system

Abstract

The system according to this embodiment aims to efficiently collect and analyze information and support rapid decision-making. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, an analysis unit, and an alert unit. The collection unit extracts information from emails, chats, calendars, and meeting minutes. The analysis unit analyzes and organizes the information extracted by the collection unit. The generation unit generates daily reports and customizable dashboards based on the information analyzed and organized by the analysis unit. The analysis unit analyzes business and project performance based on the data generated by the generation unit. The alert unit immediately issues an alert when the analysis unit detects an anomaly.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes 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 conventional technology, there is a problem that it takes time to create a daily report, data variation and omission are likely to occur, and there is a lack of information for quick decision-making.

[0005] The system according to the embodiment aims to efficiently collect and analyze information and support quick decision-making.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, an analysis unit, and an alert unit. The collection unit extracts information from emails, chats, calendars, and meeting minutes. The analysis unit analyzes and organizes the information extracted by the collection unit. The generation unit generates daily reports and customizable dashboards based on the information analyzed and organized by the analysis unit. The analysis unit analyzes business and project performance based on the data generated by the generation unit. The alert unit immediately issues an alert when the analysis unit detects an anomaly. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently collect and analyze information and support rapid decision-making. [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 numbered 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 applicable 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 Smart Report AI Agent System according to an embodiment of the present invention is a system that streamlines daily report creation and information management. To address the challenges of conventional daily report creation, which is time-consuming, prone to information inconsistency and omissions, and lacking sufficient information for rapid decision-making, the Smart Report AI Agent System has the following configuration: First, as a multi-source data integration system, the Smart Report AI Agent System extracts information from emails, chats, calendars, and meeting minutes, and the AI ​​analyzes and organizes the important content. This prevents information inconsistency and omissions. Next, as an automatic report generation and custom dashboard system, the Smart Report AI Agent System summarizes data using natural language processing to generate daily reports and customizable dashboards. This allows for real-time tracking of specific KPIs. Furthermore, as a data analysis and real-time alert system, the Smart Report AI Agent System analyzes the performance of operations and projects and issues immediate alerts when anomalies are detected. This enables rapid decision-making. Additionally, as a voice-input meeting minute creation system, the Smart Report AI Agent System processes audio during meetings in real time and automatically creates accurate meeting minutes. Finally, the Smart Report AI Agent System provides users with insights derived from aggregated data as insight delivery and learning feedback, and the AI ​​continuously learns and improves based on the feedback. This system improves operational efficiency and productivity, enhances transparency and reliability, and accelerates strategic decision-making. For example, the Smart Report AI Agent System reduces work time through automated daily report and meeting minute creation, and facilitates information sharing within the organization through integrated information management. It also promotes rapid decision-making based on real-time data and insights, strengthening the company's competitiveness. In short, the Smart Report AI Agent System can improve operational efficiency and productivity, enhance transparency and reliability, and accelerate strategic decision-making.

[0029] The Smart Report AI Agent System according to this embodiment comprises a collection unit, an analysis unit, a generation unit, an analysis unit, and an alert unit. The collection unit extracts information from emails, chats, calendars, and meeting minutes. The collection unit targets information in various formats and types, such as emails, instant messages, scheduled events, and meeting records. The collection unit can use keyword search or natural language processing techniques as methods for extracting information. The analysis unit analyzes and organizes the information extracted by the collection unit. The analysis unit uses methods such as data classification, statistical analysis, and data normalization. The generation unit generates daily reports and customizable dashboards based on the information analyzed and organized by the analysis unit. The generation unit generates daily reports and customizable dashboards by summarizing data using natural language processing, for example. The generation unit can customize the types of data displayed and how the layout is changed. The analysis unit analyzes the performance of operations and projects based on the data generated by the generation unit. The analysis unit analyzes performance using methods such as setting KPIs and comparing data. The alert unit issues an immediate alert when the analysis unit detects an anomaly. The alerting unit detects anomalies using methods such as threshold setting and anomaly pattern identification. This enables the smart report AI agent system to efficiently collect, analyze, generate, perform performance analysis, and issue alerts. Some or all of the above-described processes in the alerting unit may be performed using AI, or not. For example, the alerting unit can input the detected anomaly data into a generating AI and have the generating AI issue an alert.

[0030] The data collection unit extracts information from emails, chats, calendars, and meeting minutes. Specifically, it targets various formats and types of information, such as email bodies and attachments, instant message content, scheduled event details, and meeting records. The data collection unit can use keyword search and natural language processing techniques to extract information. For example, keyword search extracts messages and documents containing specific words or phrases, while natural language processing techniques understand the context and extract relevant information. Furthermore, the data collection unit centrally manages this information and stores it in a database. The data collection unit can collect information in real time and always maintain the latest information across the entire system. In addition, the data collection unit eliminates information duplication and redundancy, achieving efficient data management. As a result, the data collection unit can quickly and accurately collect necessary data from diverse sources and support information processing across the entire system.

[0031] The analysis unit analyzes and organizes the information extracted by the data collection unit. Specifically, it employs methods such as data classification, statistical analysis, and data normalization. For example, in data classification, collected information is organized into categories, and related data is grouped together. In statistical analysis, data trends and patterns are identified, and important indicators are extracted. In data normalization, data of different formats and units is unified to create a consistent dataset. The analysis unit combines these methods to efficiently process collected information and improve the overall data quality of the system. Furthermore, the analysis unit can use AI to perform automatic data classification and anomaly detection. For example, machine learning algorithms are used to learn patterns from past data and automatically classify new data. Also, anomaly detection algorithms are used to detect unusual data patterns, enabling early problem detection. As a result, the analysis unit can quickly and accurately analyze and organize collected information, supporting data processing throughout the entire system.

[0032] The generation unit generates daily reports and customizable dashboards based on information analyzed and organized by the analysis unit. Specifically, it uses natural language processing to summarize data and generate daily reports and customizable dashboards. The generation unit allows customization of the types of data displayed and how the layout is changed. For example, daily reports summarize the progress of important events and tasks and report them to stakeholders. Customizable dashboards allow users to change the types of data and graphs displayed according to their needs, providing information in a visually easy-to-understand format. Through these functions, the generation unit helps users quickly and efficiently obtain the information they need. Furthermore, the generation unit can use AI to automatically summarize data and generate reports. For example, it can use generation AI to generate natural language summaries based on collected data and reflect them in daily reports and dashboards. In addition, the generation unit can continuously improve the content and format of reports based on user feedback, providing a more user-friendly system. In this way, the generation unit can help users quickly and efficiently obtain the information they need and improve the overall information provision function of the system.

[0033] The analysis department analyzes the performance of business operations and projects based on data generated by the generation department. Specifically, it analyzes performance using methods such as setting KPIs and comparing data. For example, in setting KPIs, it clarifies the goals of business operations and projects and sets indicators to evaluate performance based on those goals. In data comparison methods, it evaluates current performance by comparing it with past data and data from other projects. Using these methods, the analysis department evaluates the progress and results of business operations and projects and identifies areas for improvement. Furthermore, the analysis department can perform automated data analysis and prediction using AI. For example, it can use machine learning algorithms to learn performance trends from past data and predict future performance. It can also use anomaly detection algorithms to detect unusual performance fluctuations and identify problems early. As a result, the analysis department can quickly and accurately analyze the performance of business operations and projects and support the overall performance management of the system.

[0034] The alert unit immediately issues an alert when the analysis unit detects an anomaly. Specifically, it detects anomalies using methods such as threshold setting and anomaly pattern identification. For example, threshold setting triggers an alert when a specific indicator exceeds a set threshold. Anomaly pattern identification detects data patterns that are different from the norm and identifies anomalies. Using these methods, the alert unit quickly detects anomalies and notifies relevant parties. Furthermore, the alert unit can use AI to detect anomalies and issue alerts. For example, it can use generation AI to input data where anomalies have been detected and automatically generate the content of the alert. In addition, the alert unit can reliably transmit information using multiple communication methods. For example, it can use a combination of email, instant messaging, SMS, and voice calls to reliably deliver important information. As a result, the alert unit can quickly and accurately detect anomalies and notify relevant parties, thereby improving the reliability and security of the entire system.

[0035] The meeting minutes creation department processes meeting audio in real time and automatically creates accurate meeting minutes. For example, the department uses speech recognition technology to convert meeting audio to text in real time. The department can also process meeting audio in real time using streaming data processing technology. For example, it converts meeting audio to text in real time, extracts important statements, and creates meeting minutes. The department uses speech recognition technology to improve the accuracy of speech-to-text conversion. For example, it uses speech recognition technology to convert meeting audio to text with high accuracy. The department can also use natural language processing technology to extract important statements. For example, it uses natural language processing technology to extract important statements during meetings and reflect them in the minutes. As a result, the meeting minutes creation department can process meeting audio in real time and automatically create accurate meeting minutes. Some or all of the above-described processes in the minutes creation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the minutes creation unit can input audio data from the meeting into a generation AI and have the generation AI create the minutes.

[0036] The Insights Provider provides users with insights derived from aggregated data, and the AI ​​continuously learns and improves based on the feedback. For example, the Insights Provider presents users with insights derived from the data. The Insights Provider collects user opinions and suggestions for system improvements and provides them to the AI ​​as feedback. The Insights Provider uses machine learning algorithms to enable the AI ​​to continuously learn and improve based on the feedback. For example, the Insights Provider collects user feedback, and the AI ​​learns and improves based on that feedback. The Insights Provider can also use natural language processing technology to provide users with insights derived from the data. For example, the Insights Provider uses natural language processing technology to present users with insights derived from the data. In this way, the Insights Provider provides insights, and the AI ​​continuously learns and improves based on the feedback. Some or all of the above processing in the Insights Provider may be performed using, for example, generative AI, or not using generative AI. For example, the Insights Provider can input user feedback data into a generative AI and have the generative AI perform learning and improvement.

[0037] The data collection unit analyzes the user's past information collection history and selects an appropriate collection method. For example, the data collection unit prioritizes information collection methods that the user has frequently used in the past. The data collection unit can also suggest the most efficient collection method based on the user's past collection history. The data collection unit can also analyze the user's past collection history and suggest areas for improvement in the collection method. In this way, the data collection unit can select the optimal collection method by analyzing the user's past information collection history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past information collection history data into a generating AI and have the generating AI select the optimal collection method.

[0038] The data collection unit filters information based on the user's current projects and areas of interest. For example, the data collection unit prioritizes collecting information related to the project the user is currently working on. The data collection unit can also filter highly relevant information based on the user's areas of interest. The data collection unit can also collect necessary information according to the progress of the user's project. In this way, the data collection unit can collect highly relevant information by filtering information based on the user's current projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data about the user's projects and areas of interest into a generating AI and have the generating AI perform the information filtering.

[0039] The data collection unit prioritizes collecting highly relevant information based on the user's geographical location. For example, the data collection unit prioritizes collecting information related to the user's current location. The data collection unit can also collect region-specific information based on the user's geographical location. The data collection unit can also collect highly relevant information considering the user's travel history. This enables efficient data collection by prioritizing the collection of highly relevant information while considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location data into a generating AI and have the generating AI perform the data collection.

[0040] The data collection unit analyzes the user's social media activity and collects relevant information when gathering information. For example, the data collection unit collects relevant information based on information shared by the user on social media. The data collection unit can also identify topics of interest from the user's social media activity and collect information on those topics. The data collection unit can also collect information shared by the user's social media followers and friends. This allows the data collection unit to efficiently collect relevant information by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the information collection.

[0041] The analysis unit adjusts the level of detail of the analysis based on the importance of the information during the analysis. For example, the analysis unit performs a detailed analysis on information of high importance. The analysis unit can also perform a simplified analysis on information of low importance. The analysis unit can also adjust the depth of the analysis according to the importance of the information. In this way, the analysis unit can perform efficient information analysis by adjusting the level of detail of the analysis based on the importance of the information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0042] The analysis unit applies different analysis algorithms based on the information category during analysis. For example, the analysis unit applies a specific financial analysis algorithm to financial data. The analysis unit can also apply a marketing analysis algorithm to marketing data. The analysis unit can also apply a human resources analysis algorithm to human resources data. This allows the analysis unit to perform efficient information analysis by applying different analysis algorithms according to the information category. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information category data into a generating AI and have the generating AI execute the application of analysis algorithms.

[0043] The analysis unit determines the priority of analysis based on the submission timing of the information. For example, the analysis unit prioritizes the analysis of information with an approaching submission deadline. The analysis unit can also postpone the analysis of information with ample time before the submission deadline. The analysis unit can also adjust the analysis schedule based on the submission timing. This enables efficient information analysis by allowing the analysis unit to determine the priority of analysis based on the submission timing of the information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the information submission timing data into a generating AI and have the generating AI determine the priority of analysis.

[0044] The analysis unit adjusts the order of analysis based on the relevance of the information during the analysis. For example, the analysis unit prioritizes the analysis of highly relevant information. The analysis unit can also postpone the analysis of less relevant information. The analysis unit can also adjust the order of analysis based on the relevance of the information. This allows the analysis unit to perform efficient information analysis by adjusting the order of analysis based on the relevance of the information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input information relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0045] The generation unit adjusts the level of detail in the report based on the importance of the information during report generation. For example, the generation unit generates a detailed report for information of high importance. The generation unit can also generate a simplified report for information of low importance. The generation unit can also adjust the depth of the report according to the importance of the information. This enables efficient report generation by adjusting the level of detail in the report based on the importance of the information. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input information importance data into a generation AI and have the generation AI perform the adjustment of the level of detail in the report.

[0046] The generation unit applies different generation algorithms based on the information category when generating reports. For example, the generation unit applies a specific financial report generation algorithm to financial data. The generation unit can also apply a marketing report generation algorithm to marketing data. The generation unit can also apply a human resources report generation algorithm to human resources data. This allows the generation unit to efficiently generate reports by applying different generation algorithms according to the information category. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input information category data into a generation AI and have the generation AI execute the application of the generation algorithm.

[0047] The generation unit determines the priority of reports based on the submission timing of the information when generating reports. For example, the generation unit prioritizes information with approaching submission deadlines in the report. The generation unit can also postpone information with ample time for submission. The generation unit can also adjust the report schedule based on the submission timing. This enables efficient report generation by the generation unit prioritizing reports based on the submission timing of the information. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input information submission timing data into a generation AI and have the generation AI perform the determination of report priorities.

[0048] The generation unit adjusts the order of information in the report based on the relevance of the information during report generation. For example, the generation unit prioritizes reflecting highly relevant information in the report. The generation unit can also postpone less relevant information. The generation unit can also adjust the order of information in the report based on the relevance of the information. This enables efficient report generation by adjusting the order of information in the report based on the relevance of the information. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input information relevance data into a generation AI and have the generation AI perform the adjustment of the report order.

[0049] The analysis department compares and analyzes current data based on past data during analysis. For example, the analysis department can compare and analyze current performance based on past performance data. The analysis department can also compare and analyze current projects based on past project data. The analysis department can also compare and analyze current market conditions based on past market data. This allows the analysis department to perform efficient information analysis by comparing and analyzing current data by referring to past data. Some or all of the above processes in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can input past data into a generating AI and have the generating AI perform a comparative analysis with current data.

[0050] The analysis department applies different analytical methods based on the category of information during analysis. For example, the analysis department applies a specific financial analysis method to financial data. The analysis department can also apply a marketing analysis method to marketing data. The analysis department can also apply a human resources analysis method to human resources data. This allows the analysis department to perform efficient information analysis by applying different analytical methods to each category of information. Some or all of the above processes in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input information category data into a generating AI and have the generating AI perform the application of analytical methods.

[0051] The Analysis Department analyzes changes in analysis based on the information submission timing. For example, the Analysis Department prioritizes analyzing information with an approaching submission deadline. The Analysis Department can also postpone analyzing information with ample time before the submission deadline. The Analysis Department can also adjust the analysis schedule based on the submission timing. This allows the Analysis Department to perform efficient information analysis by analyzing changes in analysis based on the information submission timing. Some or all of the above processes in the Analysis Department may be performed using AI, for example, or not using AI. For example, the Analysis Department can input information submission timing data into a generating AI and have the generating AI perform changes in analysis.

[0052] The Analysis Department performs analysis based on relevant market data. For example, the Analysis Department analyzes the current market situation based on relevant market data. The Analysis Department can also predict future market trends based on relevant market data. The Analysis Department can also analyze the trends of competitors based on relevant market data. This enables the Analysis Department to perform efficient information analysis by referring to relevant market data. Some or all of the above processes in the Analysis Department may be performed using AI, for example, or without AI. For example, the Analysis Department can input relevant market data into a generating AI and have the generating AI perform the analysis.

[0053] The alert unit adjusts the level of detail of an alert based on the severity of the anomaly when an alert is issued. For example, the alert unit issues a detailed alert for anomalies of high severity. The alert unit can also issue a simplified alert for anomalies of low severity. The alert unit can also adjust the depth of the alert according to the severity of the anomaly. This enables the alert unit to issue alerts efficiently by adjusting the level of detail of the alert based on the severity of the anomaly. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can input anomaly severity data into a generating AI and have the generating AI perform the adjustment of the level of detail of the alert.

[0054] The alert unit applies different alert algorithms based on the anomaly category when an alert is issued. For example, the alert unit applies a specific security alert algorithm to security anomalies. The alert unit can also apply a performance alert algorithm to performance anomalies. The alert unit can also apply a system alert algorithm to system anomalies. This allows the alert unit to efficiently issue alerts by applying different alert algorithms according to the anomaly category. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can input anomaly category data into a generating AI and have the generating AI execute the application of the alert algorithm.

[0055] The alert unit determines the priority of alerts based on the timing of the anomaly's occurrence when an alert is issued. For example, the alert unit prioritizes alerting on anomalies that are close in time. The alert unit can also postpone alerting on anomalies that have ample time before occurrence. The alert unit can also adjust the alert schedule based on the timing of occurrence. This enables efficient alert issuance by determining the priority of alerts based on the timing of the anomaly's occurrence. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can input anomaly occurrence timing data into a generating AI and have the generating AI perform the determination of alert priorities.

[0056] The alert unit adjusts the order of alerts based on the relevance of the anomalies when an alert is issued. For example, the alert unit prioritizes alerting on highly relevant anomalies. The alert unit can also postpone alerting on less relevant anomalies. The alert unit can also adjust the order of alerts based on the relevance of the anomalies. This allows the alert unit to issue alerts efficiently by adjusting the order of alerts based on the relevance of the anomalies. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can input anomaly relevance data into a generating AI and have the generating AI perform the adjustment of the alert order.

[0057] The minutes creation department adjusts the level of detail in meeting minutes based on the importance of the meeting. For example, the minutes creation department creates detailed minutes for highly important meetings. For less important meetings, the minutes creation department can also create simplified minutes. The minutes creation department can also adjust the depth of the minutes according to the importance of the meeting. This allows the minutes creation department to efficiently create meeting minutes by adjusting the level of detail based on the importance of the meeting. Some or all of the above processes in the minutes creation department may be performed using AI, for example, or not using AI. For example, the minutes creation department can input meeting importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in the minutes.

[0058] The minutes creation department applies different minutes creation algorithms based on the meeting category when creating meeting minutes. For example, the minutes creation department applies a specific financial minutes creation algorithm to a finance meeting. The minutes creation department can also apply a marketing minutes creation algorithm to a marketing meeting. The minutes creation department can also apply a human resources minutes creation algorithm to a human resources meeting. This allows the minutes creation department to efficiently create meeting minutes by applying different minutes creation algorithms according to the meeting category. Some or all of the above processing in the minutes creation department may be performed using AI, for example, or not using AI. For example, the minutes creation department can input meeting category data into a generating AI and have the generating AI execute the application of the minutes creation algorithm.

[0059] The minutes creation department determines the priority of meeting minutes based on the timing of the meetings. For example, the minutes creation department prioritizes creating minutes for meetings that are close to their scheduled date. The minutes creation department can also postpone creating minutes for meetings that are far in the future. The minutes creation department can also adjust the schedule for creating minutes based on the timing of the meetings. This allows the minutes creation department to efficiently create meeting minutes by determining the priority of minutes based on the timing of the meetings. Some or all of the above processes in the minutes creation department may be performed using AI, for example, or not using AI. For example, the minutes creation department can input meeting timing data into a generating AI and have the generating AI determine the priority of the minutes.

[0060] The minutes creation department adjusts the order of meeting minutes based on their relevance. For example, the minutes creation department prioritizes creating minutes for highly relevant meetings. The minutes creation department may also postpone creating minutes for less relevant meetings. The minutes creation department can also adjust the order of meeting minutes based on their relevance. This allows the minutes creation department to efficiently create meeting minutes by adjusting the order of minutes based on their relevance. Some or all of the above processes in the minutes creation department may be performed using AI, for example, or not. For example, the minutes creation department can input meeting relevance data into a generating AI and have the generating AI perform the adjustment of the order of meeting minutes.

[0061] The Insight Provision Department provides current insights by comparing them with historical data when providing insights. For example, the Insight Provision Department can provide current performance comparisons based on historical performance data. The Insight Provision Department can also provide current projects comparisons based on historical project data. The Insight Provision Department can also provide current market conditions comparisons based on historical market data. This enables the Insight Provision Department to efficiently provide insights by comparing current insights by referring to historical data. Some or all of the above processing in the Insight Provision Department may be performed using AI, for example, or without AI. For example, the Insight Provision Department can input historical data into a generating AI and have the generating AI perform the comparison and provision of current insights.

[0062] The Insights Provision Department applies different insights provision methods based on the information category when providing insights. For example, the Insights Provision Department applies a specific financial insights provision method to financial data. The Insights Provision Department can also apply a marketing insights provision method to marketing data. The Insights Provision Department can also apply a human resources insights provision method to human resources data. This allows the Insights Provision Department to efficiently provide insights by applying different insights provision methods to each information category. Some or all of the above processing in the Insights Provision Department may be performed using AI, for example, or not using AI. For example, the Insights Provision Department can input information category data into a generating AI and have the generating AI execute the application of insights provision methods.

[0063] The Insight Provision Department analyzes changes in insights based on the information submission timing when providing insights. For example, the Insight Provision Department prioritizes analyzing insights from information with an upcoming submission date. The Insight Provision Department can also postpone analyzing insights from information with ample time before submission. The Insight Provision Department can also analyze changes in insights based on submission timing. This enables the Insight Provision Department to provide insights efficiently by analyzing changes in insights based on the information submission timing. Some or all of the above processing in the Insight Provision Department may be performed using AI, for example, or without AI. For example, the Insight Provision Department can input information submission timing data into a generating AI and have the generating AI perform an analysis of changes in insights.

[0064] The Insight Provisioning Department provides insights by referring to relevant market data when providing insights. For example, the Insight Provisioning Department provides insights on the current market situation based on relevant market data. The Insight Provisioning Department can also provide insights that predict future market trends based on relevant market data. The Insight Provisioning Department can also provide insights on the actions of competitors based on relevant market data. This enables the Insight Provisioning Department to provide insights efficiently by referring to relevant market data. Some or all of the above processing in the Insight Provisioning Department may be performed using AI, for example, or not using AI. For example, the Insight Provisioning Department can input relevant market data into a generating AI and have the generating AI perform the provision of insights.

[0065] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0066] The data collection unit can also analyze the user's past information collection history and select an appropriate collection method. For example, it can prioritize selecting information collection methods that the user has frequently used in the past. The data collection unit can also suggest the most efficient collection method based on the user's past collection history. Furthermore, the data collection unit can analyze the user's past collection history and suggest improvements to the collection method. In this way, the data collection unit can select the optimal collection method by analyzing the user's past information collection history.

[0067] The analysis unit can also adjust the level of detail of the analysis based on the importance of the information. For example, it can perform a detailed analysis on highly important information, and a simplified analysis on less important information. Furthermore, the analysis unit can adjust the depth of the analysis according to the importance of the information. This allows the analysis unit to perform efficient information analysis by adjusting the level of detail based on the importance of the information.

[0068] The generation unit can also apply different generation algorithms based on the information category when generating reports. For example, a specific financial report generation algorithm can be applied to financial data. The generation unit can also apply a marketing report generation algorithm to marketing data. Furthermore, the generation unit can apply a human resources report generation algorithm to human resources data. This allows the generation unit to efficiently generate reports by applying different generation algorithms according to the information category.

[0069] The alerting unit can also adjust the level of detail of an alert based on the severity of the anomaly when issuing an alert. For example, it can issue a detailed alert for high-severity anomalies. For low-severity anomalies, it can issue a simplified alert. Furthermore, the alerting unit can adjust the depth of the alert according to the severity of the anomaly. This allows the alerting unit to efficiently issue alerts by adjusting the level of detail of the alert based on the severity of the anomaly.

[0070] The insights provision department can also provide current insights by comparing them with historical data. For example, it can compare current performance based on past performance data. The insights provision department can also compare current projects based on past project data. Furthermore, the insights provision department can compare current market conditions based on past market data. This allows the insights provision department to efficiently provide insights by referring to historical data to compare current insights.

[0071] The following briefly describes the processing flow for example form 1.

[0072] Step 1: The collection unit extracts information from emails, chats, calendars, and meeting minutes. The collection unit targets information in various formats and types, such as emails, instant messages, scheduled events, and meeting records. The collection unit can use keyword searches and natural language processing techniques to extract information. Step 2: The analysis unit analyzes and organizes the information extracted by the data collection unit. The analysis unit uses methods such as data classification, statistical analysis, and data normalization. Step 3: The generation unit generates daily reports and customizable dashboards based on the information analyzed and organized by the analysis unit. For example, the generation unit uses natural language processing to summarize the data and generate daily reports and customizable dashboards. The generation unit allows for customization of the types of data displayed and how the layout is changed. Step 4: The analysis department analyzes the performance of operations and projects based on the data generated by the generation department. The analysis department analyzes performance using methods such as setting KPIs and comparing data. Step 5: The alerting unit immediately issues an alert when the analysis unit detects an anomaly. The alerting unit detects anomalies using methods such as threshold settings and anomaly pattern identification. This enables the smart report AI agent system to efficiently collect, analyze, generate, perform performance analysis, and issue alerts.

[0073] (Example of form 2) The Smart Report AI Agent System according to an embodiment of the present invention is a system that streamlines daily report creation and information management. To address the challenges of conventional daily report creation, which is time-consuming, prone to information inconsistency and omissions, and lacking sufficient information for rapid decision-making, the Smart Report AI Agent System has the following configuration: First, as a multi-source data integration system, the Smart Report AI Agent System extracts information from emails, chats, calendars, and meeting minutes, and the AI ​​analyzes and organizes the important content. This prevents information inconsistency and omissions. Next, as an automatic report generation and custom dashboard system, the Smart Report AI Agent System summarizes data using natural language processing to generate daily reports and customizable dashboards. This allows for real-time tracking of specific KPIs. Furthermore, as a data analysis and real-time alert system, the Smart Report AI Agent System analyzes the performance of operations and projects and issues immediate alerts when anomalies are detected. This enables rapid decision-making. Additionally, as a voice-input meeting minute creation system, the Smart Report AI Agent System processes audio during meetings in real time and automatically creates accurate meeting minutes. Finally, the Smart Report AI Agent System provides users with insights derived from aggregated data as insight delivery and learning feedback, and the AI ​​continuously learns and improves based on the feedback. This system improves operational efficiency and productivity, enhances transparency and reliability, and accelerates strategic decision-making. For example, the Smart Report AI Agent System reduces work time through automated daily report and meeting minute creation, and facilitates information sharing within the organization through integrated information management. It also promotes rapid decision-making based on real-time data and insights, strengthening the company's competitiveness. In short, the Smart Report AI Agent System can improve operational efficiency and productivity, enhance transparency and reliability, and accelerate strategic decision-making.

[0074] The Smart Report AI Agent System according to this embodiment comprises a collection unit, an analysis unit, a generation unit, an analysis unit, and an alert unit. The collection unit extracts information from emails, chats, calendars, and meeting minutes. The collection unit targets information in various formats and types, such as emails, instant messages, scheduled events, and meeting records. The collection unit can use keyword search or natural language processing techniques as methods for extracting information. The analysis unit analyzes and organizes the information extracted by the collection unit. The analysis unit uses methods such as data classification, statistical analysis, and data normalization. The generation unit generates daily reports and customizable dashboards based on the information analyzed and organized by the analysis unit. The generation unit generates daily reports and customizable dashboards by summarizing data using natural language processing, for example. The generation unit can customize the types of data displayed and how the layout is changed. The analysis unit analyzes the performance of operations and projects based on the data generated by the generation unit. The analysis unit analyzes performance using methods such as setting KPIs and comparing data. The alert unit issues an immediate alert when the analysis unit detects an anomaly. The alerting unit detects anomalies using methods such as threshold setting and anomaly pattern identification. This enables the smart report AI agent system to efficiently collect, analyze, generate, perform performance analysis, and issue alerts. Some or all of the above-described processes in the alerting unit may be performed using AI, or not. For example, the alerting unit can input the detected anomaly data into a generating AI and have the generating AI issue an alert.

[0075] The data collection unit extracts information from emails, chats, calendars, and meeting minutes. Specifically, it targets various formats and types of information, such as email bodies and attachments, instant message content, scheduled event details, and meeting records. The data collection unit can use keyword search and natural language processing techniques to extract information. For example, keyword search extracts messages and documents containing specific words or phrases, while natural language processing techniques understand the context and extract relevant information. Furthermore, the data collection unit centrally manages this information and stores it in a database. The data collection unit can collect information in real time and always maintain the latest information across the entire system. In addition, the data collection unit eliminates information duplication and redundancy, achieving efficient data management. As a result, the data collection unit can quickly and accurately collect necessary data from diverse sources and support information processing across the entire system.

[0076] The analysis unit analyzes and organizes the information extracted by the data collection unit. Specifically, it employs methods such as data classification, statistical analysis, and data normalization. For example, in data classification, collected information is organized into categories, and related data is grouped together. In statistical analysis, data trends and patterns are identified, and important indicators are extracted. In data normalization, data of different formats and units is unified to create a consistent dataset. The analysis unit combines these methods to efficiently process collected information and improve the overall data quality of the system. Furthermore, the analysis unit can use AI to perform automatic data classification and anomaly detection. For example, machine learning algorithms are used to learn patterns from past data and automatically classify new data. Also, anomaly detection algorithms are used to detect unusual data patterns, enabling early problem detection. As a result, the analysis unit can quickly and accurately analyze and organize collected information, supporting data processing throughout the entire system.

[0077] The generation unit generates daily reports and customizable dashboards based on information analyzed and organized by the analysis unit. Specifically, it uses natural language processing to summarize data and generate daily reports and customizable dashboards. The generation unit allows customization of the types of data displayed and how the layout is changed. For example, daily reports summarize the progress of important events and tasks and report them to stakeholders. Customizable dashboards allow users to change the types of data and graphs displayed according to their needs, providing information in a visually easy-to-understand format. Through these functions, the generation unit helps users quickly and efficiently obtain the information they need. Furthermore, the generation unit can use AI to automatically summarize data and generate reports. For example, it can use generation AI to generate natural language summaries based on collected data and reflect them in daily reports and dashboards. In addition, the generation unit can continuously improve the content and format of reports based on user feedback, providing a more user-friendly system. In this way, the generation unit can help users quickly and efficiently obtain the information they need and improve the overall information provision function of the system.

[0078] The analysis department analyzes the performance of business operations and projects based on data generated by the generation department. Specifically, it analyzes performance using methods such as setting KPIs and comparing data. For example, in setting KPIs, it clarifies the goals of business operations and projects and sets indicators to evaluate performance based on those goals. In data comparison methods, it evaluates current performance by comparing it with past data and data from other projects. Using these methods, the analysis department evaluates the progress and results of business operations and projects and identifies areas for improvement. Furthermore, the analysis department can perform automated data analysis and prediction using AI. For example, it can use machine learning algorithms to learn performance trends from past data and predict future performance. It can also use anomaly detection algorithms to detect unusual performance fluctuations and identify problems early. As a result, the analysis department can quickly and accurately analyze the performance of business operations and projects and support the overall performance management of the system.

[0079] The alert unit immediately issues an alert when the analysis unit detects an anomaly. Specifically, it detects anomalies using methods such as threshold setting and anomaly pattern identification. For example, threshold setting triggers an alert when a specific indicator exceeds a set threshold. Anomaly pattern identification detects data patterns that are different from the norm and identifies anomalies. Using these methods, the alert unit quickly detects anomalies and notifies relevant parties. Furthermore, the alert unit can use AI to detect anomalies and issue alerts. For example, it can use generation AI to input data where anomalies have been detected and automatically generate the content of the alert. In addition, the alert unit can reliably transmit information using multiple communication methods. For example, it can use a combination of email, instant messaging, SMS, and voice calls to reliably deliver important information. As a result, the alert unit can quickly and accurately detect anomalies and notify relevant parties, thereby improving the reliability and security of the entire system.

[0080] The meeting minutes creation department processes meeting audio in real time and automatically creates accurate meeting minutes. For example, the department uses speech recognition technology to convert meeting audio to text in real time. The department can also process meeting audio in real time using streaming data processing technology. For example, it converts meeting audio to text in real time, extracts important statements, and creates meeting minutes. The department uses speech recognition technology to improve the accuracy of speech-to-text conversion. For example, it uses speech recognition technology to convert meeting audio to text with high accuracy. The department can also use natural language processing technology to extract important statements. For example, it uses natural language processing technology to extract important statements during meetings and reflect them in the minutes. As a result, the meeting minutes creation department can process meeting audio in real time and automatically create accurate meeting minutes. Some or all of the above-described processes in the minutes creation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the minutes creation unit can input audio data from the meeting into a generation AI and have the generation AI create the minutes.

[0081] The Insights Provider provides users with insights derived from aggregated data, and the AI ​​continuously learns and improves based on the feedback. For example, the Insights Provider presents users with insights derived from the data. The Insights Provider collects user opinions and suggestions for system improvements and provides them to the AI ​​as feedback. The Insights Provider uses machine learning algorithms to enable the AI ​​to continuously learn and improve based on the feedback. For example, the Insights Provider collects user feedback, and the AI ​​learns and improves based on that feedback. The Insights Provider can also use natural language processing technology to provide users with insights derived from the data. For example, the Insights Provider uses natural language processing technology to present users with insights derived from the data. In this way, the Insights Provider provides insights, and the AI ​​continuously learns and improves based on the feedback. Some or all of the above processing in the Insights Provider may be performed using, for example, generative AI, or not using generative AI. For example, the Insights Provider can input user feedback data into a generative AI and have the generative AI perform learning and improvement.

[0082] The data collection unit estimates the user's emotions and adjusts the timing of information collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the collection timing to reduce the user's burden. If the user is relaxed, the data collection unit can also speed up the collection timing to collect information efficiently. If the user is in a hurry, the data collection unit can set the collection timing immediately to collect information quickly. In this way, the data collection unit can efficiently collect information by adjusting the timing of information collection based on the user's emotions. 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 data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the timing of information collection.

[0083] The data collection unit analyzes the user's past information collection history and selects an appropriate collection method. For example, the data collection unit prioritizes information collection methods that the user has frequently used in the past. The data collection unit can also suggest the most efficient collection method based on the user's past collection history. The data collection unit can also analyze the user's past collection history and suggest areas for improvement in the collection method. In this way, the data collection unit can select the optimal collection method by analyzing the user's past information collection history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past information collection history data into a generating AI and have the generating AI select the optimal collection method.

[0084] The data collection unit filters information based on the user's current projects and areas of interest. For example, the data collection unit prioritizes collecting information related to the project the user is currently working on. The data collection unit can also filter highly relevant information based on the user's areas of interest. The data collection unit can also collect necessary information according to the progress of the user's project. In this way, the data collection unit can collect highly relevant information by filtering information based on the user's current projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data about the user's projects and areas of interest into a generating AI and have the generating AI perform the information filtering.

[0085] The data collection unit estimates the user's emotions and determines the priority of information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will postpone collecting less important information. If the user is relaxed, the data collection unit may prioritize collecting more important information. If the user is in a hurry, the data collection unit may prioritize collecting the most important information. This enables efficient information collection by prioritizing information based on the user's emotions. 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 data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform the determination of information prioritization.

[0086] The data collection unit prioritizes collecting highly relevant information based on the user's geographical location. For example, the data collection unit prioritizes collecting information related to the user's current location. The data collection unit can also collect region-specific information based on the user's geographical location. The data collection unit can also collect highly relevant information considering the user's travel history. This enables efficient data collection by prioritizing the collection of highly relevant information while considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location data into a generating AI and have the generating AI perform the data collection.

[0087] The data collection unit analyzes the user's social media activity and collects relevant information when gathering information. For example, the data collection unit collects relevant information based on information shared by the user on social media. The data collection unit can also identify topics of interest from the user's social media activity and collect information on those topics. The data collection unit can also collect information shared by the user's social media followers and friends. This allows the data collection unit to efficiently collect relevant information by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the information collection.

[0088] The analysis unit estimates the user's emotions and adjusts the presentation of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit provides simple and easy-to-understand analysis results. If the user is relaxed, the analysis unit can also provide detailed analysis results. If the user is in a hurry, the analysis unit can provide concise analysis results. This allows the analysis unit to perform efficient information analysis by adjusting the presentation of the analysis based on the user's emotions. 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 analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the presentation of the analysis.

[0089] The analysis unit adjusts the level of detail of the analysis based on the importance of the information during the analysis. For example, the analysis unit performs a detailed analysis on information of high importance. The analysis unit can also perform a simplified analysis on information of low importance. The analysis unit can also adjust the depth of the analysis according to the importance of the information. In this way, the analysis unit can perform efficient information analysis by adjusting the level of detail of the analysis based on the importance of the information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0090] The analysis unit applies different analysis algorithms based on the information category during analysis. For example, the analysis unit applies a specific financial analysis algorithm to financial data. The analysis unit can also apply a marketing analysis algorithm to marketing data. The analysis unit can also apply a human resources analysis algorithm to human resources data. This allows the analysis unit to perform efficient information analysis by applying different analysis algorithms according to the information category. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information category data into a generating AI and have the generating AI execute the application of analysis algorithms.

[0091] The analysis unit estimates the user's emotions and adjusts the length of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit provides a short, concise analysis. If the user is relaxed, the analysis unit can also provide a detailed analysis. If the user is in a hurry, the analysis unit can adjust the length of the analysis for quick understanding. This allows the analysis unit to perform efficient information analysis by adjusting the length of the analysis based on the user's emotions. 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 analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the length of the analysis.

[0092] The analysis unit determines the priority of analysis based on the submission timing of the information. For example, the analysis unit prioritizes the analysis of information with an approaching submission deadline. The analysis unit can also postpone the analysis of information with ample time before the submission deadline. The analysis unit can also adjust the analysis schedule based on the submission timing. This enables efficient information analysis by allowing the analysis unit to determine the priority of analysis based on the submission timing of the information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the information submission timing data into a generating AI and have the generating AI determine the priority of analysis.

[0093] The analysis unit adjusts the order of analysis based on the relevance of the information during the analysis. For example, the analysis unit prioritizes the analysis of highly relevant information. The analysis unit can also postpone the analysis of less relevant information. The analysis unit can also adjust the order of analysis based on the relevance of the information. This allows the analysis unit to perform efficient information analysis by adjusting the order of analysis based on the relevance of the information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input information relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0094] The generation unit estimates the user's emotions and adjusts the presentation of the generated report based on the estimated emotions. For example, if the user is stressed, the generation unit generates a simple and easy-to-read report. If the user is relaxed, the generation unit can also generate a detailed report. If the user is in a hurry, the generation unit can generate a concise report. This allows the generation unit to efficiently generate reports by adjusting the presentation of the report based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation 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 generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the presentation of the report.

[0095] The generation unit adjusts the level of detail in the report based on the importance of the information during report generation. For example, the generation unit generates a detailed report for information of high importance. The generation unit can also generate a simplified report for information of low importance. The generation unit can also adjust the depth of the report according to the importance of the information. This enables efficient report generation by adjusting the level of detail in the report based on the importance of the information. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input information importance data into a generation AI and have the generation AI perform the adjustment of the level of detail in the report.

[0096] The generation unit applies different generation algorithms based on the information category when generating reports. For example, the generation unit applies a specific financial report generation algorithm to financial data. The generation unit can also apply a marketing report generation algorithm to marketing data. The generation unit can also apply a human resources report generation algorithm to human resources data. This allows the generation unit to efficiently generate reports by applying different generation algorithms according to the information category. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input information category data into a generation AI and have the generation AI execute the application of the generation algorithm.

[0097] The generation unit estimates the user's emotions and adjusts the length of the report it generates based on the estimated emotions. For example, if the user is stressed, the generation unit generates a short, concise report. If the user is relaxed, the generation unit can also generate a detailed report. If the user is in a hurry, the generation unit can also adjust the length of the report to allow for quick understanding. This enables efficient report generation by adjusting the length of the report generated based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation 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 generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into a generation AI and have the generation AI adjust the length of the report.

[0098] The generation unit determines the priority of reports based on the submission timing of the information when generating reports. For example, the generation unit prioritizes information with approaching submission deadlines in the report. The generation unit can also postpone information with ample time for submission. The generation unit can also adjust the report schedule based on the submission timing. This enables efficient report generation by the generation unit prioritizing reports based on the submission timing of the information. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input information submission timing data into a generation AI and have the generation AI perform the determination of report priorities.

[0099] The generation unit adjusts the order of information in the report based on the relevance of the information during report generation. For example, the generation unit prioritizes reflecting highly relevant information in the report. The generation unit can also postpone less relevant information. The generation unit can also adjust the order of information in the report based on the relevance of the information. This enables efficient report generation by adjusting the order of information in the report based on the relevance of the information. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input information relevance data into a generation AI and have the generation AI perform the adjustment of the report order.

[0100] The analysis unit estimates the user's emotions and adjusts the analysis method based on the estimated emotions. For example, if the user is stressed, the analysis unit provides a simple and easy-to-understand analysis method. If the user is relaxed, the analysis unit can also provide a detailed analysis method. If the user is in a hurry, the analysis unit can provide a concise analysis method. This allows the analysis unit to perform efficient information analysis by adjusting the analysis method based on the user's emotions. 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 analysis unit may be performed using AI or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the analysis method.

[0101] The analysis department compares and analyzes current data based on past data during analysis. For example, the analysis department can compare and analyze current performance based on past performance data. The analysis department can also compare and analyze current projects based on past project data. The analysis department can also compare and analyze current market conditions based on past market data. This allows the analysis department to perform efficient information analysis by comparing and analyzing current data by referring to past data. Some or all of the above processes in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can input past data into a generating AI and have the generating AI perform a comparative analysis with current data.

[0102] The analysis department applies different analytical methods based on the category of information during analysis. For example, the analysis department applies a specific financial analysis method to financial data. The analysis department can also apply a marketing analysis method to marketing data. The analysis department can also apply a human resources analysis method to human resources data. This allows the analysis department to perform efficient information analysis by applying different analytical methods to each category of information. Some or all of the above processes in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input information category data into a generating AI and have the generating AI perform the application of analytical methods.

[0103] The analysis department estimates the user's emotions and determines the priority of analysis based on the estimated emotions. For example, if the user is stressed, the analysis department will postpone less important analyses. If the user is relaxed, the analysis department may prioritize more important analyses. If the user is in a hurry, the analysis department may perform the most important analyses immediately. This allows the analysis department to perform efficient information analysis by determining the priority of analysis based on the user's emotions. 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 analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input user emotion data into a generative AI and have the generative AI determine the priority of analysis.

[0104] The Analysis Department analyzes changes in analysis based on the information submission timing. For example, the Analysis Department prioritizes analyzing information with an approaching submission deadline. The Analysis Department can also postpone analyzing information with ample time before the submission deadline. The Analysis Department can also adjust the analysis schedule based on the submission timing. This allows the Analysis Department to perform efficient information analysis by analyzing changes in analysis based on the information submission timing. Some or all of the above processes in the Analysis Department may be performed using AI, for example, or not using AI. For example, the Analysis Department can input information submission timing data into a generating AI and have the generating AI perform changes in analysis.

[0105] The Analysis Department performs analysis based on relevant market data. For example, the Analysis Department analyzes the current market situation based on relevant market data. The Analysis Department can also predict future market trends based on relevant market data. The Analysis Department can also analyze the trends of competitors based on relevant market data. This enables the Analysis Department to perform efficient information analysis by referring to relevant market data. Some or all of the above processes in the Analysis Department may be performed using AI, for example, or without AI. For example, the Analysis Department can input relevant market data into a generating AI and have the generating AI perform the analysis.

[0106] The alert unit estimates the user's emotions and adjusts the alert delivery method based on the estimated emotions. For example, if the user is stressed, the alert unit will send a simple, highly visible alert. If the user is relaxed, the alert unit can also send a detailed alert. If the user is in a hurry, the alert unit can send a concise alert. This allows the alert unit to efficiently send alerts by adjusting the alert delivery method based on the user's emotions. 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 alert unit may be performed using AI or not using AI. For example, the alert unit can input user emotion data into a generative AI and have the generative AI adjust the alert delivery method.

[0107] The alert unit adjusts the level of detail of an alert based on the severity of the anomaly when an alert is issued. For example, the alert unit issues a detailed alert for anomalies of high severity. The alert unit can also issue a simplified alert for anomalies of low severity. The alert unit can also adjust the depth of the alert according to the severity of the anomaly. This enables the alert unit to issue alerts efficiently by adjusting the level of detail of the alert based on the severity of the anomaly. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can input anomaly severity data into a generating AI and have the generating AI perform the adjustment of the level of detail of the alert.

[0108] The alert unit applies different alert algorithms based on the anomaly category when an alert is issued. For example, the alert unit applies a specific security alert algorithm to security anomalies. The alert unit can also apply a performance alert algorithm to performance anomalies. The alert unit can also apply a system alert algorithm to system anomalies. This allows the alert unit to efficiently issue alerts by applying different alert algorithms according to the anomaly category. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can input anomaly category data into a generating AI and have the generating AI execute the application of the alert algorithm.

[0109] The alert unit estimates the user's emotions and determines the priority of alerts based on the estimated emotions. For example, if the user is stressed, the alert unit will postpone less important alerts. If the user is relaxed, the alert unit can also prioritize sending high-priority alerts. If the user is in a hurry, the alert unit can immediately send the most important alerts. This allows the alert unit to efficiently send alerts by determining the priority of alerts based on the user's emotions. 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 alert unit may be performed using AI or not using AI. For example, the alert unit can input user emotion data into a generative AI and have the generative AI determine the priority of alerts.

[0110] The alert unit determines the priority of alerts based on the timing of the anomaly's occurrence when an alert is issued. For example, the alert unit prioritizes alerting on anomalies that are close in time. The alert unit can also postpone alerting on anomalies that have ample time before occurrence. The alert unit can also adjust the alert schedule based on the timing of occurrence. This enables efficient alert issuance by determining the priority of alerts based on the timing of the anomaly's occurrence. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can input anomaly occurrence timing data into a generating AI and have the generating AI perform the determination of alert priorities.

[0111] The alert unit adjusts the order of alerts based on the relevance of the anomalies when an alert is issued. For example, the alert unit prioritizes alerting on highly relevant anomalies. The alert unit can also postpone alerting on less relevant anomalies. The alert unit can also adjust the order of alerts based on the relevance of the anomalies. This allows the alert unit to issue alerts efficiently by adjusting the order of alerts based on the relevance of the anomalies. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can input anomaly relevance data into a generating AI and have the generating AI perform the adjustment of the alert order.

[0112] The meeting minutes creation department estimates the user's emotions and adjusts the presentation of the meeting minutes based on the estimated emotions. For example, if the user is stressed, the meeting minutes creation department will create simple and easy-to-read minutes. If the user is relaxed, the meeting minutes creation department can also create detailed minutes. If the user is in a hurry, the meeting minutes creation department can create concise minutes. This allows the meeting minutes creation department to efficiently create meeting minutes by adjusting the presentation based on the user's emotions. 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 meeting minutes creation department may be performed using AI or not. For example, the meeting minutes creation department can input user emotion data into a generative AI and have the generative AI adjust the presentation of the meeting minutes.

[0113] The minutes creation department adjusts the level of detail in meeting minutes based on the importance of the meeting. For example, the minutes creation department creates detailed minutes for highly important meetings. For less important meetings, the minutes creation department can also create simplified minutes. The minutes creation department can also adjust the depth of the minutes according to the importance of the meeting. This allows the minutes creation department to efficiently create meeting minutes by adjusting the level of detail based on the importance of the meeting. Some or all of the above processes in the minutes creation department may be performed using AI, for example, or not using AI. For example, the minutes creation department can input meeting importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in the minutes.

[0114] The minutes creation department applies different minutes creation algorithms based on the meeting category when creating meeting minutes. For example, the minutes creation department applies a specific financial minutes creation algorithm to a finance meeting. The minutes creation department can also apply a marketing minutes creation algorithm to a marketing meeting. The minutes creation department can also apply a human resources minutes creation algorithm to a human resources meeting. This allows the minutes creation department to efficiently create meeting minutes by applying different minutes creation algorithms according to the meeting category. Some or all of the above processing in the minutes creation department may be performed using AI, for example, or not using AI. For example, the minutes creation department can input meeting category data into a generating AI and have the generating AI execute the application of the minutes creation algorithm.

[0115] The meeting minutes creation unit estimates the user's emotions and adjusts the length of the meeting minutes based on the estimated emotions. For example, if the user is stressed, the meeting minutes creation unit will create short, concise minutes. If the user is relaxed, the meeting minutes creation unit can also create detailed minutes. If the user is in a hurry, the meeting minutes creation unit can also adjust the length of the minutes to allow for quick understanding. This enables efficient meeting minutes creation by adjusting the length of the minutes based on the user's emotions. 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 meeting minutes creation unit may be performed using AI or not. For example, the meeting minutes creation unit can input user emotion data into a generative AI and have the generative AI adjust the length of the meeting minutes.

[0116] The minutes creation department determines the priority of meeting minutes based on the timing of the meetings. For example, the minutes creation department prioritizes creating minutes for meetings that are close to their scheduled date. The minutes creation department can also postpone creating minutes for meetings that are far in the future. The minutes creation department can also adjust the schedule for creating minutes based on the timing of the meetings. This allows the minutes creation department to efficiently create meeting minutes by determining the priority of minutes based on the timing of the meetings. Some or all of the above processes in the minutes creation department may be performed using AI, for example, or not using AI. For example, the minutes creation department can input meeting timing data into a generating AI and have the generating AI determine the priority of the minutes.

[0117] The minutes creation department adjusts the order of meeting minutes based on their relevance. For example, the minutes creation department prioritizes creating minutes for highly relevant meetings. The minutes creation department may also postpone creating minutes for less relevant meetings. The minutes creation department can also adjust the order of meeting minutes based on their relevance. This allows the minutes creation department to efficiently create meeting minutes by adjusting the order of minutes based on their relevance. Some or all of the above processes in the minutes creation department may be performed using AI, for example, or not. For example, the minutes creation department can input meeting relevance data into a generating AI and have the generating AI perform the adjustment of the order of meeting minutes.

[0118] The Insights Provider Unit estimates the user's emotions and adjusts the way insights are delivered based on the estimated emotions. For example, if the user is stressed, the Insights Provider Unit provides simple and highly visible insights. If the user is relaxed, the Insights Provider Unit can also provide detailed insights. If the user is in a hurry, the Insights Provider Unit can provide concise insights. This allows the Insights Provider Unit to efficiently deliver insights by adjusting the way insights are delivered based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 Insights Provider Unit may be performed using AI or not. For example, the Insights Provider Unit can input user emotion data into a generative AI and have the generative AI adjust the way insights are delivered.

[0119] The Insight Provision Department provides current insights by comparing them with historical data when providing insights. For example, the Insight Provision Department can provide current performance comparisons based on historical performance data. The Insight Provision Department can also provide current projects comparisons based on historical project data. The Insight Provision Department can also provide current market conditions comparisons based on historical market data. This enables the Insight Provision Department to efficiently provide insights by comparing current insights by referring to historical data. Some or all of the above processing in the Insight Provision Department may be performed using AI, for example, or without AI. For example, the Insight Provision Department can input historical data into a generating AI and have the generating AI perform the comparison and provision of current insights.

[0120] The Insights Provision Department applies different insights provision methods based on the information category when providing insights. For example, the Insights Provision Department applies a specific financial insights provision method to financial data. The Insights Provision Department can also apply a marketing insights provision method to marketing data. The Insights Provision Department can also apply a human resources insights provision method to human resources data. This allows the Insights Provision Department to efficiently provide insights by applying different insights provision methods to each information category. Some or all of the above processing in the Insights Provision Department may be performed using AI, for example, or not using AI. For example, the Insights Provision Department can input information category data into a generating AI and have the generating AI execute the application of insights provision methods.

[0121] The Insights Provider Unit estimates the user's emotions and prioritizes insights based on the estimated emotions. For example, if the user is stressed, the Insights Provider Unit will postpone less important insights. If the user is relaxed, the Insights Provider Unit may prioritize providing more important insights. If the user is in a hurry, the Insights Provider Unit may immediately provide the most important insights. This allows the Insights Provider Unit to efficiently provide insights by prioritizing insights based on 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 Insights Provider Unit may be performed using AI or not. For example, the Insights Provider Unit can input user emotion data into a generative AI and have the generative AI determine the priority of insights.

[0122] The Insight Provision Department analyzes changes in insights based on the information submission timing when providing insights. For example, the Insight Provision Department prioritizes analyzing insights from information with an upcoming submission date. The Insight Provision Department can also postpone analyzing insights from information with ample time before submission. The Insight Provision Department can also analyze changes in insights based on submission timing. This enables the Insight Provision Department to provide insights efficiently by analyzing changes in insights based on the information submission timing. Some or all of the above processing in the Insight Provision Department may be performed using AI, for example, or without AI. For example, the Insight Provision Department can input information submission timing data into a generating AI and have the generating AI perform an analysis of changes in insights.

[0123] The Insight Provisioning Department provides insights by referring to relevant market data when providing insights. For example, the Insight Provisioning Department provides insights on the current market situation based on relevant market data. The Insight Provisioning Department can also provide insights that predict future market trends based on relevant market data. The Insight Provisioning Department can also provide insights on the actions of competitors based on relevant market data. This enables the Insight Provisioning Department to provide insights efficiently by referring to relevant market data. Some or all of the above processing in the Insight Provisioning Department may be performed using AI, for example, or not using AI. For example, the Insight Provisioning Department can input relevant market data into a generating AI and have the generating AI perform the provision of insights.

[0124] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0125] The data collection unit can also estimate the user's emotions and adjust its information collection methods based on those estimates. For example, if the user is stressed, the unit can reduce the frequency of information collection to avoid burdening the user. If the user is relaxed, the unit can increase the frequency of information collection and collect more information. Also, if the user is in a hurry, the unit can prioritize the collection of important information and provide it quickly. In this way, the data collection unit can efficiently collect information by adjusting its methods based on the user's emotions.

[0126] The analysis unit can also estimate the user's emotions and prioritize analyses based on those emotions. For example, if the user is stressed, the analysis unit can postpone less important analyses and prioritize more important ones. If the user is relaxed, the analysis unit can perform a detailed analysis and provide it to the user. If the user is in a hurry, the analysis unit can perform a concise analysis to ensure quick understanding. In this way, the analysis unit can efficiently analyze information by prioritizing analyses based on the user's emotions.

[0127] The generation unit can also estimate the user's emotions and adjust the content of the generated report based on those emotions. For example, if the user is stressed, the generation unit can generate a simple and easy-to-understand report. If the user is relaxed, the generation unit can generate and provide a detailed report. If the user is in a hurry, the generation unit can generate a concise report and provide it quickly. In this way, the generation unit can efficiently generate reports by adjusting the content of the reports based on the user's emotions.

[0128] The alerting unit can also estimate the user's emotions and adjust how alerts are delivered based on those emotions. For example, if the user is stressed, the alerting unit can deliver a simple, highly visible alert. If the user is relaxed, the alerting unit can deliver a more detailed alert. If the user is in a hurry, the alerting unit can deliver a concise and quick alert. In this way, the alerting unit can efficiently deliver alerts by adjusting how alerts are delivered based on the user's emotions.

[0129] The insights delivery unit can also estimate the user's emotions and adjust how insights are delivered based on those emotions. For example, if a user is stressed, the insights delivery unit can provide simple, easy-to-understand insights. If a user is relaxed, the insights delivery unit can provide detailed insights. If a user is in a hurry, the insights delivery unit can provide concise, to-the-point insights quickly. This allows the insights delivery unit to efficiently deliver insights by adjusting how they are delivered based on the user's emotions.

[0130] The data collection unit can also analyze the user's past information collection history and select an appropriate collection method. For example, it can prioritize selecting information collection methods that the user has frequently used in the past. The data collection unit can also suggest the most efficient collection method based on the user's past collection history. Furthermore, the data collection unit can analyze the user's past collection history and suggest improvements to the collection method. In this way, the data collection unit can select the optimal collection method by analyzing the user's past information collection history.

[0131] The analysis unit can also adjust the level of detail of the analysis based on the importance of the information. For example, it can perform a detailed analysis on highly important information, and a simplified analysis on less important information. Furthermore, the analysis unit can adjust the depth of the analysis according to the importance of the information. This allows the analysis unit to perform efficient information analysis by adjusting the level of detail based on the importance of the information.

[0132] The generation unit can also apply different generation algorithms based on the information category when generating reports. For example, a specific financial report generation algorithm can be applied to financial data. The generation unit can also apply a marketing report generation algorithm to marketing data. Furthermore, the generation unit can apply a human resources report generation algorithm to human resources data. This allows the generation unit to efficiently generate reports by applying different generation algorithms according to the information category.

[0133] The alerting unit can also adjust the level of detail of an alert based on the severity of the anomaly when issuing an alert. For example, it can issue a detailed alert for high-severity anomalies. For low-severity anomalies, it can issue a simplified alert. Furthermore, the alerting unit can adjust the depth of the alert according to the severity of the anomaly. This allows the alerting unit to efficiently issue alerts by adjusting the level of detail of the alert based on the severity of the anomaly.

[0134] The insights provision department can also provide current insights by comparing them with historical data. For example, it can compare current performance based on past performance data. The insights provision department can also compare current projects based on past project data. Furthermore, the insights provision department can compare current market conditions based on past market data. This allows the insights provision department to efficiently provide insights by referring to historical data to compare current insights.

[0135] The following briefly describes the processing flow for example form 2.

[0136] Step 1: The collection unit extracts information from emails, chats, calendars, and meeting minutes. The collection unit targets information in various formats and types, such as emails, instant messages, scheduled events, and meeting records. The collection unit can use keyword searches and natural language processing techniques to extract information. Step 2: The analysis unit analyzes and organizes the information extracted by the data collection unit. The analysis unit uses methods such as data classification, statistical analysis, and data normalization. Step 3: The generation unit generates daily reports and customizable dashboards based on the information analyzed and organized by the analysis unit. For example, the generation unit uses natural language processing to summarize the data and generate daily reports and customizable dashboards. The generation unit allows for customization of the types of data displayed and how the layout is changed. Step 4: The analysis department analyzes the performance of operations and projects based on the data generated by the generation department. The analysis department analyzes performance using methods such as setting KPIs and comparing data. Step 5: The alerting unit immediately issues an alert when the analysis unit detects an anomaly. The alerting unit detects anomalies using methods such as threshold settings and anomaly pattern identification. This enables the smart report AI agent system to efficiently collect, analyze, generate, perform performance analysis, and issue alerts.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, analysis unit, alert unit, meeting minutes creation unit, and insight provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the smart device 14 and extracts information from emails, chats, calendars, and meeting minutes. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes and organizes the extracted information. The generation unit is implemented by the control unit 46A of the smart device 14 and generates daily reports and customizable dashboards. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the performance of operations and projects. The alert unit is implemented by the control unit 46A of the smart device 14 and immediately issues an alert when an anomaly is detected. The meeting minutes creation unit is implemented by the computer 36 of the smart device 14 and processes the audio during meetings in real time and automatically creates accurate meeting minutes. The insight provision unit is implemented by the specific processing unit 290 of the data processing unit 12, providing the user with insights derived from aggregated data, and the AI ​​continuously learns and improves based on the feedback. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0141] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, analysis unit, alert unit, meeting minutes creation unit, and insight provision unit, is implemented by at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the smart glasses 214 and extracts information from emails, chats, calendars, and meeting minutes. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes and organizes the extracted information. The generation unit is implemented by the control unit 46A of the smart glasses 214 and generates daily reports and customizable dashboards. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the performance of operations and projects. The alert unit is implemented by the control unit 46A of the smart glasses 214 and issues an immediate alert when an anomaly is detected. The meeting minutes creation unit is implemented by the computer 36 of the smart glasses 214 and processes the audio during meetings in real time and automatically creates accurate meeting minutes. The insight provision unit is implemented by the specific processing unit 290 of the data processing unit 12, providing the user with insights derived from aggregated data, and the AI ​​continuously learns and improves based on the feedback. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0157] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.).

[0169] 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.

[0170] 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.

[0171] 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.

[0172] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, data analysis unit, alert unit, meeting minutes creation unit, and insight provision unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the headset terminal 314 and extracts information from emails, chats, calendars, and meeting minutes. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes and organizes the extracted information. The generation unit is implemented by the control unit 46A of the headset terminal 314 and generates daily reports and customizable dashboards. The data analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the performance of operations and projects. The alert unit is implemented by the control unit 46A of the headset terminal 314 and immediately issues an alert when an anomaly is detected. The meeting minutes creation unit is implemented by the computer 36 of the headset terminal 314 and processes the audio during meetings in real time and automatically creates accurate meeting minutes. The insight provision unit is implemented by the specific processing unit 290 of the data processing unit 12, providing the user with insights derived from aggregated data, and the AI ​​continuously learns and improves based on the feedback. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0173] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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).

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.).

[0186] 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.

[0187] 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.

[0188] 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.

[0189] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, analysis unit, alert unit, meeting minutes creation unit, and insight provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the robot 414 and extracts information from emails, chats, calendars, and meeting minutes. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes and organizes the extracted information. The generation unit is implemented by the control unit 46A of the robot 414 and generates daily reports and customizable dashboards. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the performance of operations and projects. The alert unit is implemented by the control unit 46A of the robot 414 and issues an immediate alert when an anomaly is detected. The meeting minutes creation unit is implemented by the computer 36 of the robot 414 and processes the audio during meetings in real time and automatically creates accurate meeting minutes. The insight provision unit is implemented by the specific processing unit 290 of the data processing unit 12, providing the user with insights derived from aggregated data, and the AI ​​continuously learns and improves based on the feedback. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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."

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] (Note 1) A collection unit that extracts information from emails, chats, calendars, and meeting minutes, An analysis unit analyzes and organizes the information extracted by the aforementioned collection unit, A generation unit that generates daily reports and customizable dashboards based on the information analyzed and organized by the aforementioned analysis unit, An analysis unit analyzes the performance of business and projects based on the data generated by the generation unit, The system includes an alert unit that immediately issues an alert when an abnormality is detected by the aforementioned analysis unit. A system characterized by the following features. (Note 2) It features a meeting minutes creation unit that processes meeting audio in real time and automatically generates accurate meeting minutes. The system described in Appendix 1, characterized by the features described herein. (Note 3) It provides users with insights derived from aggregated data, and features an insights provision unit where AI continuously learns and improves based on feedback. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is Analyze the user's past information gathering history and select the appropriate collection method. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is When collecting information, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting information, the system prioritizes collecting highly relevant information based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting information, we analyze the user's social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During analysis, different analysis algorithms are applied based on the information category. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the 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 the analysis, the priority of the analysis is determined based on when the information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is We estimate user sentiment and adjust how reports are presented based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating a report, adjust the level of detail in the report based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating reports, different generation algorithms are applied based on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is It estimates user sentiment and adjusts the length of the report generated based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is When generating reports, prioritize reports based on when the information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is When generating reports, the order of the reports is adjusted based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit is It estimates the user's emotions and adjusts the analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit is During analysis, current data is compared and analyzed based on past data. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned analysis unit is During analysis, different analytical methods are applied based on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned analysis unit is We estimate the user's emotions and prioritize the analysis based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned analysis unit is During the analysis, we analyze changes in the analysis based on the timing of information submission. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned analysis unit is During the analysis, the analysis is conducted based on relevant market data. The system described in Appendix 1, characterized by the features described herein. (Note 28) The alert unit is, It estimates the user's emotions and adjusts how alerts are sent based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The alert unit is, When an alert is issued, adjust the level of detail of the alert based on the severity of the anomaly. The system described in Appendix 1, characterized by the features described herein. (Note 30) The alert unit is, When an alert is issued, a different alert algorithm is applied based on the category of the anomaly. The system described in Appendix 1, characterized by the features described herein. (Note 31) The alert unit is, It estimates the user's emotions and determines the priority of alerts based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The alert unit is, When an alert is issued, the priority of the alert is determined based on when the anomaly occurred. The system described in Appendix 1, characterized by the features described herein. (Note 33) The alert unit is, When an alert is issued, the order of the alerts is adjusted based on the correlation of the anomalies. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned minutes preparation department, The system estimates the user's emotions and adjusts the way the meeting minutes are written based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned minutes preparation department, When creating meeting minutes, adjust the level of detail based on the importance of the meeting. The system described in Appendix 2, characterized by the features described herein. (Note 36) The aforementioned minutes preparation department, When creating meeting minutes, different minute-taking algorithms are applied based on the meeting category. The system described in Appendix 2, characterized by the features described herein. (Note 37) The aforementioned minutes preparation department, It estimates the user's emotions and adjusts the length of the meeting minutes based on the estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 38) The aforementioned minutes preparation department, When creating meeting minutes, prioritize the minutes based on when the meeting was held. The system described in Appendix 2, characterized by the features described herein. (Note 39) The aforementioned minutes preparation department, When creating meeting minutes, adjust the order of the minutes based on the relevance of the meeting. The system described in Appendix 2, characterized by the features described herein. (Note 40) The aforementioned insight provision unit, We estimate user sentiment and adjust how we deliver insights based on that estimated sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 41) The aforementioned insight provision unit, When providing insights, we compare current insights with past data. The system described in Appendix 3, characterized by the features described herein. (Note 42) The aforementioned insight provision unit, When providing insights, apply different insight-providing methods based on the category of information. The system described in Appendix 3, characterized by the features described herein. (Note 43) The aforementioned insight provision unit, It estimates user sentiment and prioritizes insights based on the estimated user sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 44) The aforementioned insight provision unit, When providing insights, we analyze how those insights change based on when the information was submitted. The system described in Appendix 3, characterized by the features described herein. (Note 45) The aforementioned insight provision unit, When providing insights, we provide insights based on relevant market data. The system described in Appendix 3, characterized by the features described herein. [Explanation of Symbols]

[0209] 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 collection unit that extracts information from emails, chats, calendars, and meeting minutes, An analysis unit analyzes and organizes the information extracted by the aforementioned collection unit, A generation unit that generates daily reports and customizable dashboards based on the information analyzed and organized by the aforementioned analysis unit, An analysis unit analyzes the performance of business and projects based on the data generated by the generation unit, The system includes an alert unit that immediately issues an alert when an abnormality is detected by the aforementioned analysis unit. A system characterized by the following features.

2. It features a meeting minutes creation unit that processes meeting audio in real time and automatically generates accurate meeting minutes. The system according to feature 1.

3. It includes an insights provision unit that provides users with insights derived from aggregated data, and whose AI continuously learns and improves based on feedback. The system according to feature 1.

4. The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system according to feature 1.

5. The aforementioned collection unit is Analyze the user's past information gathering history and select the appropriate collection method. The system according to feature 1.

6. The aforementioned collection unit is When collecting information, filtering is performed based on the user's current projects and areas of interest. The system according to feature 1.

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

8. The aforementioned collection unit is When collecting information, the system prioritizes collecting highly relevant information based on the user's geographical location. The system according to feature 1.

9. The aforementioned collection unit is When collecting information, we analyze the user's social media activity and collect relevant information. The system according to feature 1.

10. The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system according to feature 1.

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