system

Abstract

The system according to this embodiment aims to grasp the progress of employees in real time and provide appropriate advice. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a reporting unit, and an advice unit. The collection unit collects progress data. The analysis unit analyzes the progress data collected by the collection unit. The reporting unit grasps the results based on the analysis results obtained by the analysis unit and reports to the supervisor. The advice unit gathers information on areas where there are difficulties based on the analysis results obtained by the analysis unit and provides advice.

Description

Technical Field

[0004] ,

[0006] , , , , ,

[0005] , , , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to grasp the progress status of employees in real time and provide appropriate advice.

[0005] The system according to the embodiment aims to grasp the progress status of employees in real time and provide appropriate advice.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a reporting unit, and an advice unit. The data collection unit collects progress data. The analysis unit analyzes the progress data collected by the data collection unit. The reporting unit grasps the results based on the analysis results obtained by the analysis unit and reports to the supervisor. The advice unit gathers information on areas where there are difficulties based on the analysis results obtained by the analysis unit and provides advice. [Effects of the Invention]

[0007] The system according to this embodiment can monitor employees' progress in real time and provide appropriate advice. [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, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

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

[0028] (Example of form 1) The AI ​​agent system according to an embodiment of the present invention is a system that analyzes the progress of an employee's work in real time, grasps the results based on the acquired data, and provides input to the supervisor. This AI agent system monitors the employee's work in real time and accumulates and analyzes progress data. Next, it grasps the results based on the acquired data and provides input to the supervisor. Furthermore, if there is an area where the employee is stuck in their work, the AI ​​agent gathers information and automatically provides advice. This mechanism frees the user from creating progress reports and allows them to concentrate on more creative work. Also, if the employee gets stuck in their work, the AI ​​agent provides effective advice. First, the AI ​​agent monitors the employee's work in real time and accumulates and analyzes progress data. For example, the AI ​​agent automatically collects data and displays the progress in real time so that project managers and supervisors can grasp the progress of each person in charge of each task. This improves the transparency of progress and makes it easier to grasp results. Next, the AI ​​agent grasps the results based on the acquired data and provides input to the supervisor. For example, the AI ​​agent analyzes the progress of a project and provides an appropriate report to the supervisor. This makes it easier for the supervisor to grasp the current status of the project and give appropriate instructions. Furthermore, if an employee encounters difficulties in their work, the AI ​​agent will gather information and automatically provide advice. For example, if an employee faces difficulties with a particular task, the AI ​​agent will collect relevant information and suggest solutions. This allows employees to quickly resolve problems and move forward with their work. This system frees users from creating progress reports, allowing them to focus on more creative work. For instance, employees no longer need to spend time on progress reports, freeing up that time to generate new ideas or improve projects. Also, because the AI ​​agent provides effective advice when employees get stuck, they can work with confidence. This AI agent can be used across multiple sectors, particularly in IT, manufacturing, and service industries, in the project management software market and the business efficiency market, and is expected to have a large market share.Advances in generative AI technology and real-time data analysis enable AI agents to process and analyze data more quickly and accurately than ever before, making them a timely solution in today's business environment where immediate response and efficiency improvements are crucial. This allows AI agent systems to monitor employee progress in real time and automatically report and advise managers.

[0029] The AI ​​agent system according to this embodiment comprises a data collection unit, an analysis unit, a reporting unit, and an advice unit. The data collection unit monitors the employee's work in real time and collects progress data. The data collection unit monitors the employee's work in real time, for example, using sensors or log analysis technology. The data collection unit can detect the employee's work with sensors and collect progress data. The data collection unit can also analyze the employee's work using log analysis technology and collect progress data. The data collection unit monitors the employee's work in real time and stores progress data. The analysis unit analyzes the progress data collected by the data collection unit. The analysis unit analyzes the progress data using data mining technology, for example, and grasps the current status of the project. The analysis unit can analyze the progress data using statistical analysis technology, for example, and grasp the current status of the project. The analysis unit can also analyze the progress data using machine learning technology and grasp the current status of the project. The reporting unit grasps the results based on the analysis results obtained by the analysis unit and reports to the supervisor. The reporting unit makes an appropriate report to the supervisor based on the analysis results. The reporting department can, for example, provide appropriate reports to superiors based on the analysis results. The reporting department can also provide appropriate reports to superiors based on the analysis results. The advice department gathers information on areas where there are obstacles based on the analysis results obtained by the analysis department and provides advice. The advice department can, for example, gather relevant information on areas where there are obstacles and propose solutions. The advice department can, for example, gather relevant information on areas where there are obstacles and propose solutions. The advice department can also gather relevant information on areas where there are obstacles and propose solutions. As a result, the AI ​​agent system according to the embodiment can grasp the progress of employees in real time and automatically report to and advise superiors.

[0030] The data collection department monitors employees' work in real time and collects progress data. Specifically, it monitors employees' work in real time using sensors and log analysis technology. For example, software installed on employees' desktop computers detects keystrokes and mouse movements and records the progress of their work. Cameras and motion sensors installed in the office also monitor employees' movements and understand the progress of their work. This data is transmitted in real time to a central database and stored. Furthermore, the data collection department uses log analysis technology to analyze employees' work and collect progress data. For example, it analyzes logs from project management tools and version control systems used by employees to understand task completion status and code change history. This allows the data collection department to monitor employees' work in detail and accurately collect progress data. The collected data is updated in real time and used to understand the status of the entire system. Furthermore, the data collection department centrally manages this data and can link it with other systems and departments as needed. For example, the collected data is stored on a cloud server and made accessible to the analysis and reporting departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The Analysis Department analyzes progress data collected by the Data Collection Department. Specifically, it uses data mining techniques to analyze progress data and understand the current status of the project. For example, it uses data mining techniques to extract employee work patterns and progress trends to evaluate the project's progress. It can also use statistical analysis techniques to analyze progress data and understand the project's current status. For example, it uses statistical analysis techniques to analyze task completion rates and the distribution of work time to evaluate the project's progress. Furthermore, the Analysis Department can also use machine learning techniques to analyze progress data and understand the project's current status. For example, it uses machine learning techniques to predict project progress based on past data, which helps in the early detection of risks and the development of countermeasures. This allows the Analysis Department to quickly and accurately analyze collected data and understand the project's current status in real time. In addition, the Analysis Department can utilize past data and statistical information to conduct long-term risk assessments and trend analyses. For example, it can predict fluctuations in risks for specific tasks or periods based on past project data and develop future countermeasures. Furthermore, the Analysis Department can use anomaly detection algorithms to detect unusual patterns and abnormal data, and issue warnings early. This allows the analysis department 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 security of the entire system.

[0032] The reporting department grasps the results based on the analysis obtained by the analysis department and reports them to its superiors. Specifically, it provides appropriate reports to superiors based on the analysis results. For example, it reports the project's progress and risk assessment to superiors based on the analysis results and proposes appropriate countermeasures. Furthermore, the reporting department can provide appropriate reports to superiors based on the analysis results. For example, it reports the project's progress and risk assessment to superiors based on the analysis results and proposes appropriate countermeasures. In addition, the reporting department can provide appropriate reports to superiors based on the analysis results. For example, it reports the project's progress and risk assessment to superiors based on the analysis results and proposes appropriate countermeasures. This allows the reporting department to provide appropriate reports to superiors based on the analysis results, report the project's progress and risk assessment to superiors, and propose appropriate countermeasures. Furthermore, the reporting department can provide appropriate reports to superiors based on the analysis results. For example, it reports the project's progress and risk assessment to superiors based on the analysis results and proposes appropriate countermeasures. This allows the reporting department to provide appropriate reports to superiors based on the analysis results, report the project's progress and risk assessment to superiors, and propose appropriate countermeasures.

[0033] The advisory department gathers information on areas where problems have arisen based on the analysis results obtained by the analysis department and provides advice. Specifically, it gathers relevant information on areas where problems have arisen and proposes solutions. For example, it can gather relevant information on areas where problems have arisen and propose solutions. Furthermore, the advisory department can also gather relevant information on areas where problems have arisen and propose solutions. For example, it can gather relevant information on areas where problems have arisen and propose solutions. This allows the advisory department to gather relevant information on areas where problems have arisen and propose solutions. Furthermore, the advisory department can also gather relevant information on areas where problems have arisen and propose solutions. For example, it can gather relevant information on areas where problems have arisen and propose solutions. This allows the advisory department to gather relevant information on areas where problems have arisen and propose solutions.

[0034] The data collection unit can monitor employees' work in real time and accumulate progress data. For example, the data collection unit can detect employees' work using sensors and accumulate progress data. The data collection unit can also analyze employees' work using log analysis technology and accumulate progress data. For example, the data collection unit can monitor employees' work in real time and accumulate progress data. This makes it easier to understand the progress status by monitoring employees' work in real time and accumulating progress data. 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 detect employees' work using sensors, input progress data into AI, and the AI ​​can accumulate progress data.

[0035] The analysis department can analyze the accumulated progress data and understand the current status of the project. The analysis department can, for example, use data mining techniques to analyze the progress data and understand the current status of the project. The analysis department can also, for example, use statistical analysis techniques to analyze the progress data and understand the current status of the project. The analysis department can also, for example, use machine learning techniques to analyze the progress data and understand the current status of the project. In this way, the current status of the project can be understood by analyzing the accumulated progress data. Some or all of the above processing in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can input the accumulated progress data into AI, and the AI ​​can analyze the progress data and understand the current status of the project.

[0036] The reporting department can provide appropriate reports to superiors based on the analysis results. For example, the reporting department can provide appropriate reports to superiors based on the analysis results. The reporting department can provide appropriate reports to superiors based on the analysis results. The reporting department can also provide appropriate reports to superiors based on the analysis results. This makes it easier for superiors to understand the current status of the project by providing appropriate reports to superiors based on the analysis results. Some or all of the above processes in the reporting department may be performed using AI, for example, or not using AI. For example, the reporting department can input the analysis results into AI, and the AI ​​can provide appropriate reports to superiors.

[0037] The advisory department can collect relevant information regarding the areas where an issue is stuck and propose solutions. The advisory department can, for example, collect relevant information regarding the areas where an issue is stuck and propose solutions. The advisory department can, for example, collect relevant information regarding the areas where an issue is stuck and propose solutions. The advisory department can, for example, collect relevant information regarding the areas where an issue is stuck and propose solutions. This allows employees to quickly resolve problems by collecting relevant information regarding the areas where an issue is stuck and proposing solutions. Some or all of the above-described processes in the advisory department may be performed using AI, for example, or not using AI. For example, the advisory department can input relevant information regarding the areas where an issue is stuck into an AI, and the AI ​​can propose solutions.

[0038] The data collection unit can analyze the user's past work history and select the optimal data collection method. For example, the data collection unit can suggest the optimal data collection method based on the data collection methods the user has used in the past. For example, the data collection unit can select an efficient data collection method from the user's past work history. For example, the data collection unit can analyze the user's past work patterns and select the optimal data collection method. In this way, the optimal data collection method can be selected by analyzing the user's past work history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past work history data into AI, and the AI ​​can select the optimal data collection method.

[0039] The data collection unit can filter progress data based on the user's current projects and areas of interest. For example, the data collection unit can prioritize collecting data related to the project the user is currently working on. For example, the data collection unit can filter relevant data based on the user's areas of interest. For example, the data collection unit can collect necessary data based on the progress of the user's current projects. This allows for the priority collection of relevant data by filtering based on the user's current projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's project data into AI, which can then perform the filtering.

[0040] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting progress data. 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 can filter highly relevant data based on the user's current location. For example, the data collection unit can also collect the most relevant data by considering the user's geographical location. This enables efficient data collection by prioritizing the collection of highly relevant data by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into the AI, which can then prioritize the collection of highly relevant data.

[0041] The data collection unit can analyze the user's social media activity and collect relevant data when collecting progress data. For example, the data collection unit can extract relevant data from the user's social media activity. 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 also collect relevant data by referring to the activities of the user's social media followers and friends. This allows for the efficient 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 the AI ​​can collect relevant data.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the project. For example, the analysis unit can perform a detailed analysis for high-importance projects, and a concise analysis for low-importance projects. The analysis unit can also adjust the depth of the analysis according to the importance of the project. This allows for the provision of appropriate analysis results by adjusting the level of detail of the analysis based on the importance of the project. 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 project importance data into the AI, which can then adjust the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the project category during analysis. For example, the analysis unit may use a specific algorithm for IT projects. For example, it may use a different algorithm for manufacturing projects. For example, the analysis unit may select an appropriate algorithm for service industry projects. By applying different analysis algorithms depending on the project category, appropriate analysis results can be provided. 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 project category data into AI, and the AI ​​can apply an appropriate analysis algorithm.

[0044] The analysis department can prioritize analyses based on project submission deadlines. For example, it might prioritize projects with approaching deadlines, or postpone projects with later deadlines. The analysis department can also adjust the order of analyses based on submission deadlines. This allows analyses to be completed in time for submission deadlines by prioritizing them based on project submission dates. Some or all of the above processes in the analysis department may be performed using AI, or not. For example, the analysis department could input project submission date data into an AI, which could then determine the analysis priorities.

[0045] The analysis unit can adjust the order of analysis based on the relevance of projects during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant projects. For example, the analysis unit can postpone the analysis of less relevant projects. The analysis unit can also adjust the order of analysis based on the relevance of projects. This allows for prioritizing the analysis of highly relevant projects by adjusting the order of analysis based on project relevance. 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 project relevance data into AI, which can then adjust the order of analysis.

[0046] The reporting department can adjust the level of detail in reports based on the importance of the project. For example, the reporting department can provide detailed reports for high-priority projects, and concise reports for low-priority projects. The reporting department can also adjust the depth of the report according to the importance of the project. This allows for the provision of appropriate reports by adjusting the level of detail based on the importance of the project. Some or all of the above processes in the reporting department may be performed using AI, for example, or not. For example, the reporting department can input project importance data into the AI, which can then adjust the level of detail in the report.

[0047] The reporting unit can apply different reporting algorithms depending on the project category when submitting reports. For example, the reporting unit may use a specific algorithm for IT projects. For example, it may use a different algorithm for manufacturing projects. For example, it may select an appropriate algorithm for service industry projects. This ensures that appropriate reports are provided by applying different reporting algorithms depending on the project category. Some or all of the above processing in the reporting unit may be performed using AI, for example, or not. For example, the reporting unit can input project category data into the AI, which can then apply an appropriate reporting algorithm.

[0048] The reporting department can prioritize reports based on the project submission dates. For example, it might prioritize reporting on projects with approaching deadlines, or postpone reporting on projects with later deadlines. The reporting department can also adjust the order of reports based on submission dates. This allows for timely reporting by prioritizing reports based on project submission dates. Some or all of the above processes in the reporting department may be performed using AI, or not. For example, the reporting department could input project submission date data into an AI, which could then determine the reporting priority.

[0049] The reporting department can adjust the order of reports based on the relevance of the projects. For example, the reporting department can prioritize reporting on highly relevant projects. For example, it can postpone reporting on less relevant projects. The reporting department can also adjust the order of reports based on the relevance of the projects. This allows for prioritizing the reporting of highly relevant projects by adjusting the order of reports based on their relevance. Some or all of the above processing in the reporting department may be performed using AI, for example, or not using AI. For example, the reporting department can input project relevance data into AI, which can then adjust the order of reports.

[0050] The advisory unit can adjust the level of detail of its advice based on the importance of the project. For example, it can provide detailed advice to high-priority projects and concise advice to low-priority projects. The advisory unit can also adjust the depth of its advice according to the importance of the project. This allows it to provide appropriate advice by adjusting the level of detail based on the importance of the project. Some or all of the above processes in the advisory unit may be performed using AI, for example, or not. For example, the advisory unit can input project importance data into the AI, which can then adjust the level of detail of the advice.

[0051] The advisory unit can apply different advisory algorithms depending on the project category when providing advice. For example, the advisory unit may use a specific algorithm to advise IT projects. For example, the advisory unit may use a different algorithm to advise manufacturing projects. For example, the advisory unit may select an appropriate algorithm to advise service industry projects. This allows for the provision of appropriate advice by applying different advisory algorithms depending on the project category. Some or all of the above processing in the advisory unit may be performed using AI, for example, or without AI. For example, the advisory unit can input project category data into AI, and the AI ​​can apply an appropriate advisory algorithm.

[0052] The advice unit can prioritize advice based on the project submission deadline. For example, it may prioritize advice for projects with approaching deadlines, or postpone advice for projects with later deadlines. The advice unit can also adjust the order of advice based on submission dates. This allows for advice to be given in a way that ensures deadlines are met by prioritizing advice based on project submission dates. Some or all of the above processes in the advice unit may be performed using AI, or not. For example, the advice unit can input project submission date data into an AI, which can then determine the priority of advice.

[0053] The advice unit can adjust the order of advice based on the relevance of the projects. For example, the advice unit may prioritize advice for highly relevant projects. For example, it may postpone advice for less relevant projects. The advice unit can also adjust the order of advice based on the relevance of the projects. This allows for prioritizing advice for highly relevant projects by adjusting the order of advice based on the relevance of the projects. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input project relevance data into the AI, which can then adjust the order of advice.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The AI ​​agent system can also be equipped with a feedback unit. This unit collects user feedback and uses it to improve the system. For example, by providing feedback on the advice given, the quality of the advice can be improved. The feedback unit can also evaluate user satisfaction and provide data to improve system performance. Furthermore, the feedback unit can collect user opinions and requests and incorporate them into adding or improving system features. This allows for continuous system improvement based on user feedback, providing a more user-friendly system.

[0056] The AI ​​agent system can also be equipped with a predictive unit. This unit predicts the future progress of a project based on collected progress data. For example, it can analyze historical data and predict future delay risks based on current progress. The predictive unit can also predict the project's completion date and provide appropriate alerts to supervisors. Furthermore, it can suggest optimal resource allocation to improve project efficiency. This allows for proactive understanding of project progress and the implementation of appropriate countermeasures.

[0057] AI agent systems can also be equipped with a learning unit. This unit learns the user's work patterns and preferences, providing individually optimized advice. For example, it can learn solutions the user has preferred in the past and prioritize presenting them when similar problems arise. The learning unit can also suggest new methods to improve the user's work efficiency. Furthermore, the learning unit can continuously improve the quality of its advice based on user feedback. This allows for more effective advice and improved work efficiency for the user.

[0058] The AI ​​agent system can also include a notification unit. This unit notifies the user of important progress and alerts. For example, it can notify the user when a key project milestone is approaching, allowing them to take appropriate action. The notification unit can also provide alerts if progress is behind schedule or problems arise. Furthermore, it can send reminders based on the user's schedule to ensure important tasks are not forgotten. This allows the user to receive important information in a timely manner and take appropriate action.

[0059] The AI ​​agent system can also be equipped with a collaboration section. This section facilitates communication among team members and ensures smooth project progress. For example, team members can share progress in real time and respond quickly when problems arise. The collaboration section can also assist with task assignment and coordination among team members. Furthermore, it can collect opinions and ideas from team members and use them to improve the project. This strengthens communication across the entire team and contributes to project success.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The data collection unit monitors employees' work in real time and collects progress data. For example, the data collection unit can use sensors and log analysis technology to detect employees' work and collect progress data. Step 2: The analysis department analyzes the progress data collected by the data collection department. For example, they use data mining techniques, statistical analysis techniques, and machine learning techniques to analyze the progress data and understand the current status of the project. Step 3: The reporting department grasps the results based on the analysis obtained by the analysis department and reports them to their superior. For example, the reporting department provides an appropriate report to their superior based on the analysis results. Step 4: The Advice Department gathers information and provides advice on areas where there are deadlocks, based on the analysis results obtained by the Analysis Department. For example, the Advice Department gathers relevant information on deadlocks and proposes solutions.

[0062] (Example of form 2) The AI ​​agent system according to an embodiment of the present invention is a system that analyzes the progress of an employee's work in real time, grasps the results based on the acquired data, and provides input to the supervisor. This AI agent system monitors the employee's work in real time and accumulates and analyzes progress data. Next, it grasps the results based on the acquired data and provides input to the supervisor. Furthermore, if there is an area where the employee is stuck in their work, the AI ​​agent gathers information and automatically provides advice. This mechanism frees the user from creating progress reports and allows them to concentrate on more creative work. Also, if the employee gets stuck in their work, the AI ​​agent provides effective advice. First, the AI ​​agent monitors the employee's work in real time and accumulates and analyzes progress data. For example, the AI ​​agent automatically collects data and displays the progress in real time so that project managers and supervisors can grasp the progress of each person in charge of each task. This improves the transparency of progress and makes it easier to grasp results. Next, the AI ​​agent grasps the results based on the acquired data and provides input to the supervisor. For example, the AI ​​agent analyzes the progress of a project and provides an appropriate report to the supervisor. This makes it easier for the supervisor to grasp the current status of the project and give appropriate instructions. Furthermore, if an employee encounters difficulties in their work, the AI ​​agent will gather information and automatically provide advice. For example, if an employee faces difficulties with a particular task, the AI ​​agent will collect relevant information and suggest solutions. This allows employees to quickly resolve problems and move forward with their work. This system frees users from creating progress reports, allowing them to focus on more creative work. For instance, employees no longer need to spend time on progress reports, freeing up that time to generate new ideas or improve projects. Also, because the AI ​​agent provides effective advice when employees get stuck, they can work with confidence. This AI agent can be used across multiple sectors, particularly in IT, manufacturing, and service industries, in the project management software market and the business efficiency market, and is expected to have a large market share.Advances in generative AI technology and real-time data analysis enable AI agents to process and analyze data more quickly and accurately than ever before, making them a timely solution in today's business environment where immediate response and efficiency improvements are crucial. This allows AI agent systems to monitor employee progress in real time and automatically report and advise managers.

[0063] The AI ​​agent system according to this embodiment comprises a data collection unit, an analysis unit, a reporting unit, and an advice unit. The data collection unit monitors the employee's work in real time and collects progress data. The data collection unit monitors the employee's work in real time, for example, using sensors or log analysis technology. The data collection unit can detect the employee's work with sensors and collect progress data. The data collection unit can also analyze the employee's work using log analysis technology and collect progress data. The data collection unit monitors the employee's work in real time and stores progress data. The analysis unit analyzes the progress data collected by the data collection unit. The analysis unit analyzes the progress data using data mining technology, for example, and grasps the current status of the project. The analysis unit can analyze the progress data using statistical analysis technology, for example, and grasp the current status of the project. The analysis unit can also analyze the progress data using machine learning technology and grasp the current status of the project. The reporting unit grasps the results based on the analysis results obtained by the analysis unit and reports to the supervisor. The reporting unit makes an appropriate report to the supervisor based on the analysis results. The reporting department can, for example, provide appropriate reports to superiors based on the analysis results. The reporting department can also provide appropriate reports to superiors based on the analysis results. The advice department gathers information on areas where there are obstacles based on the analysis results obtained by the analysis department and provides advice. The advice department can, for example, gather relevant information on areas where there are obstacles and propose solutions. The advice department can, for example, gather relevant information on areas where there are obstacles and propose solutions. The advice department can also gather relevant information on areas where there are obstacles and propose solutions. As a result, the AI ​​agent system according to the embodiment can grasp the progress of employees in real time and automatically report to and advise superiors.

[0064] The data collection department monitors employees' work in real time and collects progress data. Specifically, it monitors employees' work in real time using sensors and log analysis technology. For example, software installed on employees' desktop computers detects keystrokes and mouse movements and records the progress of their work. Cameras and motion sensors installed in the office also monitor employees' movements and understand the progress of their work. This data is transmitted in real time to a central database and stored. Furthermore, the data collection department uses log analysis technology to analyze employees' work and collect progress data. For example, it analyzes logs from project management tools and version control systems used by employees to understand task completion status and code change history. This allows the data collection department to monitor employees' work in detail and accurately collect progress data. The collected data is updated in real time and used to understand the status of the entire system. Furthermore, the data collection department centrally manages this data and can link it with other systems and departments as needed. For example, the collected data is stored on a cloud server and made accessible to the analysis and reporting departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0065] The Analysis Department analyzes progress data collected by the Data Collection Department. Specifically, it uses data mining techniques to analyze progress data and understand the current status of the project. For example, it uses data mining techniques to extract employee work patterns and progress trends to evaluate the project's progress. It can also use statistical analysis techniques to analyze progress data and understand the project's current status. For example, it uses statistical analysis techniques to analyze task completion rates and the distribution of work time to evaluate the project's progress. Furthermore, the Analysis Department can also use machine learning techniques to analyze progress data and understand the project's current status. For example, it uses machine learning techniques to predict project progress based on past data, which helps in the early detection of risks and the development of countermeasures. This allows the Analysis Department to quickly and accurately analyze collected data and understand the project's current status in real time. In addition, the Analysis Department can utilize past data and statistical information to conduct long-term risk assessments and trend analyses. For example, it can predict fluctuations in risks for specific tasks or periods based on past project data and develop future countermeasures. Furthermore, the Analysis Department can use anomaly detection algorithms to detect unusual patterns and abnormal data, and issue warnings early. This allows the analysis department 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 security of the entire system.

[0066] The reporting department grasps the results based on the analysis obtained by the analysis department and reports them to its superiors. Specifically, it provides appropriate reports to superiors based on the analysis results. For example, it reports the project's progress and risk assessment to superiors based on the analysis results and proposes appropriate countermeasures. Furthermore, the reporting department can provide appropriate reports to superiors based on the analysis results. For example, it reports the project's progress and risk assessment to superiors based on the analysis results and proposes appropriate countermeasures. In addition, the reporting department can provide appropriate reports to superiors based on the analysis results. For example, it reports the project's progress and risk assessment to superiors based on the analysis results and proposes appropriate countermeasures. This allows the reporting department to provide appropriate reports to superiors based on the analysis results, report the project's progress and risk assessment to superiors, and propose appropriate countermeasures. Furthermore, the reporting department can provide appropriate reports to superiors based on the analysis results. For example, it reports the project's progress and risk assessment to superiors based on the analysis results and proposes appropriate countermeasures. This allows the reporting department to provide appropriate reports to superiors based on the analysis results, report the project's progress and risk assessment to superiors, and propose appropriate countermeasures.

[0067] The advisory department gathers information on areas where problems have arisen based on the analysis results obtained by the analysis department and provides advice. Specifically, it gathers relevant information on areas where problems have arisen and proposes solutions. For example, it can gather relevant information on areas where problems have arisen and propose solutions. Furthermore, the advisory department can also gather relevant information on areas where problems have arisen and propose solutions. For example, it can gather relevant information on areas where problems have arisen and propose solutions. This allows the advisory department to gather relevant information on areas where problems have arisen and propose solutions. Furthermore, the advisory department can also gather relevant information on areas where problems have arisen and propose solutions. For example, it can gather relevant information on areas where problems have arisen and propose solutions. This allows the advisory department to gather relevant information on areas where problems have arisen and propose solutions.

[0068] The data collection unit can monitor employees' work in real time and accumulate progress data. For example, the data collection unit can detect employees' work using sensors and accumulate progress data. The data collection unit can also analyze employees' work using log analysis technology and accumulate progress data. For example, the data collection unit can monitor employees' work in real time and accumulate progress data. This makes it easier to understand the progress status by monitoring employees' work in real time and accumulating progress data. 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 detect employees' work using sensors, input progress data into AI, and the AI ​​can accumulate progress data.

[0069] The analysis department can analyze the accumulated progress data and understand the current status of the project. The analysis department can, for example, use data mining techniques to analyze the progress data and understand the current status of the project. The analysis department can also, for example, use statistical analysis techniques to analyze the progress data and understand the current status of the project. The analysis department can also, for example, use machine learning techniques to analyze the progress data and understand the current status of the project. In this way, the current status of the project can be understood by analyzing the accumulated progress data. Some or all of the above processing in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can input the accumulated progress data into AI, and the AI ​​can analyze the progress data and understand the current status of the project.

[0070] The reporting department can provide appropriate reports to superiors based on the analysis results. For example, the reporting department can provide appropriate reports to superiors based on the analysis results. The reporting department can provide appropriate reports to superiors based on the analysis results. The reporting department can also provide appropriate reports to superiors based on the analysis results. This makes it easier for superiors to understand the current status of the project by providing appropriate reports to superiors based on the analysis results. Some or all of the above processes in the reporting department may be performed using AI, for example, or not using AI. For example, the reporting department can input the analysis results into AI, and the AI ​​can provide appropriate reports to superiors.

[0071] The advisory department can collect relevant information regarding the areas where an issue is stuck and propose solutions. The advisory department can, for example, collect relevant information regarding the areas where an issue is stuck and propose solutions. The advisory department can, for example, collect relevant information regarding the areas where an issue is stuck and propose solutions. The advisory department can, for example, collect relevant information regarding the areas where an issue is stuck and propose solutions. This allows employees to quickly resolve problems by collecting relevant information regarding the areas where an issue is stuck and proposing solutions. Some or all of the above-described processes in the advisory department may be performed using AI, for example, or not using AI. For example, the advisory department can input relevant information regarding the areas where an issue is stuck into an AI, and the AI ​​can propose solutions.

[0072] The data collection unit can estimate the user's emotions and adjust the timing of progress data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the collection timing to reduce the user's burden. For example, if the user is relaxed, the data collection unit can accelerate the collection timing to collect data efficiently. For example, if the user is in a hurry, the data collection unit can optimize the collection timing to collect data quickly. This reduces the user's burden by adjusting the timing of progress data collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user emotion data into AI, which can then adjust the timing of progress data collection.

[0073] The data collection unit can analyze the user's past work history and select the optimal data collection method. For example, the data collection unit can suggest the optimal data collection method based on the data collection methods the user has used in the past. For example, the data collection unit can select an efficient data collection method from the user's past work history. For example, the data collection unit can analyze the user's past work patterns and select the optimal data collection method. In this way, the optimal data collection method can be selected by analyzing the user's past work history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past work history data into AI, and the AI ​​can select the optimal data collection method.

[0074] The data collection unit can filter progress data based on the user's current projects and areas of interest. For example, the data collection unit can prioritize collecting data related to the project the user is currently working on. For example, the data collection unit can filter relevant data based on the user's areas of interest. For example, the data collection unit can collect necessary data based on the progress of the user's current projects. This allows for the priority collection of relevant data by filtering based on the user's current projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's project data into AI, which can then perform the filtering.

[0075] The data collection unit can estimate the user's emotions and determine the priority of the progress data to collect based on the estimated user emotions. For example, if the user is stressed, the data collection unit can postpone collecting less important data. For example, if the user is relaxed, the data collection unit can prioritize collecting more important data. For example, if the user is in a hurry, the data collection unit can quickly collect the most important data. This allows for the priority collection of important data by determining the priority of the progress data to collect based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user emotion data into an AI, which can then determine the priority of the progress data.

[0076] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting progress data. 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 can filter highly relevant data based on the user's current location. For example, the data collection unit can also collect the most relevant data by considering the user's geographical location. This enables efficient data collection by prioritizing the collection of highly relevant data by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into the AI, which can then prioritize the collection of highly relevant data.

[0077] The data collection unit can analyze the user's social media activity and collect relevant data when collecting progress data. For example, the data collection unit can extract relevant data from the user's social media activity. 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 also collect relevant data by referring to the activities of the user's social media followers and friends. This allows for the efficient 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 the AI ​​can collect relevant data.

[0078] 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 can display the analysis results using simple graphs or charts. For example, if the user is relaxed, the analysis unit can provide a detailed analysis report. For example, if the user is in a hurry, the analysis unit can provide a concise report that gets straight to the point. This allows for the provision of analysis results that are easy for the user to understand by adjusting the presentation of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input user emotion data into AI, and the AI ​​can adjust the presentation of the analysis.

[0079] The analysis unit can adjust the level of detail of the analysis based on the importance of the project. For example, the analysis unit can perform a detailed analysis for high-importance projects, and a concise analysis for low-importance projects. The analysis unit can also adjust the depth of the analysis according to the importance of the project. This allows for the provision of appropriate analysis results by adjusting the level of detail of the analysis based on the importance of the project. 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 project importance data into the AI, which can then adjust the level of detail of the analysis.

[0080] The analysis unit can apply different analysis algorithms depending on the project category during analysis. For example, the analysis unit may use a specific algorithm for IT projects. For example, it may use a different algorithm for manufacturing projects. For example, the analysis unit may select an appropriate algorithm for service industry projects. By applying different analysis algorithms depending on the project category, appropriate analysis results can be provided. 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 project category data into AI, and the AI ​​can apply an appropriate analysis algorithm.

[0081] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide a short analysis report. For example, if the user is relaxed, the analysis unit can provide a detailed analysis report. For example, if the user is in a hurry, the analysis unit can provide a concise report. By adjusting the length of the analysis based on the user's emotions, the analysis results of an appropriate length can be provided to the user. 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 AI, and the AI ​​can adjust the length of the analysis.

[0082] The analysis department can prioritize analyses based on project submission deadlines. For example, it might prioritize projects with approaching deadlines, or postpone projects with later deadlines. The analysis department can also adjust the order of analyses based on submission deadlines. This allows analyses to be completed in time for submission deadlines by prioritizing them based on project submission dates. Some or all of the above processes in the analysis department may be performed using AI, or not. For example, the analysis department could input project submission date data into an AI, which could then determine the analysis priorities.

[0083] The analysis unit can adjust the order of analysis based on the relevance of projects during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant projects. For example, the analysis unit can postpone the analysis of less relevant projects. The analysis unit can also adjust the order of analysis based on the relevance of projects. This allows for prioritizing the analysis of highly relevant projects by adjusting the order of analysis based on project relevance. 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 project relevance data into AI, which can then adjust the order of analysis.

[0084] The reporting unit can estimate the user's emotions and adjust the presentation of the report based on the estimated emotions. For example, if the user is stressed, the reporting unit may use simple graphs or charts in its report. If the user is relaxed, the reporting unit may provide a detailed report. If the user is in a hurry, the reporting unit may provide a concise, to-the-point report. By adjusting the presentation of the report based on the user's emotions, the reporting unit can 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 may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reporting unit may be performed using AI or not using AI. For example, the reporting unit can input user emotion data into an AI, which can then adjust the presentation of the report.

[0085] The reporting department can adjust the level of detail in reports based on the importance of the project. For example, the reporting department can provide detailed reports for high-priority projects, and concise reports for low-priority projects. The reporting department can also adjust the depth of the report according to the importance of the project. This allows for the provision of appropriate reports by adjusting the level of detail based on the importance of the project. Some or all of the above processes in the reporting department may be performed using AI, for example, or not. For example, the reporting department can input project importance data into the AI, which can then adjust the level of detail in the report.

[0086] The reporting unit can apply different reporting algorithms depending on the project category when submitting reports. For example, the reporting unit may use a specific algorithm for IT projects. For example, it may use a different algorithm for manufacturing projects. For example, it may select an appropriate algorithm for service industry projects. This ensures that appropriate reports are provided by applying different reporting algorithms depending on the project category. Some or all of the above processing in the reporting unit may be performed using AI, for example, or not. For example, the reporting unit can input project category data into the AI, which can then apply an appropriate reporting algorithm.

[0087] The reporting unit can estimate the user's emotions and adjust the length of the report based on the estimated emotions. For example, if the user is stressed, the reporting unit can provide a short report. For example, if the user is relaxed, the reporting unit can provide a detailed report. For example, if the user is in a hurry, the reporting unit can provide a concise report. By adjusting the length of the report based on the user's emotions, the reporting unit can provide a report of an appropriate length for the user. 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 reporting unit may be performed using AI or not using AI. For example, the reporting unit can input user emotion data into an AI, and the AI ​​can adjust the length of the report.

[0088] The reporting department can prioritize reports based on the project submission dates. For example, it might prioritize reporting on projects with approaching deadlines, or postpone reporting on projects with later deadlines. The reporting department can also adjust the order of reports based on submission dates. This allows for timely reporting by prioritizing reports based on project submission dates. Some or all of the above processes in the reporting department may be performed using AI, or not. For example, the reporting department could input project submission date data into an AI, which could then determine the reporting priority.

[0089] The reporting department can adjust the order of reports based on the relevance of the projects. For example, the reporting department can prioritize reporting on highly relevant projects. For example, it can postpone reporting on less relevant projects. The reporting department can also adjust the order of reports based on the relevance of the projects. This allows for prioritizing the reporting of highly relevant projects by adjusting the order of reports based on their relevance. Some or all of the above processing in the reporting department may be performed using AI, for example, or not using AI. For example, the reporting department can input project relevance data into AI, which can then adjust the order of reports.

[0090] The advice unit can estimate the user's emotions and adjust the way it expresses advice based on those emotions. For example, if the user is stressed, the advice unit can provide simple and specific advice. If the user is relaxed, the advice unit can provide detailed advice. If the user is in a hurry, the advice unit can provide quick and to-the-point advice. By adjusting the way it expresses advice based on the user's emotions, it is possible to provide advice 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 advice unit may be performed using AI or not using AI. For example, the advice unit can input user emotion data into AI, and the AI ​​can adjust the way it expresses advice.

[0091] The advisory unit can adjust the level of detail of its advice based on the importance of the project. For example, it can provide detailed advice to high-priority projects and concise advice to low-priority projects. The advisory unit can also adjust the depth of its advice according to the importance of the project. This allows it to provide appropriate advice by adjusting the level of detail based on the importance of the project. Some or all of the above processes in the advisory unit may be performed using AI, for example, or not. For example, the advisory unit can input project importance data into the AI, which can then adjust the level of detail of the advice.

[0092] The advisory unit can apply different advisory algorithms depending on the project category when providing advice. For example, the advisory unit may use a specific algorithm to advise IT projects. For example, the advisory unit may use a different algorithm to advise manufacturing projects. For example, the advisory unit may select an appropriate algorithm to advise service industry projects. This allows for the provision of appropriate advice by applying different advisory algorithms depending on the project category. Some or all of the above processing in the advisory unit may be performed using AI, for example, or without AI. For example, the advisory unit can input project category data into AI, and the AI ​​can apply an appropriate advisory algorithm.

[0093] The advice unit can estimate the user's emotions and adjust the length of the advice based on the estimated emotions. For example, if the user is stressed, the advice unit can provide short, concise advice. For example, if the user is relaxed, the advice unit can provide detailed advice. For example, if the user is in a hurry, the advice unit can provide quick and concise advice. By adjusting the length of the advice based on the user's emotions, the advice unit can provide advice of an appropriate length for the user. 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 advice unit may be performed using AI or not using AI. For example, the advice unit can input user emotion data into an AI, and the AI ​​can adjust the length of the advice.

[0094] The advice unit can prioritize advice based on the project submission deadline. For example, it may prioritize advice for projects with approaching deadlines, or postpone advice for projects with later deadlines. The advice unit can also adjust the order of advice based on submission dates. This allows for advice to be given in a way that ensures deadlines are met by prioritizing advice based on project submission dates. Some or all of the above processes in the advice unit may be performed using AI, or not. For example, the advice unit can input project submission date data into an AI, which can then determine the priority of advice.

[0095] The advice unit can adjust the order of advice based on the relevance of the projects. For example, the advice unit may prioritize advice for highly relevant projects. For example, it may postpone advice for less relevant projects. The advice unit can also adjust the order of advice based on the relevance of the projects. This allows for prioritizing advice for highly relevant projects by adjusting the order of advice based on the relevance of the projects. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input project relevance data into the AI, which can then adjust the order of advice.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The AI ​​agent system can also be equipped with a feedback unit. This unit collects user feedback and uses it to improve the system. For example, by providing feedback on the advice given, the quality of the advice can be improved. The feedback unit can also evaluate user satisfaction and provide data to improve system performance. Furthermore, the feedback unit can collect user opinions and requests and incorporate them into adding or improving system features. This allows for continuous system improvement based on user feedback, providing a more user-friendly system.

[0098] The AI ​​agent system can also be equipped with a predictive unit. This unit predicts the future progress of a project based on collected progress data. For example, it can analyze historical data and predict future delay risks based on current progress. The predictive unit can also predict the project's completion date and provide appropriate alerts to supervisors. Furthermore, it can suggest optimal resource allocation to improve project efficiency. This allows for proactive understanding of project progress and the implementation of appropriate countermeasures.

[0099] AI agent systems can also be equipped with a learning unit. This unit learns the user's work patterns and preferences, providing individually optimized advice. For example, it can learn solutions the user has preferred in the past and prioritize presenting them when similar problems arise. The learning unit can also suggest new methods to improve the user's work efficiency. Furthermore, the learning unit can continuously improve the quality of its advice based on user feedback. This allows for more effective advice and improved work efficiency for the user.

[0100] The AI ​​agent system can also include a notification unit. This unit notifies the user of important progress and alerts. For example, it can notify the user when a key project milestone is approaching, allowing them to take appropriate action. The notification unit can also provide alerts if progress is behind schedule or problems arise. Furthermore, it can send reminders based on the user's schedule to ensure important tasks are not forgotten. This allows the user to receive important information in a timely manner and take appropriate action.

[0101] The AI ​​agent system can also be equipped with a collaboration section. This section facilitates communication among team members and ensures smooth project progress. For example, team members can share progress in real time and respond quickly when problems arise. The collaboration section can also assist with task assignment and coordination among team members. Furthermore, it can collect opinions and ideas from team members and use them to improve the project. This strengthens communication across the entire team and contributes to project success.

[0102] The AI ​​agent system can further enhance user motivation using emotion estimation capabilities. For example, if a user is feeling stressed, it can provide relaxing advice and encouraging messages. If a user is highly motivated, it can offer advice to encourage further challenges. Furthermore, if a user is tired, it can send messages prompting them to take a break. This allows for appropriate support based on the user's emotions, helping to maintain motivation.

[0103] The AI ​​agent system can further manage the user's stress level using emotion estimation capabilities. For example, if the user is experiencing high stress levels, it can suggest resources and activities to reduce stress. Conversely, if the user is relaxed, it can provide advice to prevent stress. Furthermore, it can adjust task priorities and reduce the burden based on the user's stress level. This helps manage user stress and maintain a healthy work environment.

[0104] The AI ​​agent system can further analyze user feedback using sentiment estimation capabilities to improve the system. For example, it can analyze the sentiment of user feedback and distinguish between positive and negative feedback. It can also respond quickly to negative feedback and implement corrective measures. Furthermore, it can strengthen the system's strengths based on positive feedback. This allows for the effective use of user sentiment-based feedback to improve system quality.

[0105] The AI ​​agent system can further adjust the user's communication style using emotion estimation capabilities. For example, if the user is stressed, it can communicate in a simple and clear manner. If the user is relaxed, it can provide detailed information. Furthermore, if the user is in a hurry, it can communicate quickly and to the point. This allows for effective information transmission by adjusting the communication style based on the user's emotions.

[0106] The AI ​​agent system can further optimize the user's learning style using emotion estimation capabilities. For example, if the user is stressed, it can provide simple and easy-to-understand learning content. If the user is relaxed, it can provide detailed learning materials. Furthermore, if the user is in a hurry, it can provide concise learning content that gets straight to the point. This allows the system to optimize the learning style based on the user's emotions and support effective learning.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The data collection unit monitors employees' work in real time and collects progress data. For example, the data collection unit can use sensors and log analysis technology to detect employees' work and collect progress data. Step 2: The analysis department analyzes the progress data collected by the data collection department. For example, they use data mining techniques, statistical analysis techniques, and machine learning techniques to analyze the progress data and understand the current status of the project. Step 3: The reporting department grasps the results based on the analysis obtained by the analysis department and reports them to their superior. For example, the reporting department provides an appropriate report to their superior based on the analysis results. Step 4: The Advice Department gathers information and provides advice on areas where there are deadlocks, based on the analysis results obtained by the Analysis Department. For example, the Advice Department gathers relevant information on deadlocks and proposes solutions.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] Each of the multiple elements described above, including the data collection unit, analysis unit, reporting unit, and advice unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit monitors employees' work in real time using the sensors and log analysis technology of the smart device 14 and collects progress data. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the collected progress data. The reporting unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and reports to the supervisor based on the analysis results. The advice unit is implemented by, for example, the control unit 46A of the smart device 14 and collects information on areas where work is stalled and provides advice. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the data collection unit, analysis unit, reporting unit, and advice unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit monitors the employee's work in real time using the sensors and log analysis technology of the smart glasses 214 and collects progress data. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected progress data. The reporting unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and reports to the supervisor based on the analysis results. The advice unit is implemented, for example, by the control unit 46A of the smart glasses 214 and collects information on areas where work is stalled and provides advice. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the data collection unit, analysis unit, reporting unit, and advice unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit monitors the employee's work in real time using the sensors and log analysis technology of the headset terminal 314 and collects progress data. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the collected progress data. The reporting unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and reports to the supervisor based on the analysis results. The advice unit is implemented by, for example, the control unit 46A of the headset terminal 314 and collects information on areas where work is stalled and provides advice. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] Each of the multiple elements described above, including the data collection unit, analysis unit, reporting unit, and advice unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit monitors the employee's work in real time using the robot 414's sensors and log analysis technology and collects progress data. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the collected progress data. The reporting unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and reports to the supervisor based on the analysis results. The advice unit is implemented by, for example, the control unit 46A of the robot 414 and collects information on areas where work is stalled and provides advice. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] (Note 1) A data collection unit that collects progress data, An analysis unit analyzes the progress data collected by the aforementioned collection unit, The reporting department grasps the results based on the analysis obtained by the aforementioned analysis department and reports them to its superiors. Based on the analysis results obtained by the aforementioned analysis unit, an advice unit collects information and provides advice on areas where there are deadlocks, Equipped with A system characterized by the following features. (Note 2) The aforementioned collection unit is Monitor employees' work in real time and accumulate progress data. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is Analyze accumulated progress data to understand the current status of the project. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reporting department, Based on the analysis results, provide an appropriate report to your supervisor. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned advice section, Gather relevant information on the parts where you're stuck and propose solutions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of progress data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Analyze the user's past work history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting progress data, filter it based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates the user's emotions and prioritizes the progress data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting progress data, the system prioritizes collecting highly relevant data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting progress data, analyze users' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is It estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is During the analysis, adjust the level of detail based on the importance of the project. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is During analysis, different analysis algorithms are applied depending on the project category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is During the analysis, prioritize the analysis based on the project submission deadline. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During the analysis, adjust the order of analysis based on the relevance of the projects. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned reporting department, The system estimates the user's emotions and adjusts the way reports are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned reporting department, When reporting, adjust the level of detail in the report based on the importance of the project. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned reporting department, When reporting, different reporting algorithms are applied depending on the project category. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned reporting department, The system estimates the user's sentiment and adjusts the length of the report based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned reporting department, When submitting reports, prioritize them based on the project submission deadline. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned reporting department, When reporting, adjust the order of reports based on the relevance of the projects. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned advice section, It estimates the user's emotions and adjusts the way advice is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned advice section, When providing advice, adjust the level of detail based on the importance of the project. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned advice section, When providing advice, different advice algorithms are applied depending on the project category. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned advice section, It estimates the user's emotions and adjusts the length of the advice based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned advice section, When providing advice, prioritize the advice based on the project's submission deadline. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned advice section, When giving advice, adjust the order of advice based on the relevance of the project. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0181] 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 progress data, An analysis unit analyzes the progress data collected by the aforementioned collection unit, The reporting department grasps the results based on the analysis obtained by the aforementioned analysis department and reports them to its superiors. Based on the analysis results obtained by the aforementioned analysis unit, an advice unit collects information and provides advice on areas where there are deadlocks, Equipped with A system characterized by the following features.

2. The aforementioned collection unit is Monitor employees' work in real time and accumulate progress data. The system according to feature 1.

3. The aforementioned analysis unit is Analyze accumulated progress data to understand the current status of the project. The system according to feature 1.

4. The aforementioned reporting department, Based on the analysis results, provide an appropriate report to your supervisor. The system according to feature 1.

5. The aforementioned advice section, Gather relevant information on the parts where you're stuck and propose solutions. The system according to feature 1.

6. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of progress data collection based on those estimated emotions. The system according to feature 1.

7. The aforementioned collection unit is Analyze the user's past work history and select the optimal data collection method. The system according to feature 1.

8. The aforementioned collection unit is When collecting progress data, filter it based on the user's current projects and areas of interest. The system according to feature 1.

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

10. The aforementioned collection unit is When collecting progress data, the system prioritizes collecting highly relevant data by considering the user's geographical location. The system according to feature 1.

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