system

Abstract

The system according to this embodiment aims to efficiently grasp and manage large-scale and complex data flows. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, an investigation unit, a presentation unit, and an impact scope identification unit. The data collection unit collects data. The analysis unit analyzes the data collected by the data collection unit. The investigation unit conducts an investigation in an interactive format when a problem occurs based on the data analyzed by the analysis unit. The presentation unit presents the information obtained by the investigation unit to the user. The impact scope identification unit identifies the impact scope based on the information presented by the presentation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult to efficiently grasp and manage a large-scale and complex data flow.

[0005] The system according to the embodiment aims to efficiently grasp and manage a large-scale and complex data flow.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, an investigation unit, a presentation unit, and an impact scope identification unit. The data collection unit collects data. The analysis unit analyzes the data collected by the data collection unit. The investigation unit conducts an interactive investigation when a problem occurs based on the data analyzed by the analysis unit. The presentation unit presents the information obtained by the investigation unit to the user. The impact scope identification unit identifies the impact scope based on the information presented by the presentation unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently grasp and manage large-scale and complex data flows. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] 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 only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a receiving 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 receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice 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 unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (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.

[0022] 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.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The data management efficiency system according to an embodiment of the present invention is a system for streamlining corporate data management. This data management efficiency system addresses the problem that while it is important to input more data to improve the performance of AI, the cost of data management is increasing due to growing privacy awareness and stricter regulations. The data management efficiency system provides a method for streamlining management by allowing the AI ​​itself to understand the flow of corporate data. For example, the data management efficiency system can use conversational AI to understand the content of corporate data processing and conduct investigations in a conversational format when problems occur or inquiries are made. For example, the data management efficiency system can have the AI ​​read SQL or program code to recognize the flow of data and processing flow and output it as a data flow diagram. In addition, when an anomaly such as data loss occurs, the data management efficiency system can ask about the scope of impact in a conversational format, thereby reducing the human cost of data management and incident response. Through this mechanism, companies can reduce data management costs and efficiently supply the large amount of data necessary for AI training. For example, the data management efficiency system ensures data transparency and allows users to control their data by providing a mechanism to understand the source and path of data held by a company and present it to the user. This allows companies to reduce the burden of data management and improve the performance of AI. As a result, data management efficiency systems can streamline corporate data management and reduce data management costs.

[0029] The data management efficiency system according to the embodiment comprises a collection unit, an analysis unit, an investigation unit, a presentation unit, and an impact scope identification unit. The collection unit collects data. The collection unit can, for example, collect the data processing content of a company. The data processing content of a company includes, but is not limited to, database management, data analysis, and data backup. The collection unit may also include AI processing. The analysis unit analyzes the data collected by the collection unit. The analysis unit can, for example, analyze the collected data and generate a data flow diagram. The data flow diagram is generated in, for example, the form of a flowchart or UML diagram, but is not limited to such examples. The analysis unit includes AI processing. The investigation unit conducts an investigation in a dialogue format when a problem occurs based on the data analyzed by the analysis unit. The investigation unit can, for example, investigate the data flow and processing flow in a dialogue format. The dialogue format includes, for example, a chatbot or a voice dialogue system, but is not limited to such examples. The investigation unit includes AI processing. The presentation unit presents the information obtained by the investigation unit to the user. The presentation unit can, for example, present the user with the source and path of the data. The source and path of the data include, but are not limited to, the origin of the data and the path of the data's movement. The presentation unit may also include AI processing. The impact scope identification unit identifies the scope of impact based on the information presented by the presentation unit. The impact scope identification unit can, for example, identify the scope of impact when an anomaly such as data loss occurs. The scope of impact includes, but are not limited to, the scope of the affected systems or processes. The impact scope identification unit includes AI processing. As a result, the data management efficiency system according to this embodiment can streamline a company's data management and reduce data management costs.

[0030] The data collection unit collects data. For example, the data collection unit can collect data processing information for a company. Specifically, it can collect logs of data reads and writes from the company's database management system and execute queries and analysis results from data analysis systems. It can also collect backup schedules and execution status from data backup systems. This data is automatically collected from various departments and systems within the company and aggregated in a central database. The data collection unit may also include AI processing. For example, AI can be used to dynamically adjust the frequency and target of data collection, achieving efficient data collection. The AI ​​learns past data collection patterns and system usage to predict the optimal collection timing and target. This allows the data collection unit to comprehensively and efficiently collect data processing information for a company and build a foundation for data management. Furthermore, the data collection unit also has the function to check the quality of the collected data and detect incomplete or abnormal data. For example, it performs data integrity checks and detects duplicate data to maintain data quality. This allows the data collection unit to provide reliable data and improve the accuracy of subsequent analysis and investigations.

[0031] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit can analyze the collected data and generate a data flow diagram. The data flow diagram may be generated in the form of a flowchart or UML diagram, but is not limited to these examples. Specifically, the analysis unit visually represents the flow of data and the order of processing, clarifying the data processing process. The analysis unit includes AI processing. The AI ​​automatically analyzes the collected data and generates a data flow diagram. For example, the AI ​​analyzes data dependencies and the order of processing to generate an optimal data flow diagram. The AI ​​can also learn from past data flow diagrams and similar data processing patterns to suggest an efficient data flow diagram. This allows the analysis unit to quickly and accurately grasp the flow of data and the processing process, supporting the efficiency of data management. Furthermore, the analysis unit not only generates data flow diagrams but also performs data anomaly detection and performance analysis. For example, it analyzes data processing time and resource usage to identify bottlenecks and performance problems. This allows the analysis unit to contribute to the optimization of data management and the early detection of problems, improving the overall efficiency and reliability of the system.

[0032] The Investigation Department conducts interactive investigations when problems arise based on data analyzed by the Analysis Department. For example, the Investigation Department can investigate data flow and processing flow through dialogue. This includes, but is not limited to, chatbots and voice dialogue systems. Specifically, the Investigation Department provides answers to user-inputted questions and problems based on collected data and analysis results. The Investigation Department incorporates AI processing. The AI ​​uses natural language processing techniques to understand user questions and generate appropriate answers. For example, if a user inputs "Please explain the data processing flow," the AI ​​will explain the data flow and processing sequence based on a data flow diagram generated by the Analysis Department. The AI ​​can also learn from past dialogue history and similar problems to provide more appropriate answers. This allows the Investigation Department to quickly and accurately resolve user problems and support more efficient data management. Furthermore, the Investigation Department conducts detailed analyses to identify the root cause of problems, not just interactive investigations. For example, it identifies the causes of data anomalies and errors and proposes preventative measures. This allows the Investigation Department to improve the reliability and efficiency of data management.

[0033] The presentation unit presents information obtained by the research unit to the user. For example, the presentation unit can present the user with the source and path of the data. The source and path of the data include, but are not limited to, the origin of the data and the path of the data's movement. Specifically, the presentation unit presents the information in the form of graphs, charts, maps, etc., so that the user can visually understand the source and path of the data. The presentation unit may also include AI processing. The AI ​​selects the optimal method of information presentation based on the user's interests and needs. For example, the AI ​​learns the information the user has previously viewed and their operation history, and presents the information in the format that is easiest for the user to understand. The AI ​​can also provide the latest information based on data that is updated in real time. This allows the presentation unit to help users quickly and accurately understand the source and path of the data and to improve the efficiency of data management. Furthermore, the presentation unit can collect feedback from users and continuously improve the accuracy and effectiveness of the information presentation. For example, by having users comment on and evaluate the presented information, the presentation unit can review the method and content of information presentation and achieve more effective information delivery. This allows the display unit to provide users with high-quality information and improve the efficiency and reliability of data management.

[0034] The Impact Identification Unit identifies the scope of impact based on the information presented by the Presentation Unit. For example, the Impact Identification Unit can identify the scope of impact when an anomaly occurs, such as data loss. The scope of impact includes, but is not limited to, the range of affected systems and processes. Specifically, when a data anomaly or error occurs, the Impact Identification Unit identifies the affected systems and processes and visually displays the scope of impact. The Impact Identification Unit includes AI processing. The AI ​​analyzes data dependencies and system structure to automatically identify the scope of impact. For example, the AI ​​analyzes dependencies between database tables and identifies other tables and systems affected when an anomaly occurs in a specific table. Furthermore, the AI ​​learns the scope of impact from past anomalies and can quickly identify the scope of impact when similar anomalies occur. This allows the Impact Identification Unit to quickly and accurately identify the scope of impact when a data anomaly or error occurs and provide information for taking appropriate countermeasures. In addition, the Impact Identification Unit not only identifies the scope of impact but also proposes countermeasures to minimize the impact. For example, it sets priorities for affected systems and processes and responds quickly to critical systems and processes. Furthermore, the impact identification unit can share the results of its impact identification with other departments and systems, enabling collaborative problem-solving. This allows the impact identification unit to improve the reliability and efficiency of data management and reduce overall data management costs for the company.

[0035] The collection unit can collect data processing information from companies. For example, the collection unit collects data processing information from companies. This includes, but is not limited to, database management, data analysis, and data backup. This allows for the efficient collection of data processing information from companies. Some or all of the above-described processing in the collection unit may be performed using AI or not. For example, the collection unit can input data processing information from companies into an AI and have the AI ​​perform the data collection.

[0036] The analysis unit can analyze the collected data and generate a data flow diagram. For example, the analysis unit can analyze the collected data and generate a data flow diagram. The data flow diagram may include, but is not limited to, formats such as flowcharts and UML diagrams. This allows for a visual understanding of the data flow and processing flow. Some or all of the above-described processes in the analysis unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the analysis unit can input the collected data into a generation AI and have the generation AI generate a data flow diagram.

[0037] The investigation unit can investigate data flows and processing flows in an interactive format. For example, the investigation unit can investigate data flows and processing flows in an interactive format. This includes, but is not limited to, examples such as chatbots and voice dialogue systems. This allows for the investigation of data flows and processing flows in an interactive format. Some or all of the above-described processing in the investigation unit may be performed using or without generative AI. For example, the investigation unit can input data flows and processing flows into a generative AI and have the generative AI perform the interactive investigation.

[0038] The presentation unit can present the user with the source and path of the data. For example, the presentation unit can present the user with the source and path of the data. The source and path of the data include, but are not limited to, the origin of the data and the path through which the data moved. By presenting the source and path of the data to the user, data transparency can be ensured. Some or all of the above processing in the presentation unit may be performed using AI or not. For example, the presentation unit can input the source and path of the data into the AI ​​and have the AI ​​perform the presentation to the user.

[0039] The impact scope identification unit can identify the scope of impact when an anomaly such as data loss occurs. For example, the impact scope identification unit identifies the scope of impact when an anomaly such as data loss occurs. The scope of impact includes, but is not limited to, the range of systems or processes affected. This enables a rapid response by identifying the scope of impact when an anomaly such as data loss occurs. Some or all of the above processing in the impact scope identification unit may be performed using a generation AI, or it may be performed without a generation AI. For example, when an anomaly such as data loss occurs, the impact scope identification unit can input the scope of impact to a generation AI and have the generation AI perform the identification of the scope of impact.

[0040] The data collection unit can analyze a company's past data processing history and select the optimal data collection method. For example, the data collection unit can select the most efficient data collection method from past data processing history. The data collection unit can also optimize the timing of data collection based on past data processing history. Furthermore, the data collection unit can analyze past data processing history and determine the priority of data collection. This enables efficient data collection by selecting the optimal data collection method based on past data processing history. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input past data processing history into AI and have the AI ​​select the optimal data collection method.

[0041] The data collection unit can filter data based on the company's current business situation and areas of interest during data collection. For example, the data collection unit can prioritize the collection of highly relevant data, taking into account the company's current business situation. It can also filter out unnecessary data based on the company's areas of interest. Furthermore, the data collection unit can adjust the scope of data collection based on the company's business situation and areas of interest. This allows for the collection of highly relevant data by filtering data based on the company's business situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the company's business situation and areas of interest into an AI and have the AI ​​perform the data filtering.

[0042] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of companies during data collection. For example, the data collection unit prioritizes the collection of highly relevant data based on the company's location. The data collection unit can also collect region-specific data by considering the geographical location information of companies. Furthermore, the data collection unit can adjust the scope of data collection based on the geographical location information of companies. This allows for the efficient collection of region-specific data by considering the geographical location information of companies. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the geographical location information of companies into AI and have the AI ​​perform the collection of highly relevant data.

[0043] The data collection unit can analyze a company's social media activities and collect relevant data during data collection. For example, the data collection unit can analyze a company's social media activities and collect highly relevant data. The data collection unit can also determine the priority of data collection based on the company's social media activities. Furthermore, the data collection unit can adjust the scope of data collection considering the company's social media activities. This allows for the collection of highly relevant data by analyzing the company's social media activities. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input a company's social media activities into an AI and have the AI ​​perform the collection of relevant data.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during data analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. It can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can determine the priority of the analysis based on the importance of the data. This allows for efficient data analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the importance of the data into the AI ​​and have the AI ​​perform the adjustment of the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the data category during data analysis. For example, the analysis unit can select the optimal analysis algorithm based on the data category. The analysis unit can also adjust the level of detail of the analysis based on the data category. Furthermore, the analysis unit can determine the priority of the analysis based on the data category. This improves the accuracy of the analysis by applying the optimal analysis algorithm according to the data category. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the data category into the AI ​​and have the AI ​​execute the application of the analysis algorithm.

[0046] The research department can improve the accuracy of its research by considering the interrelationships between data during the research process. For example, the research department can analyze the interrelationships between data to improve research accuracy. Furthermore, the research department can determine research priorities based on the interrelationships between data. In addition, the research department can adjust the scope of the research by considering the interrelationships between data. This improves research accuracy by considering the interrelationships between data. Some or all of the above processes in the research department may be performed using AI or not. For example, the research department can input the interrelationships between data into AI and have the AI ​​perform the research accuracy improvement.

[0047] The research department can conduct research while considering the attribute information of data submitters. For example, the research department can determine the priority of the research based on the attribute information of the data submitters. The research department can also adjust the scope of the research while considering the attribute information of the data submitters. Furthermore, the research department can adjust the level of detail of the research based on the attribute information of the data submitters. This improves the accuracy of the research by considering the attribute information of the data submitters. Some or all of the above processes in the research department may be performed using AI or not. For example, the research department can input the attribute information of the data submitters into AI and have the AI ​​perform the research.

[0048] The presentation unit can adjust the level of detail in the presentation based on the importance of the data. For example, the presentation unit can provide a detailed presentation for highly important data, and a simplified presentation for less important data. Furthermore, the presentation unit can determine the priority of the presentation based on the importance of the data. This allows for efficient information presentation by adjusting the level of detail based on the importance of the data. Some or all of the above processing in the presentation unit may be performed using AI, or not. For example, the presentation unit can input the importance of the data into the AI ​​and have the AI ​​adjust the level of detail in the presentation.

[0049] The presentation unit can apply different presentation algorithms depending on the data category during presentation. For example, the presentation unit can select the optimal presentation algorithm based on the data category. The presentation unit can also adjust the level of detail of the presentation based on the data category. Furthermore, the presentation unit can determine the presentation priority based on the data category. This improves the accuracy of information presentation by applying the optimal presentation algorithm according to the data category. Some or all of the above processing in the presentation unit may be performed using AI or not. For example, the presentation unit can input the data category into the AI ​​and have the AI ​​perform the application of the presentation algorithm.

[0050] The presentation unit can determine the priority of presentation based on the data submission date. For example, the presentation unit may prioritize the presentation of the most recent data. The presentation unit can also provide a simplified presentation for older data. Furthermore, the presentation unit can adjust the level of detail of the presentation based on the data submission date. This allows for the prioritization of the presentation of the most recent information by determining the presentation priority based on the data submission date. Some or all of the above processing in the presentation unit may be performed using AI or not. For example, the presentation unit can input the data submission date into the AI ​​and have the AI ​​determine the presentation priority.

[0051] The presentation unit can adjust the order of presentation based on the relevance of the data. For example, the presentation unit can prioritize the presentation of highly relevant data. The presentation unit can also provide a simplified presentation for less relevant data. Furthermore, the presentation unit can adjust the level of detail in the presentation based on the relevance of the data. This allows for the priority presentation of highly relevant information by adjusting the order of presentation based on the relevance of the data. Some or all of the above processing in the presentation unit may be performed using AI or not. For example, the presentation unit can input the relevance of the data into the AI ​​and have the AI ​​perform the adjustment of the presentation order.

[0052] The impact scope identification unit can improve the accuracy of its identification by considering the interrelationships of data when identifying the impact scope. For example, the impact scope identification unit can analyze the interrelationships of data to improve the accuracy of its identification. The impact scope identification unit can also determine specific priorities based on the interrelationships of data. Furthermore, the impact scope identification unit can adjust the specific range by considering the interrelationships of data. This improves the accuracy of impact scope identification by considering the interrelationships of data. Some or all of the above processing in the impact scope identification unit may be performed using AI or not. For example, the impact scope identification unit can input the interrelationships of data into AI and have AI perform the specific accuracy improvement.

[0053] The impact scope identification unit can identify the scope of impact by considering the attribute information of the data submitter. For example, the impact scope identification unit can determine the priority of the scope of impact based on the attribute information of the data submitter. The impact scope identification unit can also adjust the scope of impact by considering the attribute information of the data submitter. Furthermore, the impact scope identification unit can adjust the level of detail of the scope of impact based on the attribute information of the data submitter. This improves the accuracy of impact scope identification by considering the attribute information of the data submitter. Some or all of the above processing in the impact scope identification unit may be performed using AI or not. For example, the impact scope identification unit can input the attribute information of the data submitter into AI and have the AI ​​perform specific actions.

[0054] The impact scope identification unit can identify the impact scope by considering the geographical distribution of the data. For example, the impact scope identification unit can determine a specific priority based on the geographical distribution of the data. The impact scope identification unit can also adjust the specific range by considering the geographical distribution of the data. Furthermore, the impact scope identification unit can adjust the level of detail based on the geographical distribution of the data. This improves the accuracy of impact scope identification by considering the geographical distribution of the data. Some or all of the above processing in the impact scope identification unit may be performed using AI or not. For example, the impact scope identification unit can input the geographical distribution of the data into AI and have the AI ​​perform specific actions.

[0055] The impact scope identification unit can improve the accuracy of its identification by referring to relevant literature for the data when identifying the scope of impact. For example, the impact scope identification unit can refer to relevant literature for the data to improve the accuracy of its identification. The impact scope identification unit can also determine specific priorities based on the relevant literature for the data. Furthermore, the impact scope identification unit can adjust the specific scope by considering the relevant literature for the data. As a result, the accuracy of identifying the scope of impact is improved by referring to relevant literature for the data. Some or all of the above processing in the impact scope identification unit may be performed using AI or not. For example, the impact scope identification unit can input relevant literature for the data into AI and have the AI ​​perform specific actions.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] A data management efficiency system can also include a security management unit that dynamically adjusts the security level of data. For example, the security management unit can adjust the encryption level of data based on its importance and confidentiality. Furthermore, the security management unit can dynamically change data access permissions and impose access restrictions on specific users or systems. In addition, the security management unit can apply security protocols during data transfer to prevent data leakage and tampering. This dynamic management of data security enhances corporate data protection.

[0058] A data management efficiency system can further include a quality evaluation unit that assesses data quality. This unit can, for example, check data consistency and completeness to evaluate data quality. It can also detect missing or outlier data and perform data correction or supplementation. Furthermore, it can evaluate data reliability and filter out unreliable data. This improves the accuracy of data management by evaluating data quality and providing reliable data.

[0059] A data management efficiency system can also include a monitoring unit to monitor data usage. This unit can, for example, monitor data access frequency and usage in real time. Furthermore, it can analyze data usage patterns and detect abnormal access or misuse. In addition, based on data usage, the monitoring unit can propose optimal data placement and caching strategies. This enables monitoring of data usage and efficient data management.

[0060] A data management efficiency system can further include a backup management unit that manages data backup and recovery. For example, the backup management unit can create regular data backups to prepare for data loss or corruption. It can also automate data recovery procedures to quickly restore data. Furthermore, the backup management unit can optimize data backup schedules to reduce system load. This ensures data security by efficiently managing data backup and recovery.

[0061] A data management efficiency system can further incorporate a lifecycle management unit to manage the data lifecycle. This unit can, for example, manage the entire process from data creation to disposal and set data expiration dates. It can also automate data archiving and deletion, efficiently processing unnecessary data. Furthermore, it can dynamically change data storage locations and access permissions based on data usage. This enables efficient data utilization and storage by managing the data lifecycle.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The collection unit collects data. The collection unit can, for example, collect information on a company's data processing activities. The collection unit can, but is not limited to, information on a company's data processing activities, which may include, for example, database management, data analysis, and data backup. The collection unit may also include AI processing. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit can, for example, analyze the collected data and generate a data flow diagram. The data flow diagram may be generated in the form of a flowchart or UML diagram, but is not limited to such examples. The analysis unit includes AI processing. Step 3: The Investigation Department conducts an interactive investigation when a problem arises based on the data analyzed by the Analysis Department. For example, the Investigation Department can investigate the data flow and processing flow in an interactive manner. This interactive format includes, but is not limited to, chatbots or voice dialogue systems. The Investigation Department may also utilize AI processing. Step 4: The presentation unit presents the information obtained by the research unit to the user. The presentation unit can, for example, present the user with the origin and path of the data. The origin and path of the data may include, but are not limited to, the source of the data and the path the data moved. The presentation unit may also include AI processing. Step 5: The impact scope identification unit identifies the impact scope based on the information presented by the presentation unit. The impact scope identification unit can identify the impact scope when an anomaly occurs, such as data loss. The impact scope includes, but is not limited to, the scope of affected systems or processes. The impact scope identification unit includes AI processing.

[0064] (Example of form 2) The data management efficiency system according to an embodiment of the present invention is a system for streamlining corporate data management. This data management efficiency system addresses the problem that while it is important to input more data to improve the performance of AI, the cost of data management is increasing due to growing privacy awareness and stricter regulations. The data management efficiency system provides a method for streamlining management by allowing the AI ​​itself to understand the flow of corporate data. For example, the data management efficiency system can use conversational AI to understand the content of corporate data processing and conduct investigations in a conversational format when problems occur or inquiries are made. For example, the data management efficiency system can have the AI ​​read SQL or program code to recognize the flow of data and processing flow and output it as a data flow diagram. In addition, when an anomaly such as data loss occurs, the data management efficiency system can ask about the scope of impact in a conversational format, thereby reducing the human cost of data management and incident response. Through this mechanism, companies can reduce data management costs and efficiently supply the large amount of data necessary for AI training. For example, the data management efficiency system ensures data transparency and allows users to control their data by providing a mechanism to understand the source and path of data held by a company and present it to the user. This allows companies to reduce the burden of data management and improve the performance of AI. As a result, data management efficiency systems can streamline corporate data management and reduce data management costs.

[0065] The data management efficiency system according to the embodiment comprises a collection unit, an analysis unit, an investigation unit, a presentation unit, and an impact scope identification unit. The collection unit collects data. The collection unit can, for example, collect the data processing content of a company. The data processing content of a company includes, but is not limited to, database management, data analysis, and data backup. The collection unit may also include AI processing. The analysis unit analyzes the data collected by the collection unit. The analysis unit can, for example, analyze the collected data and generate a data flow diagram. The data flow diagram is generated in, for example, the form of a flowchart or UML diagram, but is not limited to such examples. The analysis unit includes AI processing. The investigation unit conducts an investigation in a dialogue format when a problem occurs based on the data analyzed by the analysis unit. The investigation unit can, for example, investigate the data flow and processing flow in a dialogue format. The dialogue format includes, for example, a chatbot or a voice dialogue system, but is not limited to such examples. The investigation unit includes AI processing. The presentation unit presents the information obtained by the investigation unit to the user. The presentation unit can, for example, present the user with the source and path of the data. The source and path of the data include, but are not limited to, the origin of the data and the path of the data's movement. The presentation unit may also include AI processing. The impact scope identification unit identifies the scope of impact based on the information presented by the presentation unit. The impact scope identification unit can, for example, identify the scope of impact when an anomaly such as data loss occurs. The scope of impact includes, but are not limited to, the scope of the affected systems or processes. The impact scope identification unit includes AI processing. As a result, the data management efficiency system according to this embodiment can streamline a company's data management and reduce data management costs.

[0066] The data collection unit collects data. For example, the data collection unit can collect data processing information for a company. Specifically, it can collect logs of data reads and writes from the company's database management system and execute queries and analysis results from data analysis systems. It can also collect backup schedules and execution status from data backup systems. This data is automatically collected from various departments and systems within the company and aggregated in a central database. The data collection unit may also include AI processing. For example, AI can be used to dynamically adjust the frequency and target of data collection, achieving efficient data collection. The AI ​​learns past data collection patterns and system usage to predict the optimal collection timing and target. This allows the data collection unit to comprehensively and efficiently collect data processing information for a company and build a foundation for data management. Furthermore, the data collection unit also has the function to check the quality of the collected data and detect incomplete or abnormal data. For example, it performs data integrity checks and detects duplicate data to maintain data quality. This allows the data collection unit to provide reliable data and improve the accuracy of subsequent analysis and investigations.

[0067] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit can analyze the collected data and generate a data flow diagram. The data flow diagram may be generated in the form of a flowchart or UML diagram, but is not limited to these examples. Specifically, the analysis unit visually represents the flow of data and the order of processing, clarifying the data processing process. The analysis unit includes AI processing. The AI ​​automatically analyzes the collected data and generates a data flow diagram. For example, the AI ​​analyzes data dependencies and the order of processing to generate an optimal data flow diagram. The AI ​​can also learn from past data flow diagrams and similar data processing patterns to suggest an efficient data flow diagram. This allows the analysis unit to quickly and accurately grasp the flow of data and the processing process, supporting the efficiency of data management. Furthermore, the analysis unit not only generates data flow diagrams but also performs data anomaly detection and performance analysis. For example, it analyzes data processing time and resource usage to identify bottlenecks and performance problems. This allows the analysis unit to contribute to the optimization of data management and the early detection of problems, improving the overall efficiency and reliability of the system.

[0068] The Investigation Department conducts interactive investigations when problems arise based on data analyzed by the Analysis Department. For example, the Investigation Department can investigate data flow and processing flow through dialogue. This includes, but is not limited to, chatbots and voice dialogue systems. Specifically, the Investigation Department provides answers to user-inputted questions and problems based on collected data and analysis results. The Investigation Department incorporates AI processing. The AI ​​uses natural language processing techniques to understand user questions and generate appropriate answers. For example, if a user inputs "Please explain the data processing flow," the AI ​​will explain the data flow and processing sequence based on a data flow diagram generated by the Analysis Department. The AI ​​can also learn from past dialogue history and similar problems to provide more appropriate answers. This allows the Investigation Department to quickly and accurately resolve user problems and support more efficient data management. Furthermore, the Investigation Department conducts detailed analyses to identify the root cause of problems, not just interactive investigations. For example, it identifies the causes of data anomalies and errors and proposes preventative measures. This allows the Investigation Department to improve the reliability and efficiency of data management.

[0069] The presentation unit presents information obtained by the research unit to the user. For example, the presentation unit can present the user with the source and path of the data. The source and path of the data include, but are not limited to, the origin of the data and the path of the data's movement. Specifically, the presentation unit presents the information in the form of graphs, charts, maps, etc., so that the user can visually understand the source and path of the data. The presentation unit may also include AI processing. The AI ​​selects the optimal method of information presentation based on the user's interests and needs. For example, the AI ​​learns the information the user has previously viewed and their operation history, and presents the information in the format that is easiest for the user to understand. The AI ​​can also provide the latest information based on data that is updated in real time. This allows the presentation unit to help users quickly and accurately understand the source and path of the data and to improve the efficiency of data management. Furthermore, the presentation unit can collect feedback from users and continuously improve the accuracy and effectiveness of the information presentation. For example, by having users comment on and evaluate the presented information, the presentation unit can review the method and content of information presentation and achieve more effective information delivery. This allows the display unit to provide users with high-quality information and improve the efficiency and reliability of data management.

[0070] The Impact Identification Unit identifies the scope of impact based on the information presented by the Presentation Unit. For example, the Impact Identification Unit can identify the scope of impact when an anomaly occurs, such as data loss. The scope of impact includes, but is not limited to, the range of affected systems and processes. Specifically, when a data anomaly or error occurs, the Impact Identification Unit identifies the affected systems and processes and visually displays the scope of impact. The Impact Identification Unit includes AI processing. The AI ​​analyzes data dependencies and system structure to automatically identify the scope of impact. For example, the AI ​​analyzes dependencies between database tables and identifies other tables and systems affected when an anomaly occurs in a specific table. Furthermore, the AI ​​learns the scope of impact from past anomalies and can quickly identify the scope of impact when similar anomalies occur. This allows the Impact Identification Unit to quickly and accurately identify the scope of impact when a data anomaly or error occurs and provide information for taking appropriate countermeasures. In addition, the Impact Identification Unit not only identifies the scope of impact but also proposes countermeasures to minimize the impact. For example, it sets priorities for affected systems and processes and responds quickly to critical systems and processes. Furthermore, the impact identification unit can share the results of its impact identification with other departments and systems, enabling collaborative problem-solving. This allows the impact identification unit to improve the reliability and efficiency of data management and reduce overall data management costs for the company.

[0071] The collection unit can collect data processing information from companies. For example, the collection unit collects data processing information from companies. This includes, but is not limited to, database management, data analysis, and data backup. This allows for the efficient collection of data processing information from companies. Some or all of the above-described processing in the collection unit may be performed using AI or not. For example, the collection unit can input data processing information from companies into an AI and have the AI ​​perform the data collection.

[0072] The analysis unit can analyze the collected data and generate a data flow diagram. For example, the analysis unit can analyze the collected data and generate a data flow diagram. The data flow diagram may include, but is not limited to, formats such as flowcharts and UML diagrams. This allows for a visual understanding of the data flow and processing flow. Some or all of the above-described processes in the analysis unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the analysis unit can input the collected data into a generation AI and have the generation AI generate a data flow diagram.

[0073] The investigation unit can investigate data flows and processing flows in an interactive format. For example, the investigation unit can investigate data flows and processing flows in an interactive format. This includes, but is not limited to, examples such as chatbots and voice dialogue systems. This allows for the investigation of data flows and processing flows in an interactive format. Some or all of the above-described processing in the investigation unit may be performed using or without generative AI. For example, the investigation unit can input data flows and processing flows into a generative AI and have the generative AI perform the interactive investigation.

[0074] The presentation unit can present the user with the source and path of the data. For example, the presentation unit can present the user with the source and path of the data. The source and path of the data include, but are not limited to, the origin of the data and the path through which the data moved. By presenting the source and path of the data to the user, data transparency can be ensured. Some or all of the above processing in the presentation unit may be performed using AI or not. For example, the presentation unit can input the source and path of the data into the AI ​​and have the AI ​​perform the presentation to the user.

[0075] The impact scope identification unit can identify the scope of impact when an anomaly such as data loss occurs. For example, the impact scope identification unit identifies the scope of impact when an anomaly such as data loss occurs. The scope of impact includes, but is not limited to, the range of systems or processes affected. This enables a rapid response by identifying the scope of impact when an anomaly such as data loss occurs. Some or all of the above processing in the impact scope identification unit may be performed using a generation AI, or it may be performed without a generation AI. For example, when an anomaly such as data loss occurs, the impact scope identification unit can input the scope of impact to a generation AI and have the generation AI perform the identification of the scope of impact.

[0076] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to alleviate the user's burden. Conversely, if the user is relaxed, the data collection unit can increase the frequency of data collection to collect more detailed data. Furthermore, if the user is in a hurry, the data collection unit can adjust the timing of data collection to quickly collect the necessary data. In this way, the user's burden can be reduced by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the timing of data collection.

[0077] The data collection unit can analyze a company's past data processing history and select the optimal data collection method. For example, the data collection unit can select the most efficient data collection method from past data processing history. The data collection unit can also optimize the timing of data collection based on past data processing history. Furthermore, the data collection unit can analyze past data processing history and determine the priority of data collection. This enables efficient data collection by selecting the optimal data collection method based on past data processing history. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input past data processing history into AI and have the AI ​​select the optimal data collection method.

[0078] The data collection unit can filter data based on the company's current business situation and areas of interest during data collection. For example, the data collection unit can prioritize the collection of highly relevant data, taking into account the company's current business situation. It can also filter out unnecessary data based on the company's areas of interest. Furthermore, the data collection unit can adjust the scope of data collection based on the company's business situation and areas of interest. This allows for the collection of highly relevant data by filtering data based on the company's business situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the company's business situation and areas of interest into an AI and have the AI ​​perform the data filtering.

[0079] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting high-priority data. If the user is relaxed, the data collection unit can also prioritize collecting detailed data. Furthermore, if the user is in a hurry, the data collection unit can prioritize collecting data that can be collected quickly. This allows for the priority collection of important data by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the data prioritization.

[0080] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of companies during data collection. For example, the data collection unit prioritizes the collection of highly relevant data based on the company's location. The data collection unit can also collect region-specific data by considering the geographical location information of companies. Furthermore, the data collection unit can adjust the scope of data collection based on the geographical location information of companies. This allows for the efficient collection of region-specific data by considering the geographical location information of companies. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the geographical location information of companies into AI and have the AI ​​perform the collection of highly relevant data.

[0081] The data collection unit can analyze a company's social media activities and collect relevant data during data collection. For example, the data collection unit can analyze a company's social media activities and collect highly relevant data. The data collection unit can also determine the priority of data collection based on the company's social media activities. Furthermore, the data collection unit can adjust the scope of data collection considering the company's social media activities. This allows for the collection of highly relevant data by analyzing the company's social media activities. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input a company's social media activities into an AI and have the AI ​​perform the collection of relevant data.

[0082] The analysis unit can estimate the user's emotions and adjust the presentation of the data analysis based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visual presentation. If the user is relaxed, the analysis unit can also provide a presentation that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a concise presentation. By adjusting the presentation of the data analysis according to the user's emotions, the analysis results can be provided in a way that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the presentation of the data analysis.

[0083] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during data analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. It can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can determine the priority of the analysis based on the importance of the data. This allows for efficient data analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the importance of the data into the AI ​​and have the AI ​​perform the adjustment of the level of detail of the analysis.

[0084] The analysis unit can apply different analysis algorithms depending on the data category during data analysis. For example, the analysis unit can select the optimal analysis algorithm based on the data category. The analysis unit can also adjust the level of detail of the analysis based on the data category. Furthermore, the analysis unit can determine the priority of the analysis based on the data category. This improves the accuracy of the analysis by applying the optimal analysis algorithm according to the data category. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the data category into the AI ​​and have the AI ​​execute the application of the analysis algorithm.

[0085] The research department can estimate the user's emotions and adjust the research criteria based on those estimated emotions. For example, if the user is nervous, the research department can provide simple and easy-to-understand research criteria. If the user is relaxed, the research department can also provide research criteria that include more detailed information. Furthermore, if the user is in a hurry, the research department can provide concise research criteria. By adjusting the research criteria according to the user's emotions, the research department can provide research results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the research department may be performed using AI or not. For example, the research department can input user emotion data into a generative AI and have the generative AI adjust the research criteria.

[0086] The research department can improve the accuracy of its research by considering the interrelationships between data during the research process. For example, the research department can analyze the interrelationships between data to improve research accuracy. Furthermore, the research department can determine research priorities based on the interrelationships between data. In addition, the research department can adjust the scope of the research by considering the interrelationships between data. This improves research accuracy by considering the interrelationships between data. Some or all of the above processes in the research department may be performed using AI or not. For example, the research department can input the interrelationships between data into AI and have the AI ​​perform the research accuracy improvement.

[0087] The research department can conduct research while considering the attribute information of data submitters. For example, the research department can determine the priority of the research based on the attribute information of the data submitters. The research department can also adjust the scope of the research while considering the attribute information of the data submitters. Furthermore, the research department can adjust the level of detail of the research based on the attribute information of the data submitters. This improves the accuracy of the research by considering the attribute information of the data submitters. Some or all of the above processes in the research department may be performed using AI or not. For example, the research department can input the attribute information of the data submitters into AI and have the AI ​​perform the research.

[0088] The presentation unit can estimate the user's emotions and adjust the presentation method based on the estimated emotions. For example, if the user is nervous, the presentation unit can provide a simple and highly visible presentation method. If the user is relaxed, the presentation unit can also provide a presentation method that includes detailed information. Furthermore, if the user is in a hurry, the presentation unit can provide a concise presentation method. In this way, by adjusting the presentation method according to the user's emotions, information that is easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the presentation unit may be performed using AI or not. For example, the presentation unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the presentation method.

[0089] The presentation unit can adjust the level of detail in the presentation based on the importance of the data. For example, the presentation unit can provide a detailed presentation for highly important data, and a simplified presentation for less important data. Furthermore, the presentation unit can determine the priority of the presentation based on the importance of the data. This allows for efficient information presentation by adjusting the level of detail based on the importance of the data. Some or all of the above processing in the presentation unit may be performed using AI, or not. For example, the presentation unit can input the importance of the data into the AI ​​and have the AI ​​adjust the level of detail in the presentation.

[0090] The presentation unit can apply different presentation algorithms depending on the data category during presentation. For example, the presentation unit can select the optimal presentation algorithm based on the data category. The presentation unit can also adjust the level of detail of the presentation based on the data category. Furthermore, the presentation unit can determine the presentation priority based on the data category. This improves the accuracy of information presentation by applying the optimal presentation algorithm according to the data category. Some or all of the above processing in the presentation unit may be performed using AI or not. For example, the presentation unit can input the data category into the AI ​​and have the AI ​​perform the application of the presentation algorithm.

[0091] The presentation unit can estimate the user's emotions and adjust the presentation order based on the estimated emotions. For example, if the user is nervous, the presentation unit can provide a simple and highly visual presentation order. If the user is relaxed, the presentation unit can also provide a presentation order that includes detailed information. Furthermore, if the user is in a hurry, the presentation unit can provide a concise presentation order. In this way, by adjusting the presentation order according to the user's emotions, information that is easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the presentation unit may be performed using AI or not. For example, the presentation unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the presentation order.

[0092] The presentation unit can determine the priority of presentation based on the data submission date. For example, the presentation unit may prioritize the presentation of the most recent data. The presentation unit can also provide a simplified presentation for older data. Furthermore, the presentation unit can adjust the level of detail of the presentation based on the data submission date. This allows for the prioritization of the presentation of the most recent information by determining the presentation priority based on the data submission date. Some or all of the above processing in the presentation unit may be performed using AI or not. For example, the presentation unit can input the data submission date into the AI ​​and have the AI ​​determine the presentation priority.

[0093] The presentation unit can adjust the order of presentation based on the relevance of the data. For example, the presentation unit can prioritize the presentation of highly relevant data. The presentation unit can also provide a simplified presentation for less relevant data. Furthermore, the presentation unit can adjust the level of detail in the presentation based on the relevance of the data. This allows for the priority presentation of highly relevant information by adjusting the order of presentation based on the relevance of the data. Some or all of the above processing in the presentation unit may be performed using AI or not. For example, the presentation unit can input the relevance of the data into the AI ​​and have the AI ​​perform the adjustment of the presentation order.

[0094] The impact scope identification unit can estimate the user's emotions and adjust the impact scope identification method based on the estimated user emotions. For example, if the user is tense, the impact scope identification unit can provide a simple and highly visible identification method. If the user is relaxed, the impact scope identification unit can also provide an identification method that includes detailed information. Furthermore, if the user is in a hurry, the impact scope identification unit can provide a concise identification method. By adjusting the impact scope identification method according to the user's emotions, the system can provide identification results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the impact scope identification unit may be performed using AI or not. For example, the impact scope identification unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the impact scope identification method.

[0095] The impact scope identification unit can improve the accuracy of its identification by considering the interrelationships of data when identifying the impact scope. For example, the impact scope identification unit can analyze the interrelationships of data to improve the accuracy of its identification. The impact scope identification unit can also determine specific priorities based on the interrelationships of data. Furthermore, the impact scope identification unit can adjust the specific range by considering the interrelationships of data. This improves the accuracy of impact scope identification by considering the interrelationships of data. Some or all of the above processing in the impact scope identification unit may be performed using AI or not. For example, the impact scope identification unit can input the interrelationships of data into AI and have AI perform the specific accuracy improvement.

[0096] The impact scope identification unit can identify the scope of impact by considering the attribute information of the data submitter. For example, the impact scope identification unit can determine the priority of the scope of impact based on the attribute information of the data submitter. The impact scope identification unit can also adjust the scope of impact by considering the attribute information of the data submitter. Furthermore, the impact scope identification unit can adjust the level of detail of the scope of impact based on the attribute information of the data submitter. This improves the accuracy of impact scope identification by considering the attribute information of the data submitter. Some or all of the above processing in the impact scope identification unit may be performed using AI or not. For example, the impact scope identification unit can input the attribute information of the data submitter into AI and have the AI ​​perform specific actions.

[0097] The impact scope identification unit can estimate the user's emotions and adjust the display method of the impact scope based on the estimated user emotions. For example, if the user is tense, the impact scope identification unit can provide a simple and highly visible display method. If the user is relaxed, the impact scope identification unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the impact scope identification unit can provide a concise display method. By adjusting the display method of the impact scope according to the user's emotions, it is possible to provide display results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the impact scope identification unit may be performed using AI or not using AI. For example, the impact scope identification unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the display method of the impact scope.

[0098] The impact scope identification unit can identify the impact scope by considering the geographical distribution of the data. For example, the impact scope identification unit can determine a specific priority based on the geographical distribution of the data. The impact scope identification unit can also adjust the specific range by considering the geographical distribution of the data. Furthermore, the impact scope identification unit can adjust the level of detail based on the geographical distribution of the data. This improves the accuracy of impact scope identification by considering the geographical distribution of the data. Some or all of the above processing in the impact scope identification unit may be performed using AI or not. For example, the impact scope identification unit can input the geographical distribution of the data into AI and have the AI ​​perform specific actions.

[0099] The impact scope identification unit can improve the accuracy of its identification by referring to relevant literature for the data when identifying the scope of impact. For example, the impact scope identification unit can refer to relevant literature for the data to improve the accuracy of its identification. The impact scope identification unit can also determine specific priorities based on the relevant literature for the data. Furthermore, the impact scope identification unit can adjust the specific scope by considering the relevant literature for the data. As a result, the accuracy of identifying the scope of impact is improved by referring to relevant literature for the data. Some or all of the above processing in the impact scope identification unit may be performed using AI or not. For example, the impact scope identification unit can input relevant literature for the data into AI and have the AI ​​perform specific actions.

[0100] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0101] A data management efficiency system can also include a security management unit that dynamically adjusts the security level of data. For example, the security management unit can adjust the encryption level of data based on its importance and confidentiality. Furthermore, the security management unit can dynamically change data access permissions and impose access restrictions on specific users or systems. In addition, the security management unit can apply security protocols during data transfer to prevent data leakage and tampering. This dynamic management of data security enhances corporate data protection.

[0102] A data management efficiency system can further include a quality evaluation unit that assesses data quality. This unit can, for example, check data consistency and completeness to evaluate data quality. It can also detect missing or outlier data and perform data correction or supplementation. Furthermore, it can evaluate data reliability and filter out unreliable data. This improves the accuracy of data management by evaluating data quality and providing reliable data.

[0103] A data management efficiency system can also include a monitoring unit to monitor data usage. This unit can, for example, monitor data access frequency and usage in real time. Furthermore, it can analyze data usage patterns and detect abnormal access or misuse. In addition, based on data usage, the monitoring unit can propose optimal data placement and caching strategies. This enables monitoring of data usage and efficient data management.

[0104] A data management efficiency system can further include a backup management unit that manages data backup and recovery. For example, the backup management unit can create regular data backups to prepare for data loss or corruption. It can also automate data recovery procedures to quickly restore data. Furthermore, the backup management unit can optimize data backup schedules to reduce system load. This ensures data security by efficiently managing data backup and recovery.

[0105] A data management efficiency system can further incorporate a lifecycle management unit to manage the data lifecycle. This unit can, for example, manage the entire process from data creation to disposal and set data expiration dates. It can also automate data archiving and deletion, efficiently processing unnecessary data. Furthermore, it can dynamically change data storage locations and access permissions based on data usage. This enables efficient data utilization and storage by managing the data lifecycle.

[0106] The data management efficiency system can further include a display adjustment unit that estimates the user's emotions and adjusts the data display method based on the estimated emotions. For example, if the user is stressed, the display adjustment unit can provide a simple and highly visible display method. If the user is relaxed, the display adjustment unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the display adjustment unit can provide a display method that gets straight to the point. In this way, by adjusting the data display method according to the user's emotions, it is possible to provide information that is easy for the user to understand.

[0107] The data management efficiency system can further include a search adjustment unit that estimates the user's emotions and adjusts the search results based on those emotions. For example, if the user is stressed, the search adjustment unit can narrow down the search results and prioritize displaying more relevant information. It can also provide detailed search results if the user is relaxed. Furthermore, if the user is in a hurry, the search adjustment unit can prioritize displaying information that can be accessed quickly. This allows the system to provide the user with the most relevant information by adjusting search results according to their emotions.

[0108] The data management efficiency system can further include a notification adjustment unit that estimates the user's emotions and adjusts the data notification method based on the estimated emotions. For example, if the user is stressed, the notification adjustment unit may reduce the frequency of notifications and send only important notifications. It can also send detailed notifications if the user is relaxed. Furthermore, if the user is in a hurry, the notification adjustment unit may send concise notifications. In this way, by adjusting the notification method according to the user's emotions, the system can provide the user with appropriate information.

[0109] The data management efficiency system can further include a feedback adjustment unit that estimates the user's emotions and adjusts the data feedback method based on the estimated emotions. For example, if the user is stressed, the feedback adjustment unit provides simple, positive feedback. It can also provide detailed feedback if the user is relaxed. Furthermore, if the user is in a hurry, it can provide concise feedback. This allows the system to provide feedback that is easy for the user to understand by adjusting the feedback method according to the user's emotions.

[0110] The data management efficiency system can further include an export adjustment unit that estimates the user's emotions and adjusts the data export method based on those emotions. For example, if the user is stressed, the export adjustment unit might provide a simple and quick export method. If the user is relaxed, it could also provide an export method with detailed settings. Furthermore, if the user is in a hurry, it could provide a concise export method. This allows for user-friendly export functionality by adjusting the export method according to the user's emotions.

[0111] The following briefly describes the processing flow for example form 2.

[0112] Step 1: The collection unit collects data. The collection unit can, for example, collect information on a company's data processing activities. The collection unit can, but is not limited to, information on a company's data processing activities, which may include, for example, database management, data analysis, and data backup. The collection unit may also include AI processing. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit can, for example, analyze the collected data and generate a data flow diagram. The data flow diagram may be generated in the form of a flowchart or UML diagram, but is not limited to such examples. The analysis unit includes AI processing. Step 3: The Investigation Department conducts an interactive investigation when a problem arises based on the data analyzed by the Analysis Department. For example, the Investigation Department can investigate the data flow and processing flow in an interactive manner. This interactive format includes, but is not limited to, chatbots or voice dialogue systems. The Investigation Department may also utilize AI processing. Step 4: The presentation unit presents the information obtained by the research unit to the user. The presentation unit can, for example, present the user with the origin and path of the data. The origin and path of the data may include, but are not limited to, the source of the data and the path the data moved. The presentation unit may also include AI processing. Step 5: The impact scope identification unit identifies the impact scope based on the information presented by the presentation unit. The impact scope identification unit can identify the impact scope when an anomaly occurs, such as data loss. The impact scope includes, but is not limited to, the scope of affected systems or processes. The impact scope identification unit includes AI processing.

[0113] 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.

[0114] Data generation model 58 is a form of 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> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. 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 (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0115] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0116] Each of the multiple elements described above, including the data collection unit, analysis unit, investigation unit, presentation unit, and impact scope identification unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the data collection unit collects data using the camera 42 and microphone 38B of the smart device 14 and the control unit 46A collects the data processing details of the company. The analysis unit is implemented in the identification processing unit 290 of the data processing device 12, for example, and analyzes the collected data and generates a data flow diagram. The investigation unit is implemented in the identification processing unit 46A of the smart device 14, for example, and investigates the data flow and processing flow in an interactive manner. The presentation unit presents information to the user using the display 40A and speaker 40B of the smart device 14, for example. The impact scope identification unit is implemented in the identification processing unit 290 of the data processing device 12, for example, and identifies the impact scope when an anomaly occurs. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0117] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0118] 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.

[0119] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0120] 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.

[0121] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0122] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0123] 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.

[0124] 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 by the processor 28. The storage 32 stores the specific processing program 56.

[0125] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0126] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0127] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0129] 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.

[0130] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0131] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements described above, including the data collection unit, analysis unit, investigation unit, presentation unit, and impact scope identification unit, is implemented in at least one of the smart glasses 214 and the data processing device 12. For example, the data collection unit collects data using the camera 42 and microphone 238 of the smart glasses 214 and the control unit 46A collects the data processing details of the company. The analysis unit is implemented in the identification processing unit 290 of the data processing device 12, for example, and analyzes the collected data and generates a data flow diagram. The investigation unit is implemented in the identification processing unit 46A of the smart glasses 214, for example, and investigates the data flow and processing flow in an interactive manner. The presentation unit presents information to the user using the display and speaker 240 of the smart glasses 214. The impact scope identification unit is implemented in the identification processing unit 290 of the data processing device 12, for example, and identifies the impact scope when an anomaly occurs. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0133] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0134] 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.

[0135] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0136] 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.

[0137] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0138] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0139] 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.

[0140] 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.

[0141] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0142] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0143] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0144] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0145] 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.

[0146] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0147] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0148] Each of the multiple elements described above, including the data collection unit, analysis unit, investigation unit, presentation unit, and impact scope identification unit, is implemented in at least one of the headset terminal 314 and the data processing device 12. For example, the data collection unit collects data using the camera 42 and microphone 238 of the headset terminal 314 and the control unit 46A collects the data processing details of the company. The analysis unit is implemented in the identification processing unit 290 of the data processing device 12, for example, and analyzes the collected data and generates a data flow diagram. The investigation unit is implemented in the control unit 46A of the headset terminal 314, for example, and investigates the data flow and processing flow in an interactive format. The presentation unit presents information to the user using the display 343 and speaker 240 of the headset terminal 314. The impact scope identification unit is implemented in the identification processing unit 290 of the data processing device 12, for example, and identifies the impact scope when an anomaly occurs. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0149] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0150] 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.

[0151] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0152] 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.

[0153] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0154] 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 image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0155] 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.

[0156] 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. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0157] 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.

[0158] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0159] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0160] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0161] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0162] 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.

[0163] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0164] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0165] Each of the multiple elements described above, including the data collection unit, analysis unit, investigation unit, presentation unit, and impact scope identification unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects data using the camera 42 and microphone 238 of the robot 414 and the control unit 46A collects the data processing details of the company. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and analyzes the collected data and generates a data flow diagram. The investigation unit is implemented in the control unit 46A of the robot 414, for example, and investigates the data flow and processing flow in an interactive manner. The presentation unit presents information to the user using the display and speaker 240 of the robot 414. The impact scope identification unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and identifies the impact scope when an anomaly occurs. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0166] 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.

[0167] Figure 9 shows the 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.

[0168] 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.

[0169] 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.

[0170] 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, and motorcycles, 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 based, for example, 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.

[0171] 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."

[0172] 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.

[0173] 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 method for the specific process may be used, which includes computer 22 and multiple other computers.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0182] 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 other things 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.

[0183] 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.

[0184] (Note 1) A data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, The investigation unit conducts an investigation in a dialogue format when a problem occurs based on the data analyzed by the aforementioned analysis unit, A presentation unit that presents the information obtained by the aforementioned investigation unit to the user, The system includes an impact range identification unit that identifies the impact range based on the information presented by the aforementioned presentation unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect information on how companies process data. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Analyze the collected data and generate a data flow diagram. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned investigation department, Investigate data flow and processing flow through an interactive format. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned display unit is, Present the user the source and path of the data. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned unit for identifying the scope of influence is: Identify the scope of impact when anomalies such as data loss occur. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze a company's past data processing history to select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting data, filtering is performed based on the company's current business situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the geographical location of companies. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, we analyze the company's social media activities and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, We estimate the user's emotions and adjust the representation of the data analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During data analysis, adjust the level of detail of the analysis based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, When analyzing data, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned investigation department, We estimate user sentiment and adjust the survey criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned investigation department, During the survey, consider the interrelationships between data to improve the accuracy of the survey. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned investigation department, During the survey, the attribute information of the data submitters will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned display unit is, It estimates the user's emotions and adjusts the presentation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned display unit is, When presenting data, adjust the level of detail based on its importance. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned display unit is, When presenting data, different presentation algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned display unit is, It estimates the user's emotions and adjusts the order of presentation based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned display unit is, When presenting data, the priority of presentation will be determined based on when the data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned display unit is, When presenting the data, adjust the order of presentation based on its relevance. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned unit for identifying the scope of influence is: We estimate user sentiment and adjust the method of identifying the scope of impact based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned unit for identifying the scope of influence is: When identifying the scope of impact, consider the interrelationships between data to improve the accuracy of the identification. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned unit for identifying the scope of influence is: When determining the scope of impact, the attribute information of the data submitter should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned unit for identifying the scope of influence is: It estimates the user's emotions and adjusts how the scope of influence is displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned unit for identifying the scope of influence is: When determining the scope of impact, the geographical distribution of the data should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned unit for identifying the scope of influence is: When identifying the scope of impact, referencing relevant literature on the data improves the accuracy of the identification. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, The investigation unit conducts an investigation in a dialogue format when a problem occurs based on the data analyzed by the aforementioned analysis unit, A presentation unit that presents the information obtained by the aforementioned investigation unit to the user, The system includes an impact range identification unit that identifies the impact range based on the information presented by the aforementioned presentation unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect information on how companies process data. The system according to feature 1.

3. The aforementioned analysis unit, Analyze the collected data and generate a data flow diagram. The system according to feature 1.

4. The aforementioned investigation department, Investigate data flow and processing flow through an interactive format. The system according to feature 1.

5. The aforementioned display unit is, Present the user the source and path of the data. The system according to feature 1.

6. The aforementioned unit for identifying the scope of influence is: Identify the scope of impact when anomalies such as data loss occur. The system according to feature 1.

7. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze a company's past data processing history to select the optimal data collection method. The system according to feature 1.

9. The aforementioned collection unit is When collecting data, filtering is performed based on the company's current business situation and areas of interest. The system according to feature 1.

10. The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

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