system

Abstract

The system according to this embodiment aims to efficiently identify and periodically present issues and abnormal conditions in specific tasks or projects. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, and an output unit. The data collection unit collects data related to a specific task or project. The analysis unit analyzes the data collected by the data collection unit to identify issues and abnormal conditions. The output unit outputs the issues and abnormal conditions identified by the analysis unit as a report.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to efficiently discover problems and abnormal states in specific operations or projects and present them regularly.

[0005] The system according to the embodiment aims to efficiently discover problems and abnormal states in specific operations or projects and present them regularly.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, and an output unit. The data collection unit collects data related to a specific task or project. The analysis unit analyzes the data collected by the data collection unit to identify issues and abnormal conditions. The output unit outputs the issues and abnormal conditions identified by the analysis unit as a report. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently detect and periodically present issues and abnormal conditions in specific tasks or projects. [Brief explanation of the drawing]

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

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

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

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

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

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing apparatus 12 and a smart device 14. An example of the data processing apparatus 12 is a server.

[0018] The data processing apparatus 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) An AI agent system according to an embodiment of the present invention is a system that periodically presents abstract and specific issues in a particular task or project, regardless of whether they are consciously recognized by the members. This AI agent system monitors chat content in channels of communication tools used in a particular task or project, internal wikis, project documents, member names, etc., as input sources, and identifies and presents issues over periods such as the past few years or the past week. For example, the AI ​​agent system monitors chat content in channels of communication tools related to a particular task or project, internal wikis, project documents, member names, etc. For example, it may extract that when a member asks a question, a response is not received within 60% of cases, or that when member A receives a question or request, the response is different from the question or request in 50% of cases. Next, the AI ​​agent system outputs a report showing that in the task of approving a plan for a particular project, there have been a total of three schedule reschedulings over a six-month period, resulting in a total delay of two months. This report specifically describes the causes and issues of the delays. Furthermore, the AI ​​agent system can identify abnormal conditions in a particular task or project. For example, the system can detect that Person A has missed an invitation to a mailing list that is mandatory for work, or that while 80% of colleagues respond to a request from Person B within one day, only 40% respond to a request from Person C within one day, and 50% of requests are left unanswered. In this way, the AI ​​agent system can improve communication among members and increase work efficiency by regularly presenting issues and abnormal conditions in specific tasks or projects.

[0029] The AI ​​agent system according to this embodiment comprises a collection unit, an analysis unit, and an output unit. The collection unit collects data related to specific tasks or projects. For example, the collection unit collects data such as chat content from communication tools, internal wikis, project documents, and member names. For example, the collection unit monitors chat content in real time and extracts important messages. The collection unit can also periodically check the update history of the internal wiki and collect project-related information. Furthermore, the collection unit can automatically scan project documents and save them as digital data. For example, the collection unit scans project plans and progress reports and saves them as digital data. The analysis unit analyzes the data collected by the collection unit to find issues and abnormal conditions. For example, the analysis unit analyzes the collected data using natural language processing technology to find the percentage of questions asked by specific members that have not received a response, and the accuracy of each member's response. For example, the analysis unit calculates the rate of unanswered questions and evaluates the accuracy of responses from specific members. The analysis unit can also find the causes of schedule rescheduling or delays in project plan approval. For example, the analysis unit analyzes the schedule change history and identifies the cause of the delay. The output unit outputs the issues and abnormal conditions found by the analysis unit as a report. The output unit outputs the analysis results as, for example, a text report, graph, or chart. The output unit visually displays, for example, the non-response rate or response accuracy as a graph. The output unit can also output a report that specifically describes the cause of the delay and the issues. For example, the output unit generates a report that explains the cause of the delay and the issues in detail. As a result, the AI ​​agent system according to the embodiment can periodically present issues and abnormal conditions in specific tasks or projects.

[0030] The data collection department collects data related to specific tasks and projects. Specifically, it collects data such as chat content from communication tools, internal wikis, project documents, and member names. For example, the data collection department monitors chat content in real time and extracts important messages. This allows for an understanding of project progress and the quality of communication among members. The data collection department also regularly checks the update history of the internal wiki and collects project-related information. This ensures that the latest information on projects is always available. Furthermore, the data collection department can automatically scan project documents and save them as digital data. For example, by scanning project plans and progress reports and saving them as digital data, paper documents can be digitized and managed efficiently. The data collection department centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and output departments. By adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the data collection department to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis department analyzes data collected by the data collection department to identify issues and anomalies. Specifically, the analysis department uses natural language processing technology to analyze the collected data and find the percentage of questions asked by specific members that have not received a response, as well as the accuracy of each member's response. For example, the analysis department calculates the rate of unanswered questions and evaluates the accuracy of responses from specific members. The analysis department can also find the causes of schedule rescheduling and delays in project plan approval. For example, the analysis department analyzes the schedule change history to identify the cause of delays. Furthermore, the analysis department uses AI to process data in real time and evaluate project progress and member performance. The AI ​​can, for example, analyze chat content to evaluate the quality of communication between members and identify where problems are occurring. It can also analyze project documents to understand progress and issues. This allows the analysis department to quickly and accurately analyze collected data and understand project issues and anomalies in real time. In addition, the analysis department can utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, based on past project data, it can predict fluctuations in risks in specific tasks or projects and formulate future countermeasures. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling it to issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The output unit outputs issues and abnormal conditions identified by the analysis unit as a report. Specifically, the output unit outputs the analysis results as text reports, graphs, and charts. For example, the output unit visually displays the non-response rate and response accuracy as graphs. This allows for a quick overview of project progress and member performance. The output unit can also output reports that specifically describe the causes and issues of delays. For example, the output unit generates a report that explains the causes and issues of delays in detail. This allows for a detailed understanding of project progress and issues, enabling appropriate countermeasures to be taken. Furthermore, the output unit can update analysis results in real time, providing the latest information. For example, if project progress or member performance changes, the output unit immediately incorporates the new data and updates the report. The output unit can also collect user feedback and continuously improve the accuracy and effectiveness of the report content. For example, the report content can be reviewed and improved based on feedback from users who receive the report. This makes the output unit a useful tool for providing users with quick and accurate information and understanding project progress and issues. Furthermore, the output unit can output reports in multiple formats. For example, reports can be output not only in text format but also in PDF and Excel formats, allowing for flexible responses to user needs. This makes the output unit a useful tool for providing users with information in diverse formats and understanding project progress and challenges.

[0033] The data collection unit can collect data such as chat content from communication tools, internal wikis, project documents, and member names. For example, the data collection unit can monitor chat content in real time and extract important messages. For example, the data collection unit can periodically check the update history of the internal wiki and collect project-related information. For example, the data collection unit can automatically scan project documents and save them as digital data. For example, the data collection unit can scan project plans and progress reports and save them as digital data. This allows for the collection of a wide range of data related to specific tasks or projects. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input chat content into AI and have the AI ​​extract important messages.

[0034] The analysis unit can analyze the collected data to find the percentage of questions posed by specific members that have not received a response, and the accuracy of each member's response. For example, the analysis unit can analyze the collected data using natural language processing techniques to calculate the rate of unanswered questions. For example, the analysis unit can evaluate the accuracy of a specific member's response. For example, the analysis unit can compare the content of the question with the content of the response to evaluate the accuracy of the response. This allows for the analysis of the accuracy of responses and the response rate for each member. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into an AI and have the AI ​​calculate the rate of unanswered questions.

[0035] The output unit can output the analysis results as a concrete report. For example, the output unit can output the analysis results as a text report, graph, or chart. For example, the output unit can visually display the non-response rate or response accuracy as a graph. For example, the output unit can also output a report that specifically describes the causes and issues of delays. For example, the output unit can generate a report that explains the causes and issues of delays in detail. By outputting the analysis results as a concrete report, issues and abnormal conditions can be clearly presented. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input the analysis results into AI and have the AI ​​generate the report.

[0036] The analysis unit can identify the causes of schedule rescheduling or delays in the planning approval process for a specific project. For example, the analysis unit can analyze the schedule change history to identify the cause of the delay. For example, the analysis unit can identify causes such as task reassignment or resource reallocation. This allows the analysis unit to identify the causes of schedule rescheduling or delays in the planning approval process for a project. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input the schedule change history into the AI ​​and have the AI ​​identify the cause of the delay.

[0037] The output unit can output a report that specifically details the causes and issues of the delay. For example, the output unit generates a report that explains the causes and issues of the delay in detail. The output unit can also visually display the causes and issues of the delay as graphs or charts. This makes it possible to clarify the problems by outputting a report that specifically details the causes and issues of the delay. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input the causes and issues of the delay into AI and have the AI ​​generate the report.

[0038] The data collection unit can analyze past data collection history and select the optimal collection method. For example, the data collection unit can identify the most efficient collection time from past data collection history. For example, the data collection unit can determine the priority of data to be collected based on past data collection history. For example, the data collection unit can analyze past data collection history and find areas for improvement in the collection method. In this way, the optimal collection method can be selected by analyzing past data collection history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past data collection history into AI and have the AI ​​select the optimal collection method.

[0039] The data collection unit can filter data based on the progress of specific projects or tasks during data collection. For example, the data collection unit can collect only important data according to the progress of a project. For example, the data collection unit can change the type of data collected based on the progress of a task. For example, the data collection unit can monitor project progress in real time and filter the collected data as appropriate. This allows for the priority collection of important data by filtering data based on the progress of specific projects or tasks. 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 project progress data into AI and have the AI ​​perform data filtering.

[0040] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. For example, the data collection unit will collect data related to nearby projects based on the user's location information. For example, the data collection unit will collect highly relevant data by considering the user's travel history. By prioritizing the collection of highly relevant data while considering the user's geographical location information, more useful data can be collected. 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 information into AI and have the AI ​​perform the collection of highly relevant data.

[0041] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. For example, the data collection unit can analyze the content of the user's social media posts and collect relevant data. For example, the data collection unit can determine the type of data to collect based on the user's interests on social media. For example, the data collection unit can adjust the timing of data collection considering the frequency of the user's activity on social media. This allows the collection of relevant data 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 data into AI and have AI perform the collection of relevant data.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on data with high importance. For example, the analysis unit performs a simplified analysis on data with low importance. For example, the analysis unit determines the priority of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into the AI ​​and have the AI ​​adjust the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies a natural language processing algorithm to text data. For example, the analysis unit applies a statistical analysis algorithm to numerical data. For example, the analysis unit applies an image recognition algorithm to image data. By applying different analysis algorithms depending on the data category, appropriate analysis can be performed. 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 the data category into the AI ​​and have the AI ​​select the analysis algorithm to apply.

[0044] The analysis unit can determine the priority of analysis based on the data submission date during the analysis process. For example, the analysis unit may prioritize the analysis of recently submitted data. For example, the analysis unit may postpone the analysis of older data. For example, the analysis unit may adjust the analysis schedule based on the submission date. This allows for efficient analysis by determining the priority of analysis based on the data submission date. 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 data submission date into the AI ​​and have the AI ​​determine the analysis priority.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. For example, the analysis unit may postpone the analysis of less relevant data. For example, the analysis unit may determine the order of analysis based on the relevance of the data. In this way, by adjusting the order of analysis based on the relevance of the data, highly relevant data can be prioritized for analysis. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the relevance of the data into the AI ​​and have the AI ​​perform the adjustment of the order of analysis.

[0046] The output unit can adjust the level of detail in the report based on the importance of the analysis results when outputting the report. For example, the output unit provides a detailed report for analysis results of high importance. For example, the output unit provides a simplified report for analysis results of low importance. For example, the output unit determines the priority of the report according to the importance of the analysis results. This allows for the provision of efficient reports by adjusting the level of detail in the report based on the importance of the analysis results. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input the importance of the analysis results into the AI ​​and have the AI ​​perform the adjustment of the level of detail in the report.

[0047] The output unit can apply different report formats depending on the category of the analysis results when outputting reports. For example, the output unit provides a text-based report for the analysis results of text data. For example, the output unit provides a graph or tabular report for the analysis results of numerical data. For example, the output unit provides a report including images for the analysis results of image data. By applying different report formats depending on the category of the analysis results, an appropriate report can be provided. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input the category of the analysis results into the AI ​​and have the AI ​​select the report format to apply.

[0048] The output unit can determine the priority of reports based on the submission date of the analysis results when generating reports. For example, the output unit prioritizes reporting recent analysis results. For example, the output unit postpones reporting older analysis results. For example, the output unit adjusts the report schedule based on the submission date. This allows for the provision of efficient reports by determining the priority of reports based on the submission date of the analysis results. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input the submission date of the analysis results into the AI ​​and have the AI ​​perform the determination of report priority.

[0049] The output unit can adjust the order of reports based on the relevance of the analysis results when outputting reports. For example, the output unit prioritizes reporting highly relevant analysis results. For example, the output unit postpones reporting less relevant analysis results. For example, the output unit determines the order of reports based on the relevance of the analysis results. This allows for the priority provision of highly relevant reports by adjusting the order of reports based on the relevance of the analysis results. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input the relevance of the analysis results into the AI ​​and have the AI ​​perform the adjustment of the report order.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location. For example, if the user is in a specific region, it can prioritize the collection of data related to that region. Based on the user's location, it can collect data related to nearby projects. It can also collect highly relevant data by considering the user's travel history. As a result, by prioritizing the collection of highly relevant data while considering the user's geographical location, more useful data can be collected. 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 into AI and have AI perform the collection of highly relevant data.

[0052] The analysis unit can apply different analysis algorithms depending on the data category. For example, a natural language processing algorithm can be applied to text data. A statistical analysis algorithm can be applied to numerical data. An image recognition algorithm can be applied to image data. This allows for appropriate analysis by applying different analysis algorithms depending on the data 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 the data category into the AI ​​and have the AI ​​select the analysis algorithm to apply.

[0053] The analysis unit can determine the priority of analysis based on the data submission date. For example, it can prioritize the analysis of recently submitted data. Older data can be analyzed later. The analysis schedule can be adjusted based on the submission date. This allows for efficient analysis by determining the priority of analysis based on the data submission date. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input the data submission date into the AI ​​and have the AI ​​determine the analysis priority.

[0054] The output unit can adjust the level of detail in the report based on the importance of the analysis results. For example, a detailed report can be provided for analysis results of high importance, while a simplified report can be provided for analysis results of low importance. The report priority can be determined according to the importance of the analysis results. This allows for the provision of efficient reports by adjusting the level of detail based on the importance of the analysis results. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input the importance of the analysis results into the AI ​​and have the AI ​​perform the adjustment of the level of detail in the report.

[0055] The data collection unit can analyze a user's social media activity and collect relevant data. For example, it can analyze a user's social media posts and collect relevant data. It can determine the type of data to collect based on the user's interests on social media. It can adjust the timing of data collection considering the frequency of the user's social media activity. This allows for the collection of relevant data by analyzing the user's social media activity. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input the user's social media data into an AI and have the AI ​​perform the collection of relevant data.

[0056] The output unit can apply different report formats depending on the category of the analysis results when outputting reports. For example, it can provide a text-based report for the analysis results of text data. For the analysis results of numerical data, it can provide a report in graph or tabular format. For the analysis results of image data, it can provide a report that includes images. In this way, by applying different report formats depending on the category of the analysis results, an appropriate report can be provided. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input the category of the analysis results into the AI ​​and have the AI ​​select the report format to apply.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The data collection unit collects data related to specific tasks or projects. For example, the collection unit collects data such as chat content from communication tools, internal wikis, project documents, and member names. The collection unit monitors chat content in real time and extracts important messages. It can also periodically check the update history of the internal wiki and collect project-related information. Furthermore, it can automatically scan project documents and save them as digital data. For example, it can scan project plans and progress reports and save them as digital data. Step 2: The analysis unit analyzes the data collected by the collection unit to identify issues and abnormal conditions. For example, the analysis unit uses natural language processing techniques to analyze the collected data and find the percentage of questions asked by specific members that have not received a response, as well as the accuracy of each member's response. The analysis unit calculates the rate of unanswered questions and evaluates the accuracy of responses from specific members. It can also find the causes of schedule rescheduling or delays in project plan approval. For example, it can analyze the schedule change history to identify the cause of delays. Step 3: The output unit outputs the issues and abnormal conditions found by the analysis unit as a report. For example, the output unit outputs the analysis results as a text report, graph, or chart. The output unit visually displays the non-response rate and response accuracy as a graph. It can also output a report that specifically describes the causes and issues of delays. For example, it can generate a report that explains the causes and issues of delays in detail.

[0059] (Example of form 2) An AI agent system according to an embodiment of the present invention is a system that periodically presents abstract and specific issues in a particular task or project, regardless of whether they are consciously recognized by the members. This AI agent system monitors chat content in channels of communication tools used in a particular task or project, internal wikis, project documents, member names, etc., as input sources, and identifies and presents issues over periods such as the past few years or the past week. For example, the AI ​​agent system monitors chat content in channels of communication tools related to a particular task or project, internal wikis, project documents, member names, etc. For example, it may extract that when a member asks a question, a response is not received within 60% of cases, or that when member A receives a question or request, the response is different from the question or request in 50% of cases. Next, the AI ​​agent system outputs a report showing that in the task of approving a plan for a particular project, there have been a total of three schedule reschedulings over a six-month period, resulting in a total delay of two months. This report specifically describes the causes and issues of the delays. Furthermore, the AI ​​agent system can identify abnormal conditions in a particular task or project. For example, the system can detect that Person A has missed an invitation to a mailing list that is mandatory for work, or that while 80% of colleagues respond to a request from Person B within one day, only 40% respond to a request from Person C within one day, and 50% of requests are left unanswered. In this way, the AI ​​agent system can improve communication among members and increase work efficiency by regularly presenting issues and abnormal conditions in specific tasks or projects.

[0060] The AI ​​agent system according to this embodiment comprises a collection unit, an analysis unit, and an output unit. The collection unit collects data related to specific tasks or projects. For example, the collection unit collects data such as chat content from communication tools, internal wikis, project documents, and member names. For example, the collection unit monitors chat content in real time and extracts important messages. The collection unit can also periodically check the update history of the internal wiki and collect project-related information. Furthermore, the collection unit can automatically scan project documents and save them as digital data. For example, the collection unit scans project plans and progress reports and saves them as digital data. The analysis unit analyzes the data collected by the collection unit to find issues and abnormal conditions. For example, the analysis unit analyzes the collected data using natural language processing technology to find the percentage of questions asked by specific members that have not received a response, and the accuracy of each member's response. For example, the analysis unit calculates the rate of unanswered questions and evaluates the accuracy of responses from specific members. The analysis unit can also find the causes of schedule rescheduling or delays in project plan approval. For example, the analysis unit analyzes the schedule change history and identifies the cause of the delay. The output unit outputs the issues and abnormal conditions found by the analysis unit as a report. The output unit outputs the analysis results as, for example, a text report, graph, or chart. The output unit visually displays, for example, the non-response rate or response accuracy as a graph. The output unit can also output a report that specifically describes the cause of the delay and the issues. For example, the output unit generates a report that explains the cause of the delay and the issues in detail. As a result, the AI ​​agent system according to the embodiment can periodically present issues and abnormal conditions in specific tasks or projects.

[0061] The data collection department collects data related to specific tasks and projects. Specifically, it collects data such as chat content from communication tools, internal wikis, project documents, and member names. For example, the data collection department monitors chat content in real time and extracts important messages. This allows for an understanding of project progress and the quality of communication among members. The data collection department also regularly checks the update history of the internal wiki and collects project-related information. This ensures that the latest information on projects is always available. Furthermore, the data collection department can automatically scan project documents and save them as digital data. For example, by scanning project plans and progress reports and saving them as digital data, paper documents can be digitized and managed efficiently. The data collection department centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and output departments. By adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the data collection department to collect data efficiently and effectively, improving the overall system performance.

[0062] The analysis department analyzes data collected by the data collection department to identify issues and anomalies. Specifically, the analysis department uses natural language processing technology to analyze the collected data and find the percentage of questions asked by specific members that have not received a response, as well as the accuracy of each member's response. For example, the analysis department calculates the rate of unanswered questions and evaluates the accuracy of responses from specific members. The analysis department can also find the causes of schedule rescheduling and delays in project plan approval. For example, the analysis department analyzes the schedule change history to identify the cause of delays. Furthermore, the analysis department uses AI to process data in real time and evaluate project progress and member performance. The AI ​​can, for example, analyze chat content to evaluate the quality of communication between members and identify where problems are occurring. It can also analyze project documents to understand progress and issues. This allows the analysis department to quickly and accurately analyze collected data and understand project issues and anomalies in real time. In addition, the analysis department can utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, based on past project data, it can predict fluctuations in risks in specific tasks or projects and formulate future countermeasures. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling it to issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0063] The output unit outputs issues and abnormal conditions identified by the analysis unit as a report. Specifically, the output unit outputs the analysis results as text reports, graphs, and charts. For example, the output unit visually displays the non-response rate and response accuracy as graphs. This allows for a quick overview of project progress and member performance. The output unit can also output reports that specifically describe the causes and issues of delays. For example, the output unit generates a report that explains the causes and issues of delays in detail. This allows for a detailed understanding of project progress and issues, enabling appropriate countermeasures to be taken. Furthermore, the output unit can update analysis results in real time, providing the latest information. For example, if project progress or member performance changes, the output unit immediately incorporates the new data and updates the report. The output unit can also collect user feedback and continuously improve the accuracy and effectiveness of the report content. For example, the report content can be reviewed and improved based on feedback from users who receive the report. This makes the output unit a useful tool for providing users with quick and accurate information and understanding project progress and issues. Furthermore, the output unit can output reports in multiple formats. For example, reports can be output not only in text format but also in PDF and Excel formats, allowing for flexible responses to user needs. This makes the output unit a useful tool for providing users with information in diverse formats and understanding project progress and challenges.

[0064] The data collection unit can collect data such as chat content from communication tools, internal wikis, project documents, and member names. For example, the data collection unit can monitor chat content in real time and extract important messages. For example, the data collection unit can periodically check the update history of the internal wiki and collect project-related information. For example, the data collection unit can automatically scan project documents and save them as digital data. For example, the data collection unit can scan project plans and progress reports and save them as digital data. This allows for the collection of a wide range of data related to specific tasks or projects. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input chat content into AI and have the AI ​​extract important messages.

[0065] The analysis unit can analyze the collected data to find the percentage of questions posed by specific members that have not received a response, and the accuracy of each member's response. For example, the analysis unit can analyze the collected data using natural language processing techniques to calculate the rate of unanswered questions. For example, the analysis unit can evaluate the accuracy of a specific member's response. For example, the analysis unit can compare the content of the question with the content of the response to evaluate the accuracy of the response. This allows for the analysis of the accuracy of responses and the response rate for each member. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into an AI and have the AI ​​calculate the rate of unanswered questions.

[0066] The output unit can output the analysis results as a concrete report. For example, the output unit can output the analysis results as a text report, graph, or chart. For example, the output unit can visually display the non-response rate or response accuracy as a graph. For example, the output unit can also output a report that specifically describes the causes and issues of delays. For example, the output unit can generate a report that explains the causes and issues of delays in detail. By outputting the analysis results as a concrete report, issues and abnormal conditions can be clearly presented. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input the analysis results into AI and have the AI ​​generate the report.

[0067] The analysis unit can identify the causes of schedule rescheduling or delays in the planning approval process for a specific project. For example, the analysis unit can analyze the schedule change history to identify the cause of the delay. For example, the analysis unit can identify causes such as task reassignment or resource reallocation. This allows the analysis unit to identify the causes of schedule rescheduling or delays in the planning approval process for a project. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input the schedule change history into the AI ​​and have the AI ​​identify the cause of the delay.

[0068] The output unit can output a report that specifically details the causes and issues of the delay. For example, the output unit generates a report that explains the causes and issues of the delay in detail. The output unit can also visually display the causes and issues of the delay as graphs or charts. This makes it possible to clarify the problems by outputting a report that specifically details the causes and issues of the delay. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input the causes and issues of the delay into AI and have the AI ​​generate the report.

[0069] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to alleviate the burden. For example, if the user is relaxed, the data collection unit can increase the frequency of data collection to collect more detailed information. For example, if the user is busy, the data collection unit can prioritize collecting only important data. This reduces the burden on the user by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI 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 without AI. For example, the data collection unit can input user emotion data into AI and have the AI ​​adjust the timing of data collection.

[0070] The data collection unit can analyze past data collection history and select the optimal collection method. For example, the data collection unit can identify the most efficient collection time from past data collection history. For example, the data collection unit can determine the priority of data to be collected based on past data collection history. For example, the data collection unit can analyze past data collection history and find areas for improvement in the collection method. In this way, the optimal collection method can be selected by analyzing past data collection history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past data collection history into AI and have the AI ​​select the optimal collection method.

[0071] The data collection unit can filter data based on the progress of specific projects or tasks during data collection. For example, the data collection unit can collect only important data according to the progress of a project. For example, the data collection unit can change the type of data collected based on the progress of a task. For example, the data collection unit can monitor project progress in real time and filter the collected data as appropriate. This allows for the priority collection of important data by filtering data based on the progress of specific projects or tasks. 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 project progress data into AI and have the AI ​​perform data filtering.

[0072] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will postpone the collection of less important data. For example, if the user is relaxed, the data collection unit will prioritize the collection of detailed data. For example, if the user is busy, the data collection unit will prioritize the collection of only important data. In this way, by determining the priority of data to collect according to the user's emotions, important data can be collected preferentially. 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 an AI and have the AI ​​perform the determination of data priority.

[0073] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. For example, the data collection unit will collect data related to nearby projects based on the user's location information. For example, the data collection unit will collect highly relevant data by considering the user's travel history. By prioritizing the collection of highly relevant data while considering the user's geographical location information, more useful data can be collected. 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 information into AI and have the AI ​​perform the collection of highly relevant data.

[0074] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. For example, the data collection unit can analyze the content of the user's social media posts and collect relevant data. For example, the data collection unit can determine the type of data to collect based on the user's interests on social media. For example, the data collection unit can adjust the timing of data collection considering the frequency of the user's activity on social media. This allows the collection of relevant data 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 data into AI and have AI perform the collection of relevant data.

[0075] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit provides a simple analysis result. For example, if the user is relaxed, the analysis unit provides a detailed analysis result. For example, if the user is busy, the analysis unit provides a concise analysis result. By adjusting the presentation of the analysis according to the user's emotions, the analysis unit can provide analysis results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into an AI and have the AI ​​adjust the presentation of the analysis.

[0076] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on data with high importance. For example, the analysis unit performs a simplified analysis on data with low importance. For example, the analysis unit determines the priority of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into the AI ​​and have the AI ​​adjust the level of detail of the analysis.

[0077] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies a natural language processing algorithm to text data. For example, the analysis unit applies a statistical analysis algorithm to numerical data. For example, the analysis unit applies an image recognition algorithm to image data. By applying different analysis algorithms depending on the data category, appropriate analysis can be performed. 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 the data category into the AI ​​and have the AI ​​select the analysis algorithm to apply.

[0078] The analysis unit can estimate the user's emotions and determine the priority of analyses based on the estimated emotions. For example, if the user is stressed, the analysis unit will prioritize high-priority analyses. If the user is relaxed, the analysis unit will prioritize detailed analyses. If the user is busy, the analysis unit will prioritize concise analyses. This allows important analyses to be prioritized by determining the priority of analyses according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI 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 an AI and have the AI ​​determine the priority of analyses.

[0079] The analysis unit can determine the priority of analysis based on the data submission date during the analysis process. For example, the analysis unit may prioritize the analysis of recently submitted data. For example, the analysis unit may postpone the analysis of older data. For example, the analysis unit may adjust the analysis schedule based on the submission date. This allows for efficient analysis by determining the priority of analysis based on the data submission date. 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 data submission date into the AI ​​and have the AI ​​determine the analysis priority.

[0080] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. For example, the analysis unit may postpone the analysis of less relevant data. For example, the analysis unit may determine the order of analysis based on the relevance of the data. In this way, by adjusting the order of analysis based on the relevance of the data, highly relevant data can be prioritized for analysis. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the relevance of the data into the AI ​​and have the AI ​​perform the adjustment of the order of analysis.

[0081] The output unit can estimate the user's emotions and adjust the report's presentation based on the estimated emotions. For example, if the user is stressed, the output unit provides a simple report. If the user is relaxed, the output unit provides a detailed report. If the user is busy, the output unit provides a concise report. By adjusting the report's presentation according to the user's emotions, it is possible to provide a report that is easy for the user to understand. 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 output unit may be performed using AI or not. For example, the output unit can input user emotion data into an AI and have the AI ​​adjust the report's presentation.

[0082] The output unit can adjust the level of detail in the report based on the importance of the analysis results when outputting the report. For example, the output unit provides a detailed report for analysis results of high importance. For example, the output unit provides a simplified report for analysis results of low importance. For example, the output unit determines the priority of the report according to the importance of the analysis results. This allows for the provision of efficient reports by adjusting the level of detail in the report based on the importance of the analysis results. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input the importance of the analysis results into the AI ​​and have the AI ​​perform the adjustment of the level of detail in the report.

[0083] The output unit can apply different report formats depending on the category of the analysis results when outputting reports. For example, the output unit provides a text-based report for the analysis results of text data. For example, the output unit provides a graph or tabular report for the analysis results of numerical data. For example, the output unit provides a report including images for the analysis results of image data. By applying different report formats depending on the category of the analysis results, an appropriate report can be provided. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input the category of the analysis results into the AI ​​and have the AI ​​select the report format to apply.

[0084] The output unit can estimate the user's emotions and prioritize reports based on the estimated emotions. For example, if the user is stressed, the output unit will prioritize high-priority reports. If the user is relaxed, the output unit will prioritize detailed reports. If the user is busy, the output unit will prioritize concise reports. This allows important reports to be delivered preferentially by prioritizing reports according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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 output unit may be performed using AI or not. For example, the output unit can input user emotion data into an AI and have the AI ​​determine the report priorities.

[0085] The output unit can determine the priority of reports based on the submission date of the analysis results when generating reports. For example, the output unit prioritizes reporting recent analysis results. For example, the output unit postpones reporting older analysis results. For example, the output unit adjusts the report schedule based on the submission date. This allows for the provision of efficient reports by determining the priority of reports based on the submission date of the analysis results. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input the submission date of the analysis results into the AI ​​and have the AI ​​perform the determination of report priority.

[0086] The output unit can adjust the order of reports based on the relevance of the analysis results when outputting reports. For example, the output unit prioritizes reporting highly relevant analysis results. For example, the output unit postpones reporting less relevant analysis results. For example, the output unit determines the order of reports based on the relevance of the analysis results. This allows for the priority provision of highly relevant reports by adjusting the order of reports based on the relevance of the analysis results. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input the relevance of the analysis results into the AI ​​and have the AI ​​perform the adjustment of the report order.

[0087] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0088] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, if the user is stressed, high-priority analysis can be prioritized. If the user is relaxed, detailed analysis can be prioritized. If the user is busy, concise analysis can be prioritized. In this way, important analysis can be prioritized by determining the priority of analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI 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 AI and have the AI ​​determine the priority of analysis.

[0089] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location. For example, if the user is in a specific region, it can prioritize the collection of data related to that region. Based on the user's location, it can collect data related to nearby projects. It can also collect highly relevant data by considering the user's travel history. As a result, by prioritizing the collection of highly relevant data while considering the user's geographical location, more useful data can be collected. 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 into AI and have AI perform the collection of highly relevant data.

[0090] The output unit can estimate the user's emotions and adjust the report's presentation based on the estimated emotions. For example, if the user is stressed, a simple report can be provided. If the user is relaxed, a detailed report can be provided. If the user is busy, a concise report can be provided. By adjusting the report's presentation according to the user's emotions, a report that is easy for the user to understand can be provided. 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 output unit may be performed using AI, or not using AI. For example, the output unit can input user emotion data into AI and have the AI ​​adjust the report's presentation.

[0091] The analysis unit can apply different analysis algorithms depending on the data category. For example, a natural language processing algorithm can be applied to text data. A statistical analysis algorithm can be applied to numerical data. An image recognition algorithm can be applied to image data. This allows for appropriate analysis by applying different analysis algorithms depending on the data 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 the data category into the AI ​​and have the AI ​​select the analysis algorithm to apply.

[0092] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the collection of less important data can be postponed. If the user is relaxed, detailed data can be prioritized for collection. If the user is busy, only important data can be prioritized for collection. In this way, by prioritizing the data to be collected according to the user's emotions, important data can be collected preferentially. 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 an AI and have the AI ​​perform the determination of data prioritization.

[0093] The analysis unit can determine the priority of analysis based on the data submission date. For example, it can prioritize the analysis of recently submitted data. Older data can be analyzed later. The analysis schedule can be adjusted based on the submission date. This allows for efficient analysis by determining the priority of analysis based on the data submission date. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input the data submission date into the AI ​​and have the AI ​​determine the analysis priority.

[0094] The output unit can adjust the level of detail in the report based on the importance of the analysis results. For example, a detailed report can be provided for analysis results of high importance, while a simplified report can be provided for analysis results of low importance. The report priority can be determined according to the importance of the analysis results. This allows for the provision of efficient reports by adjusting the level of detail based on the importance of the analysis results. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input the importance of the analysis results into the AI ​​and have the AI ​​perform the adjustment of the level of detail in the report.

[0095] The data collection unit can analyze a user's social media activity and collect relevant data. For example, it can analyze a user's social media posts and collect relevant data. It can determine the type of data to collect based on the user's interests on social media. It can adjust the timing of data collection considering the frequency of the user's social media activity. This allows for the collection of relevant data by analyzing the user's social media activity. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input the user's social media data into an AI and have the AI ​​perform the collection of relevant data.

[0096] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is stressed, a simple analysis result can be provided. If the user is relaxed, a detailed analysis result can be provided. If the user is busy, a concise analysis result can be provided. In this way, by adjusting the presentation of the analysis according to the user's emotions, an analysis result that is easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into AI and have the AI ​​adjust the presentation of the analysis.

[0097] The output unit can apply different report formats depending on the category of the analysis results when outputting reports. For example, it can provide a text-based report for the analysis results of text data. For the analysis results of numerical data, it can provide a report in graph or tabular format. For the analysis results of image data, it can provide a report that includes images. In this way, by applying different report formats depending on the category of the analysis results, an appropriate report can be provided. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input the category of the analysis results into the AI ​​and have the AI ​​select the report format to apply.

[0098] The following briefly describes the processing flow for example form 2.

[0099] Step 1: The data collection unit collects data related to specific tasks or projects. For example, the collection unit collects data such as chat content from communication tools, internal wikis, project documents, and member names. The collection unit monitors chat content in real time and extracts important messages. It can also periodically check the update history of the internal wiki and collect project-related information. Furthermore, it can automatically scan project documents and save them as digital data. For example, it can scan project plans and progress reports and save them as digital data. Step 2: The analysis unit analyzes the data collected by the collection unit to identify issues and abnormal conditions. For example, the analysis unit uses natural language processing techniques to analyze the collected data and find the percentage of questions asked by specific members that have not received a response, as well as the accuracy of each member's response. The analysis unit calculates the rate of unanswered questions and evaluates the accuracy of responses from specific members. It can also find the causes of schedule rescheduling or delays in project plan approval. For example, it can analyze the schedule change history to identify the cause of delays. Step 3: The output unit outputs the issues and abnormal conditions found by the analysis unit as a report. For example, the output unit outputs the analysis results as a text report, graph, or chart. The output unit visually displays the non-response rate and response accuracy as a graph. It can also output a report that specifically describes the causes and issues of delays. For example, it can generate a report that explains the causes and issues of delays in detail.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] Each of the multiple elements described above, including the data collection unit, analysis unit, and output unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the smart device 14 and monitors chat content from a communication tool and update history of the company wiki. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data using natural language processing technology to identify problems and abnormal conditions. The output unit is implemented by the control unit 46A of the smart device 14 and outputs the analysis results as a text report or graph. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0104] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] Each of the multiple elements described above, including the data collection unit, analysis unit, and output unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the smart glasses 214 and monitors chat content from a communication tool and update history of the company wiki. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data using natural language processing technology to identify problems and abnormal conditions. The output unit is implemented by the control unit 46A of the smart glasses 214 and outputs the analysis results as a text report or graph. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0120] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] Each of the multiple elements described above, including the data collection unit, analysis unit, and output unit, is implemented in at least one of the following: the headset terminal 314 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the headset terminal 314 and monitors chat content from communication tools and update history of the company wiki. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data using natural language processing technology to identify problems and abnormal conditions. The output unit is implemented by the control unit 46A of the headset terminal 314 and outputs the analysis results as a text report or graph. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0136] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] Each of the multiple elements described above, including the data collection unit, analysis unit, and output unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the robot 414 and monitors the chat content of a communication tool and the update history of the company's wiki. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data using natural language processing technology to identify problems and abnormal conditions. The output unit is implemented by the control unit 46A of the robot 414 and outputs the analysis results as a text report or graph. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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."

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] (Note 1) A data collection unit that collects data related to specific tasks or projects, An analysis unit analyzes the data collected by the aforementioned collection unit to identify problems and abnormal conditions, The system includes an output unit that outputs a report of the issues and abnormal conditions found by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect data such as chat content from communication tools, internal wikis, project documents, and member names. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected data is analyzed to determine the percentage of questions asked by specific members that do not receive a response, and the accuracy of each member's response. The system described in Appendix 1, characterized by the features described herein. (Note 4) The output unit is, Output the analysis results as a detailed report. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, Identifying the causes of schedule rescheduling or delays in the approval process for a specific project. The system described in Appendix 1, characterized by the features described herein. (Note 6) The output unit is, Output a report that specifically details the causes and issues of the delay. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze past data collection history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting data, filter it based on the progress of a specific project or task. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) 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 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, The system estimates the user's emotions and determines the priority of analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of analyses is determined based on the timing of data submission. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The output unit is, It estimates user sentiment and adjusts the way reports are presented based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The output unit is, When generating a report, adjust the level of detail in the report based on the importance of the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 21) The output unit is, When generating reports, different report formats are applied depending on the category of the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 22) The output unit is, It estimates user sentiment and prioritizes reports based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The output unit is, When generating reports, the report priority is determined based on the submission date of the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 24) The output unit is, When generating reports, adjust the order of reports based on the relevance of the analysis results. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0172] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects data related to specific tasks or projects, An analysis unit analyzes the data collected by the aforementioned collection unit to identify problems and abnormal conditions, The system includes an output unit that outputs a report of the issues and abnormal conditions found by the analysis unit. A system characterized by the following features.

2. The aforementioned collection unit is We collect data such as chat content from communication tools, internal wikis, project documents, and member names. The system according to feature 1.

3. The aforementioned analysis unit, The collected data is analyzed to determine the percentage of questions asked by specific members that do not receive a response, and the accuracy of each member's response. The system according to feature 1.

4. The output unit is, Output the analysis results as a detailed report. The system according to feature 1.

5. The aforementioned analysis unit, Identifying the causes of schedule rescheduling or delays in the approval process for a specific project. The system according to feature 1.

6. The output unit is, Output a report that specifically details the causes and issues of the delay. The system according to feature 1.

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

8. The aforementioned collection unit is Analyze past data collection history and select the optimal collection method. The system according to feature 1.

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