer sentiment in real time as they browse the store, providing them with relevant information on products and sales that interest them. This is expected to improve the user experience.

[0556] As a concrete example, when a user approaches a specific product shelf in a store, information about the products on that shelf is displayed on their smart glasses. When the user is feeling stressed, only simple and intuitive information is displayed, providing a comfortable shopping experience tailored to their emotions.

[0557] Examples of prompts for the generating AI model include, "If the user is relaxed, we will display bonus points information and products with relaxing effects," and "If the user is excited, we will suggest new products that stimulate adrenaline."

[0558] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0559] Step 1:

[0560] The server collects data from the network. It takes information from external data sources as input and generates metadata based on this. This metadata includes data attribute information and related tags, which are used in subsequent processing. The output is metadata that forms the basis for classification and tagging.

[0561] Step 2:

[0562] The server collects and analyzes user emotional data using emotion recognition cameras and voice input devices. It acquires user facial expressions and voice data as input and applies an emotion analysis algorithm built in Python to identify the user's emotional state. The analysis results are output, and this data is used to refine the visualization process.

[0563] Step 3:

[0564] The server classifies and tags data based on the generated metadata. It uses metadata as input, parses it, categorizes and tags it, and organizes it in a manageable format. The output is structured data suitable for searching and visualization.

[0565] Step 4:

[0566] The terminal dynamically adjusts the data visualization based on the sentiment analysis results sent from the server. Here, it receives sentiment data and structured data as input and displays them to the user in a visually understandable format using the JavaScript library D3.js. The output is visualized data adapted to the user's emotional state.

[0567] Step 5:

[0568] Users access in-store information through smart glasses. This action allows them to make decisions based on the data displayed on the device. Visualized information is received as input, which is then used to inform purchasing decisions and product selections. The output is improved user experience and increased satisfaction.

[0569] Step 6:

[0570] The device then sends user feedback and new sentiment data back to the server. This step improves data accuracy by collecting the user's latest sentiment state and operation history as input and transferring it to the server. The output is more accurate sentiment recognition and data visualization.

[0571] 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.

[0572] 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.

[0573] 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.

[0574] [Fourth Embodiment]

[0575] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0576] 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.

[0577] 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).

[0578] 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.

[0579] 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.

[0580] 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).

[0581] 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.

[0582] 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.

[0583] 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.

[0584] 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.

[0585] 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.

[0586] 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.

[0587] 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".

[0588] The system of the present invention includes a series of processes that automatically perform data collection, analysis, metadata generation, data classification and tagging, detection and removal of unnecessary data, and visualization. This makes it possible to significantly improve the efficiency of data management. The following specifically describes examples of each means and their operation.

[0589] Within this system, the server first collects necessary data from external data sources, such as databases and APIs. The collected data is diverse and includes various types of information related to business activities, such as sales information, customer information, and inventory information. This collected data is temporarily stored and then passed on to the next processing step.

[0590] Next, the server analyzes the collected data. This analysis is performed using natural language processing, machine learning algorithms, or statistical analysis techniques, with the aim of revealing the data's attributes and generating metadata based on them. For example, in the case of sales data, attributes such as the date, customer ID, and sales amount are extracted.

[0591] Furthermore, the device automatically classifies the data using this generated metadata and assigns appropriate tags. This classification is performed according to the purpose of the data's use, and tagging improves the efficiency of data searching and re-editing. For example, sales data is classified into categories such as "Sales," "By Customer," and "By Period."

[0592] The server then detects unnecessary data. Criteria for unnecessary data include infrequent updates and lack of owner information. Detected unnecessary data is automatically deleted or archived to separate storage based on pre-configured policies.

[0593] Finally, users can view the organized data through visualization tools on their devices. This allows them to intuitively obtain the information necessary for business management, such as understanding sales patterns and customer trends through graphs.

[0594] By operating such a system, the accuracy and efficiency of data management will improve, and human resources will be able to be allocated to higher value-added activities.

[0595] The following describes the processing flow.

[0596] Step 1:

[0597] The server collects data from pre-configured data sources (e.g., databases or APIs). This includes sales information and customer data, which are collected periodically or based on trigger events and stored in temporary storage.

[0598] Step 2:

[0599] The server analyzes the collected data and extracts the attributes of each data point. Natural language processing and machine learning algorithms are used to identify the content of the data and organize it according to its attributes. This clarifies the background information of the data.

[0600] Step 3:

[0601] The server generates metadata based on the analyzed data. This metadata includes data type, generation date and time, and associated tags, and is used for data management and retrieval. The generated metadata is stored in a database.

[0602] Step 4:

[0603] The device uses metadata to categorize relevant data into specific categories. These categories could include "sales," "customer information," and "inventory management." Therefore, appropriate tags are assigned to the data to organize it effectively.

[0604] Step 5:

[0605] The server scans the existing database and detects unnecessary data. Based on criteria such as infrequently updated data or data lacking owner information, it automatically deletes or archives the data, enabling efficient data management.

[0606] Step 6:

[0607] Users can access visualization tools from their devices to view organized data in graphs and charts. This allows them to visually grasp sales trends and customer behavior, supporting data-driven decision-making.

[0608] (Example 1)

[0609] 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".

[0610] In modern information management, there is a demand for efficiently processing vast amounts of data and quickly extracting useful information. However, in conventional systems, the processes of data collection, analysis, classification, organization, and visualization are often performed separately, making centralized management difficult. As a result, information leaks and wasteful handling of data occur, leading to a decrease in the efficiency of data management.

[0611] 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.

[0612] In this invention, the server includes means for collecting information from a data source, means for analyzing the information to generate attached information, and means for classifying the information and assigning identification labels based on the attached information. This enables centralized management from efficient information collection to analysis, classification, organization, and visualization.

[0613] A "data source" refers to an external infrastructure or mechanism that provides information, and this includes databases and APIs.

[0614] "Means of collecting information" refers to methods and processes for obtaining useful information from data sources, and includes technologies aimed at automating and streamlining information acquisition.

[0615] "Analysis" refers to the process of evaluating collected information and extracting meaningful features and patterns from it, and is carried out using natural language processing and machine learning algorithms.

[0616] "Attached information" refers to attribute data derived from the analyzed information that explains or complements the original information.

[0617] "Classification" is the process of organizing information according to specific criteria and grouping information that has similar characteristics.

[0618] "Means of assigning identification labels" refers to the process of adding labels to classified information to facilitate identification, and plays a role in making searching and filtering more efficient.

[0619] "Information that has not been exchanged" refers to information that has not been updated and shows no signs of activity for a long period of time; deletion or archiving is usually recommended.

[0620] "Visual presentation methods" refer to the process of converting information into visual forms such as graphs and charts, and displaying them in a way that can be intuitively understood.

[0621] The embodiment of this invention aims to centrally perform a series of processes from data collection to analysis, classification, organization, and visualization. This system enables efficient data management through the respective roles of the server, terminal, and user.

[0622] The server collects information from external data sources. The hardware and software used at this stage include corporate databases and third-party APIs. The server accesses these data sources to retrieve a variety of data related to business activities, such as sales information, customer information, and inventory information.

[0623] Next, the server analyzes the collected information. This analysis process utilizes natural language processing tools and machine learning algorithms. Specific software examples include the Python libraries NLTK and TensorFlow. The server uses these tools to extract attributes from the information and generate attached information (metadata).

[0624] Subsequently, the terminal classifies the information based on the generated attachments and assigns identification labels. This classification and labeling process improves information organization and search efficiency. An example of software used is Elasticsearch, which enables rapid searching and filtering.

[0625] Finally, users are presented with organized information visually via their devices. Common visualization software such as Tableau and Power BI can be used as visualization tools. By leveraging these tools to graph and chart data, users can quickly obtain the information necessary for strategic decision-making within their business.

[0626] As a concrete example, a server retrieves sales data from an API and identifies customer segments through analysis. Next, the terminal assigns labels such as "high-frequency buyer" and "low-frequency buyer." Finally, the user uses a visualization tool to graph monthly sales trends, allowing them to visually understand when sales peak.

[0627] An example of an input prompt for a generating AI model is, "Visualize the purchase frequency and sales amount for each customer and output it as a graph."

[0628] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0629] Step 1:

[0630] The server collects information from data sources. The input is raw data obtained from APIs and databases. This data includes company sales, inventory, and customer information. The server periodically retrieves this data and stores it in temporary storage. Specifically, the server sends API requests and saves the received JSON-formatted response data to an SQL database. The output is the stored data in a structured format.

[0631] Step 2:

[0632] The server analyzes the data it has collected. The input is structured data collected and stored in the previous step. The server extracts features from the data using natural language processing tools and machine learning algorithms and generates metadata. Specifically, it processes the dataset using Python's NLTK and TensorFlow to analyze customer purchasing patterns and sales trends. The output is metadata with attribute information attached.

[0633] Step 3:

[0634] The terminal classifies information based on the generated metadata and assigns identification labels. The input is metadata obtained through analysis. The terminal uses a classification algorithm to divide the information into specific categories and assigns identification labels to facilitate searching and management. Specifically, it uses Elasticsearch to tag data into categories such as "Sales," "Monthly," and "Regional." The output is the classified and labeled information.

[0635] Step 4:

[0636] The server detects and organizes information that is not being exchanged. The input is categorized and labeled information. The server identifies unnecessary data based on update frequency and owner identification information, and deletes or archives it according to a pre-configured policy. Specifically, it runs a cleaning script and moves old data to backup storage. The output is organized information.

[0637] Step 5:

[0638] The system presents organized information visually to the user through their device. The input is organized information. The user uses visualization tools to graph the data and obtain information useful for business management. Specifically, it utilizes tools like Tableau and Power BI to visualize sales data as a time-series graph. The output is usable graphical data.

[0639] (Application Example 1)

[0640] 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".

[0641] Current data management systems require the efficient classification and processing of vast amounts of data, but organizing and visualizing that data remains a time-consuming task. Furthermore, there is a lack of means to automatically detect and manage unnecessary information within the data. It is necessary to address these challenges and improve the efficiency and accuracy of data management.

[0642] 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.

[0643] In this invention, the server includes means for acquiring data, means for analyzing the data to generate supplementary data, and means for classifying the data and assigning specific information based on the supplementary data. This enables efficient data management and rapid acquisition of necessary information.

[0644] "Means for acquiring data" refers to a device or method that has the function of gathering necessary information from external sources.

[0645] "Supplemental data" refers to data that is generated by analyzing acquired information, indicating attributes and characteristics that make the original information more useful.

[0646] "Means for classification and assigning specific information" refers to methods for grouping information based on analyzed data and attaching related specific information or tags.

[0647] "Means for detecting and organizing unnecessary data" refers to a process or device for identifying, organizing, or deleting unnecessary information from a given set of pieces of data.

[0648] "Means of visualization" refer to methods and techniques for visually representing organized information, with the aim of promoting understanding.

[0649] A "clustering algorithm" is a mathematical or statistical method for automatically classifying a group of information into different groups.

[0650] "Means for adding specific risk information" refers to a method or apparatus for analyzing information, evaluating the degree of risk, and adding predetermined risk information to each piece of information.

[0651] The system implementing this invention employs a method in which a server, terminal, and user cooperate to efficiently manage information.

[0652] First, the server retrieves information from external sources. These sources include databases and APIs, allowing for the collection of a wide variety of information. The collected information is temporarily stored and then passed on to subsequent processing steps.

[0653] Next, the server analyzes the collected information. This analysis utilizes natural language processing, machine learning algorithms, and statistical analysis techniques, which in turn generates supplementary data. For example, this involves extracting the characteristics of the information and organizing them as metadata.

[0654] Next, the terminal automatically classifies the information based on the generated supplemental data and assigns specific information. This classification is performed using a clustering algorithm, and the information is grouped based on similarity. At the same time, specific information such as risk information is added, improving the efficiency of management.

[0655] Furthermore, the server detects and organizes unnecessary information. Criteria for determining unnecessary information include its update frequency. This information is automatically deleted or archived to separate storage.

[0656] Finally, users can view the organized information through visualization tools on their devices. This allows them to intuitively grasp the necessary information and, for example, visually understand inventory surpluses or shortages in a logistics center.

[0657] As a concrete example, in inventory management at a logistics center, this system can be used to quickly identify which products are in excess inventory and which products have a fast turnover. An example of a prompt to the generated AI model is, "Analyze the inventory data from the logistics center and classify and visualize high-risk and low-risk inventory using clustering."

[0658] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0659] Step 1:

[0660] The server retrieves data from external sources. It receives information provided by databases and APIs as input. The server sends queries to these sources to collect the necessary information. The resulting dataset is then temporarily stored in storage.

[0661] Step 2:

[0662] The server analyzes the collected data. It uses the dataset obtained in Step 1 as input. The server uses natural language processing and machine learning algorithms to analyze the data's attributes and trends, and generates supplementary data (metadata). The generated metadata is then passed to the next processing step.

[0663] Step 3:

[0664] The terminal classifies the data using the generated supplemental data and assigns specific information to it. The metadata generated in step 2 is used as input. The terminal applies a clustering algorithm to classify the data into groups with similar properties. The output includes the classification results along with specific information assigned to each data point.

[0665] Step 4:

[0666] The server detects and organizes unnecessary data. It uses the data categorized in step 3 as input. The server automatically identifies data deemed unnecessary based on its update frequency and ownership information. As output, identified unnecessary data is deleted or archived, leaving only important data.

[0667] Step 5:

[0668] The user visualizes the organized data on their device. The data organized in step 4 is used as input. Using visualization tools, the user intuitively identifies data patterns and trends. Visual graphs and charts are generated as output, making it easier for the user to understand the data.

[0669] 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.

[0670] This invention is a system that combines an emotion engine with a series of processes from data collection to visualization. This emotion engine analyzes the user's emotional state and uses the results to adjust the data visualization. The main components and examples of its operation are shown below.

[0671] The core function of this system begins with the server collecting data from multiple data sources. For example, this could include sales data and customer information held by a company. This data is temporarily stored on the server as foundational information for subsequent steps.

[0672] The server analyzes this data and generates metadata based on it. This metadata includes data attribute information and related tags, which are used in subsequent classification tasks. Furthermore, terminals use the metadata to classify data into appropriate categories and assign tags, facilitating data management and retrieval.

[0673] A distinctive feature of this invention lies in its emotion engine, which recognizes the user's emotions. While the user is using the system, the emotion engine analyzes the user's input and responses in real time to identify the user's emotional state. This analysis is then reflected in the visualization process. For example, if the user is feeling stressed, the visualization of that data is automatically adjusted to a more intuitive and simpler format.

[0674] The server automatically detects and organizes unnecessary data based on update frequency and owner information. This ensures efficient data management and quick access to important information.

[0675] Finally, users can access visualization tools from their devices to intuitively understand data that has been refined by the emotion engine. For example, when a user reviews sales data, the color palette and graph types are optimized according to the emotion engine's guidelines, improving readability.

[0676] In this way, this system dynamically adapts to user emotions, achieving highly efficient and secure data management, and ultimately increasing user satisfaction.

[0677] The following describes the processing flow.

[0678] Step 1:

[0679] The server collects necessary data from external data sources. For example, it extracts data in various formats, such as sales data and customer information, and stores it in temporary storage.

[0680] Step 2:

[0681] The server analyzes the collected data, calculates its attributes, and generates metadata. Natural language processing and machine learning algorithms are used for the analysis, which identifies the characteristics and structure of the data.

[0682] Step 3:

[0683] The terminal uses the generated metadata to categorize the data and assign appropriate tags. In this process, categories such as "sales," "customer information," and "market trends" are set, and the data is organized and tidied up.

[0684] Step 4:

[0685] The server monitors the data in the database and detects unnecessary data. This includes data that is not frequently updated or whose owner is unknown, and this data is automatically deleted or archived.

[0686] Step 5:

[0687] While the user interacts with the system, the emotion engine analyzes the user's input data and physiological responses in real time. This analysis identifies the user's emotional state, and the results are then reflected in the next process.

[0688] Step 6:

[0689] The device adjusts data visualizations based on the analysis results of the emotion engine. For example, if the user is experiencing stress, it changes the color palette of the graph to provide a simpler and easier-to-understand visualization. This approach facilitates an intuitive understanding of the data.

[0690] Step 7:

[0691] Ultimately, users review the adjusted data through their devices and make decisions based on the visualized information. This leads to faster and more accurate data-driven decision-making, improving work efficiency.

[0692] (Example 2)

[0693] 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".

[0694] Traditional data management systems have the drawback of not adequately optimizing the user experience because they visualize data without considering the user's emotional state. Furthermore, the lack of dynamic adjustments in efficient data classification and removal of unnecessary data makes it difficult to quickly understand information and access important data.

[0695] 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.

[0696] In this invention, the server includes means for collecting data, means for analyzing the data to generate metadata, and means for analyzing the user's emotional state and adjusting the data visualization based on the generated emotional information. This enables optimal data visualization according to the user's emotions, improving the user experience and allowing quick access to important information.

[0697] "Means of data collection" refers to a function that automatically retrieves necessary information from multiple sources and converts it into a format usable within the system.

[0698] "Methods for analyzing data and generating metadata" refers to the process of analyzing collected information, extracting related attribute information and tags, and generating additional information to facilitate management.

[0699] "Methods for classifying and tagging data based on metadata" refers to methods of using generated additional information to divide data into specific categories and attach sticky notes to streamline management and retrieval.

[0700] "Methods for analyzing a user's emotional state" refer to technologies that analyze a user's input, reactions, facial expressions, etc., to determine their psychological state.

[0701] "Means of adjusting data visualization based on emotional information" refers to a function that changes the method of presenting information and the display style according to the analyzed emotional results, thereby improving user comprehension.

[0702] "Methods for detecting and organizing unnecessary data" refers to a system that automatically identifies and organizes infrequently used information based on its retention period and importance.

[0703] "Methods for visualizing organized data" refer to technologies that present managed information in the form of graphs and charts that users can easily understand.

[0704] This invention relates to a data management system that analyzes the user's emotional state and dynamically adjusts data visualization based on that analysis. This system can optimize the user experience throughout the entire process, from data collection to visualization. The specific implementation of this system is described below.

[0705] The server retrieves data from multiple sources. Specifically, it uses APIs to collect company sales and customer information and temporarily stores it on the server. Next, the server analyzes the collected data using the Python Pandas library and generates metadata, including information attributes and related tags. Using this metadata, the terminal classifies the data into categories and manages it efficiently through an SQL database.

[0706] To analyze the user's emotional state, the system uses a machine learning model (for example, a CNN model using TensorFlow). It analyzes data collected from the user's webcam and input devices to determine emotions from the user's facial expressions and behavior. The results of the analysis are reflected in the data visualization. The server uses the D3.js library to present the data in the most appropriate format corresponding to the user's emotions. For example, a simple bar graph is displayed for users experiencing stress.

[0707] The system also automatically detects and organizes unnecessary data based on its usage frequency and ownership information. This feature enables more efficient data storage and quicker access to important information. Finally, users can easily understand visualized data by interacting with an interactive dashboard via their device.

[0708] For example, when a user views sales data, the emotion engine will change the graph visualization if it detects a stress level. An example prompt would be, "How can I automatically adjust the sales data visualization based on the user's emotions?"

[0709] Thus, the embodiments of the present invention enable data presentation that responds to the user's emotions, providing a system that improves both data management and user experience.

[0710] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0711] Step 1:

[0712] The server collects data from multiple sources via APIs. It sends requests containing API keys and authentication information as input, retrieving company sales data and customer information. The retrieved data is stored in JSON or CSV format. This process ensures that the necessary information is incorporated into the system.

[0713] Step 2:

[0714] The server uses the Pandas library to analyze the collected data. It reads the data stored as input and performs data cleaning, such as checking for missing values ​​and removing duplicate data. As a result of the analysis, metadata is generated based on the data's attribute information and relationships. This metadata streamlines data management.

[0715] Step 3:

[0716] The terminal utilizes the generated metadata to categorize the data. Metadata is used as input, and SQL commands are used to categorize the information in the database. The categorized data is tagged, making it easy to search.

[0717] Step 4:

[0718] The user accesses the system and manipulates data through their terminal. During this process, the emotion engine acquires data from the user's webcam and input devices. Using the user's facial expressions and operation patterns as input, a generative AI model is employed to analyze their emotional state in real time. The user's emotional state is then determined as output.

[0719] Step 5:

[0720] The server uses the D3.js library to visualize data based on the user's emotional state. Input includes analyzed emotional states and classified data, which are used to dynamically adjust the visualization style. The output is data visualized in an optimal format for each emotion.

[0721] Step 6:

[0722] The server periodically detects and organizes unnecessary data based on its usage frequency and owner information. Using access logs and owner metadata from the database as input, it compresses or deletes data that has not been used for a long period. This improves storage efficiency.

[0723] Step 7:

[0724] Users view visualized data through their devices and examine sales data trends as a concrete example. Depending on the sentiment analysis results, information may be presented in a simple display format, making it easier for users to intuitively understand the data.

[0725] (Application Example 2)

[0726] 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".

[0727] Modern information processing systems require the effective management and utilization of vast amounts of data. However, conventional systems lack the flexibility to provide data that responds to user emotions and states, leading to problems such as reduced user experience and operational efficiency. Therefore, there is a need for means to achieve dynamic data visualization and management that takes user emotions into account.

[0728] 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.

[0729] In this invention, the server includes means for collecting data, means for collecting and analyzing emotional data, and means for adaptively adjusting the visualization based on the emotional data. This enables dynamic and effective visualization of data in accordance with the user's emotional state.

[0730] "Means of data collection" refers to the process or function of obtaining necessary information from networks and devices.

[0731] "Means for generating metadata" refers to a process or apparatus for analyzing collected information and generating supplementary information that explicitly indicates its attributes, relationships, etc.

[0732] "Means of classifying and tagging data" refers to the process of dividing information into specific categories and assigning identifiable tags based on the generated metadata.

[0733] "Methods for detecting and organizing unnecessary data" refers to a process of judging the value of information based on its update frequency and attributes, and eliminating and organizing the unnecessary parts.

[0734] "Means of visualization" refers to a process or device for displaying organized information in a format that is easily understandable to the user.

[0735] "Means for collecting and analyzing emotional data" refers to the process or technology of acquiring information from facial expressions and behaviors and analyzing it in order to understand the emotional state of a user.

[0736] "Means of adaptively adjusting visualization" refers to a process that dynamically changes the format and content of the displayed data to suit the individual user's state, based on the analyzed emotional information.

[0737] In realizing this system, the server, terminals, and users each play crucial roles. The server collects data from the network and generates metadata from it. This metadata includes attribute information and tags for each data item and is sent to the terminals for subsequent data classification and tagging tasks.

[0738] The server also collects and analyzes user emotion data. This involves using technologies that capture and analyze the user's emotional state in real time, such as facial recognition cameras and voice analysis systems. This information is analyzed on the server and used to dynamically adjust data visualizations based on the user's emotions. This process utilizes emotion analysis algorithms built with programming languages ​​such as Python, and JavaScript libraries such as D3.js for data visualization.

[0739] The terminal acts as the interface with the user, displaying visualization results sent from the server. The terminal, such as smart glasses, presents information in a format suitable for the user. In this process, an emotion engine is used to customize the information according to the user's state. For example, if the user is relaxed, detailed product information can be displayed.

[0740] This system is designed for use in physical stores and can analyze customer sentiment in real time as they browse the store, providing them with relevant information on products and sales that interest them. This is expected to improve the user experience.

[0741] As a concrete example, when a user approaches a specific product shelf in a store, information about the products on that shelf is displayed on their smart glasses. When the user is feeling stressed, only simple and intuitive information is displayed, providing a comfortable shopping experience tailored to their emotions.

[0742] Examples of prompts for the generating AI model include, "If the user is relaxed, we will display bonus points information and products with relaxing effects," and "If the user is excited, we will suggest new products that stimulate adrenaline."

[0743] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0744] Step 1:

[0745] The server collects data from the network. It takes information from external data sources as input and generates metadata based on this. This metadata includes data attribute information and related tags, which are used in subsequent processing. The output is metadata that forms the basis for classification and tagging.

[0746] Step 2:

[0747] The server collects and analyzes user emotional data using emotion recognition cameras and voice input devices. It acquires user facial expressions and voice data as input and applies an emotion analysis algorithm built in Python to identify the user's emotional state. The analysis results are output, and this data is used to refine the visualization process.

[0748] Step 3:

[0749] The server classifies and tags data based on the generated metadata. It uses metadata as input, parses it, categorizes and tags it, and organizes it in a manageable format. The output is structured data suitable for searching and visualization.

[0750] Step 4:

[0751] The terminal dynamically adjusts the data visualization based on the sentiment analysis results sent from the server. Here, it receives sentiment data and structured data as input and displays them to the user in a visually understandable format using the JavaScript library D3.js. The output is visualized data adapted to the user's emotional state.

[0752] Step 5:

[0753] Users access in-store information through smart glasses. This action allows them to make decisions based on the data displayed on the device. Visualized information is received as input, which is then used to inform purchasing decisions and product selections. The output is improved user experience and increased satisfaction.

[0754] Step 6:

[0755] The device then sends user feedback and new sentiment data back to the server. This step improves data accuracy by collecting the user's latest sentiment state and operation history as input and transferring it to the server. The output is more accurate sentiment recognition and data visualization.

[0756] 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.

[0757] 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.

[0758] 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.

[0759] 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.

[0760] 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.

[0761] 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.

[0762] 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.

[0763] 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.

[0764] 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."

[0765] 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.

[0766] 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.

[0767] 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.

[0768] 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.

[0769] 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.

[0770] 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.

[0771] 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.

[0772] 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.

[0773] 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.

[0774] 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.

[0775] 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.

[0776] 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.

[0777] The following is further disclosed regarding the embodiments described above.

[0778] (Claim 1)

[0779] Means of collecting data,

[0780] Means for analyzing the data and generating metadata,

[0781] Means for classifying and tagging data based on the metadata,

[0782] A method for detecting and organizing unnecessary data,

[0783] A means for visualizing the organized data,

[0784] A system that includes this.

[0785] (Claim 2)

[0786] The system according to claim 1, wherein the metadata indicates the attributes of the data.

[0787] (Claim 3)

[0788] The system according to claim 1, wherein the unnecessary data is identified based on update frequency and owner information.

[0789] "Example 1"

[0790] (Claim 1)

[0791] Means for collecting information from data sources,

[0792] A means for analyzing the information and generating attached information,

[0793] A means for classifying and assigning identification labels to information based on the attached information,

[0794] A means of detecting and organizing information that has not been exchanged,

[0795] A means for visually presenting the organized information,

[0796] A system that includes this.

[0797] (Claim 2)

[0798] The system according to claim 1, wherein the attached information indicates the characteristics of the information.

[0799] (Claim 3)

[0800] The system according to claim 1, wherein information for which such information has not been exchanged is identified based on update frequency and owner identification information.

[0801] "Application Example 1"

[0802] (Claim 1)

[0803] Means of acquiring data,

[0804] A means for analyzing the data and generating supplementary data,

[0805] A means for classifying data and assigning specific information based on the supplementary data,

[0806] A means of detecting and organizing unnecessary data,

[0807] A means for visualizing the organized data,

[0808] A means of classifying data into groups using a clustering algorithm and adding specific risk information,

[0809] A system that includes this.

[0810] (Claim 2)

[0811] The system according to claim 1, wherein the supplementary data represents the characteristics of the data.

[0812] (Claim 3)

[0813] The system according to claim 1, wherein the unnecessary data is identified based on the frequency of updates or ownership information.

[0814] "Example 2 of combining an emotion engine"

[0815] (Claim 1)

[0816] Means of collecting data,

[0817] Means for analyzing the data and generating metadata,

[0818] Means for classifying and tagging data based on the metadata,

[0819] A means of analyzing the user's emotional state, and a means of adjusting data visualization based on the generated emotional information,

[0820] A method for detecting and organizing unnecessary data,

[0821] A means for visualizing the organized data,

[0822] A system that includes this.

[0823] (Claim 2)

[0824] The system according to claim 1, wherein the metadata indicates the attributes of the data, and the emotional state is analyzed based on user input and responses.

[0825] (Claim 3)

[0826] The system according to claim 1, wherein the unnecessary data is identified based on update frequency and owner information, and the visualization is dynamically adjusted based on sentiment analysis results.

[0827] "Application example 2 when combining with an emotional engine"

[0828] (Claim 1)

[0829] Means of collecting data,

[0830] Means for analyzing the data and generating metadata,

[0831] Means for classifying and tagging data based on the metadata,

[0832] A method for detecting and organizing unnecessary data,

[0833] A means for visualizing the organized data,

[0834] A means of collecting and analyzing emotional data,

[0835] Means for adaptively adjusting the visualization based on the emotion data,

[0836] A system that includes this.

[0837] (Claim 2)

[0838] The system according to claim 1, wherein the metadata indicates the attributes of the data.

[0839] (Claim 3)

[0840] Unnecessary data is identified based on update frequency and owner information.

[0841] The system according to claim 1, wherein the emotion data is detected in real time and reflected in the visualization. [Explanation of symbols]

[0842] 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. Means of collecting data, Means for analyzing the data and generating metadata, Means for classifying and tagging data based on the metadata, A method for detecting and organizing unnecessary data, A means for visualizing the organized data, A system that includes this.

2. The system according to claim 1, wherein the metadata indicates the attributes of the data.

3. The system according to claim 1, wherein the unnecessary data is identified based on update frequency and owner information.

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