system

Abstract

The system according to this embodiment aims to efficiently manage the learning progress and work progress of new employees and to appropriately determine the necessity of meetings. [Solution] The system according to the embodiment comprises a collection unit, a decision unit, and an automation unit. The collection unit collects the learning progress and work progress of new employees. The decision unit determines the necessity of a meeting based on the information collected by the collection unit. The automation unit automates daily tasks based on the results determined by the decision unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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 efficiently manage the learning progress and work progress of new employees and appropriately judge the necessity of meetings.

[0005] The system according to the embodiment aims to efficiently manage the learning progress and work progress of new employees and appropriately judge the necessity of meetings.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, a decision unit, and an automation unit. The data collection unit collects the learning progress and work progress of new employees. The decision unit determines the necessity of a meeting based on the information collected by the data collection unit. The automation unit automates daily tasks based on the results determined by the decision unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently manage the learning progress and work progress of new employees and appropriately determine the necessity of meetings. [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 manages communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The system according to an embodiment of the present invention is a system in which an AI agent and a new employee learn together, realizing immediate information sharing and improved work efficiency. This system collects the learning progress and work progress of new employees, determines the necessity of meetings, and automates daily tasks, thereby providing an efficient work environment. For example, this system creates an environment in which new employees and an AI agent learn together. New employees receive guidance from the AI ​​agent to deepen their basic understanding of the work. The AI ​​agent grasps the learning progress of new employees in real time and provides necessary information. Next, this system determines the necessity of meetings based on the progress and importance of the work. The AI ​​agent analyzes the progress and importance of each task and determines whether a meeting is necessary. Furthermore, this system automates daily tasks using generative AI. Generative AI automatically processes routine tasks and work. This allows new employees to concentrate on more important tasks. This system reduces meeting time to near zero and realizes an efficient work environment. Thus, this system can realize an efficient work environment by determining the necessity of meetings based on the learning progress and work progress of new employees and automating daily tasks.

[0029] The system according to this embodiment comprises a data collection unit, a decision unit, and an automation unit. The data collection unit collects the learning progress and work progress of new employees. For example, the data collection unit can collect data such as test results, study time, and achievement level to understand the learning progress of new employees. The data collection unit can also collect data such as task completion status and adherence to deadlines to understand the work progress. The decision unit determines the necessity of a meeting based on the information collected by the data collection unit. For example, the decision unit can analyze the progress and importance of work to determine whether a meeting is necessary. The decision unit determines the necessity of a meeting based on criteria such as delays in progress and the presence or absence of important decisions. The automation unit automates daily tasks based on the results determined by the decision unit. For example, the automation unit can automatically process routine tasks and work. The automation unit can automate tasks such as email processing, data entry, and report creation. As a result, the system according to this embodiment can realize an efficient work environment by determining the necessity of meetings based on the learning progress and work progress of new employees and automating daily tasks. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input learning progress data of new employees into the AI, which can analyze the data to understand the learning progress. Some or all of the above-described processes in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input work progress data collected by the data collection unit into the AI, which can analyze the data to determine the necessity of a meeting. Some or all of the above-described processes in the automation unit may be performed using a generation AI, for example, or without a generation AI. For example, the automation unit can input tasks determined by the decision-making unit into a generation AI, which can automatically process the tasks.

[0030] The data collection department collects information on the learning progress and work progress of new employees. Specifically, it can collect data such as the results of tests taken by new employees, the time spent studying, and the degree of achievement for each learning item. This data is automatically acquired from online learning platforms and learning management systems (LMS). For example, online test results are collected as detailed data such as the correct answer rate and answer time for each question, and learning time is recorded as the time logged into the learning platform and the time spent on each content. The degree of achievement is evaluated as the completion status of each learning module and the level of skills acquired. In addition, to understand the progress of work, data such as the completion status and adherence to deadlines can be collected from project management tools and task management systems. For example, the progress of each task is collected as data such as the start date, end date, and progress rate, and adherence to deadlines is evaluated by comparing the planned completion date with the actual completion date. This allows the data collection department to understand the learning progress and work progress of new employees in detail and in real time. Furthermore, the data collection unit can centrally manage this data and collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the decision-making and automation units. Adjusting the frequency and accuracy of data collection allows for flexible responses to specific situations and conditions. This enables the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The decision-making unit determines the necessity of a meeting based on the information collected by the data collection unit. Specifically, it analyzes collected data on the learning progress of new employees and the progress of their work to determine whether a meeting is necessary. For example, based on learning progress data, if a new employee is struggling with a particular learning item or is behind schedule, a follow-up meeting is deemed necessary. Similarly, based on work progress data, if a project is behind schedule or important tasks are incomplete, a progress check meeting is deemed necessary. The decision-making unit inputs this data into the AI, which analyzes the data to determine the necessity of a meeting. The AI ​​evaluates the necessity of a meeting based on specific conditions and patterns, using past data and statistical information. For example, it analyzes past meeting data to evaluate whether meetings were effective for specific progress or learning progress, and suggests a meeting if a similar situation occurs. The AI ​​can also use anomaly detection algorithms to detect unusual patterns or abnormal data, and warn of the need for a meeting early. This allows the decision-making unit to quickly and accurately analyze the collected data and determine the necessity of a meeting in real time. Furthermore, the decision-making unit can also perform long-term risk assessments and trend analyses. For example, based on past data, it can predict fluctuations in risk at specific times or situations and assess the need for future meetings. This allows the decision-making unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the reliability and security of the entire system.

[0032] The automation unit automates daily tasks based on the decisions made by the decision-making unit. Specifically, it can automatically process routine tasks and standardized work. For example, if the decision-making unit determines that a meeting is necessary, the automation unit automatically schedules the meeting and sends notifications to relevant parties. It can also automate tasks such as email processing, data entry, and report creation. The automation unit inputs these tasks into a generation AI, which then automatically processes them. The generation AI uses natural language processing technology to analyze email content and generate appropriate replies. For data entry tasks, the generation AI analyzes the input data and automatically extracts and inputs the necessary information. For report creation, the generation AI automatically creates reports based on collected data and according to standardized report templates. This allows the automation unit to efficiently process daily tasks and improve operational efficiency. Furthermore, the automation unit can monitor the progress of tasks and adjust task priorities as needed. For example, if a high-priority task arises, the automation unit temporarily suspends processing of other tasks and prioritizes the important task. Furthermore, the automation unit can continuously improve the accuracy and efficiency of task processing by collecting task processing results as feedback and utilizing them as training data for the generated AI. This allows the automation unit to simultaneously achieve operational efficiency and quality improvement, thereby enhancing the overall system performance.

[0033] The data collection unit can collect information to answer questions from new employees. For example, it can collect information such as FAQs, manuals, and expert opinions. By collecting information to answer questions, the data collection unit can improve the learning efficiency of new employees. Some or all of the above-described processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can input the content of a new employee's question into an AI, which then searches for and provides relevant information.

[0034] The decision-making unit can analyze the progress and importance of tasks and determine whether a meeting is necessary. For example, to understand the progress of tasks, the decision-making unit can analyze data such as task completion status and adherence to deadlines. The decision-making unit can also use criteria such as the impact and priority of tasks to evaluate their importance. By analyzing the progress and importance of tasks, the decision-making unit can appropriately determine the necessity of a meeting. Some or all of the above processes in the decision-making unit may be performed using AI, for example, or not. For example, the decision-making unit can input task progress data into AI, which can then analyze the data to determine the necessity of a meeting.

[0035] The automation unit can automatically process routine tasks and work. For example, it can automate tasks such as processing emails, data entry, and report creation. By automating routine tasks and work, the automation unit allows new employees to focus on more important tasks. Some or all of the above-mentioned processes in the automation unit may be performed using or without a generative AI. For example, the automation unit can input routine tasks into a generative AI, which can then process the tasks automatically.

[0036] The automation unit can generate periodic reports using a generation AI. For example, the automation unit can automatically generate periodic reports such as weekly, monthly, and quarterly reports using the generation AI. By automatically generating periodic reports using the generation AI, the automation unit can improve the efficiency of reporting operations. Some or all of the above processes in the automation unit may be performed using the generation AI, or they may not be performed using the generation AI. For example, the automation unit can input a template for a periodic report into the generation AI, and the generation AI can automatically generate the report.

[0037] The automation unit can perform data entry automatically using a generation AI. For example, the automation unit can automatically perform data entry tasks such as entering customer information or sales data using the generation AI. By automating data entry using the generation AI, the automation unit can improve the efficiency of data entry work. Some or all of the above-mentioned processes in the automation unit may be performed using the generation AI, or they may be performed without the generation AI. For example, the automation unit can input a data entry template into the generation AI, and the generation AI can automatically enter the data.

[0038] The data collection unit can analyze the past learning history of new employees and select the optimal information collection method. For example, the data collection unit can prioritize providing learning methods that have been effective for new employees in the past. Furthermore, the data collection unit can provide supplementary information for areas where new employees struggle. In addition, the data collection unit can provide information in stages according to the new employee's learning progress. This allows the optimal information collection method to be selected by analyzing the new employee's past learning history. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input the new employee's past learning history data into an AI, which can then analyze the data and select the optimal information collection method.

[0039] The data collection unit can filter information based on the new employee's current projects and areas of interest during the information gathering process. For example, the data collection unit can prioritize providing information related to the projects the new employee is currently working on. It can also filter and provide relevant information based on the new employee's areas of interest. Furthermore, the data collection unit can monitor project progress in real time to quickly provide the new employee with the information they need. This allows for the rapid provision of necessary information by filtering it based on the new employee's current projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the new employee's project data and areas of interest data into an AI, which can then analyze the data and filter the relevant information.

[0040] The data collection unit can prioritize the collection of highly relevant information by considering the geographical location of new employees during information gathering. For example, if a new employee is in the office, the data collection unit can prioritize providing office-related information. If a new employee is out of the office, the data collection unit can prioritize providing information related to that location. Furthermore, if a new employee is working remotely, the data collection unit can prioritize providing information related to remote work. In this way, by considering the geographical location of new employees, highly relevant information can be prioritized for collection. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the geographical location data of new employees into AI, and the AI ​​can analyze the data to collect highly relevant information.

[0041] The data collection unit can analyze the social media activities of new employees and collect relevant information during the information gathering process. For example, the data collection unit can provide information related to topics that new employees have shown interest in on social media. It can also provide information about experts that new employees follow on social media. Furthermore, the data collection unit can provide relevant information based on the information that new employees have shared on social media. In this way, relevant information can be collected by analyzing the social media activities of new employees. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the social media data of new employees into an AI, which can then analyze the data and collect relevant information.

[0042] The decision-making unit can improve the accuracy of its judgment when determining the necessity of a meeting by considering the interrelationships between tasks. For example, the decision-making unit can grasp the progress of tasks in real time and determine the necessity of a meeting by considering the interrelationships. It can also analyze the importance of tasks and determine the necessity of a meeting by considering the interrelationships. Furthermore, the decision-making unit can determine the necessity of a meeting by considering the dependencies between tasks. In this way, the necessity of a meeting can be appropriately determined by considering the interrelationships between tasks. Some or all of the above processing in the decision-making unit may be performed using AI or not. For example, the decision-making unit can input interrelationship data of tasks into AI, and the AI ​​can analyze the data to determine the necessity of a meeting.

[0043] The decision-making unit can consider the attribute information of the person submitting the task when determining the necessity of a meeting. For example, if the person submitting the task is a new employee, the decision-making unit can carefully determine the necessity of a meeting. Also, if the person submitting the task is an experienced employee, the decision-making unit can quickly determine the necessity of a meeting. Furthermore, the decision-making unit can determine the necessity of a meeting based on the person submitting the task's position and responsibilities. In this way, by considering the attribute information of the person submitting the task, the necessity of a meeting can be appropriately determined. Some or all of the above processing in the decision-making unit may be performed using AI or not. For example, the decision-making unit can input the attribute information of the person submitting the task into AI, and the AI ​​can analyze the data to determine the necessity of a meeting.

[0044] The decision-making unit can make a decision on the necessity of a meeting by considering the geographical distribution of the work. For example, if the work spans multiple locations, the decision-making unit can carefully determine the necessity of a meeting. Also, if the work is concentrated in a specific location, the decision-making unit can quickly determine the necessity of a meeting. Furthermore, the decision-making unit can determine the necessity of a meeting based on the geographical distribution of the work. In this way, by considering the geographical distribution of the work, the necessity of a meeting can be appropriately determined. Some or all of the above processing in the decision-making unit may be performed using AI or not. For example, the decision-making unit can input geographical distribution data of the work into AI, and the AI ​​can analyze the data to determine the necessity of a meeting.

[0045] The decision-making unit can improve the accuracy of its judgment when determining the necessity of a meeting by referring to relevant business literature. For example, the decision-making unit can refer to business-related literature to determine the necessity of a meeting. It can also refer to literature related to the progress of the work to determine the necessity of a meeting. Furthermore, it can refer to literature related to the importance of the work to determine the necessity of a meeting. In this way, by referring to business-related literature, the necessity of a meeting can be appropriately determined. Some or all of the above processing in the decision-making unit may be performed using AI or not. For example, the decision-making unit can input business-related literature data into AI, and the AI ​​can analyze the data to determine the necessity of a meeting.

[0046] The automation unit can improve the accuracy of automation by considering the interrelationships of the tasks to be automated. For example, the automation unit can optimize the automation order by considering the dependencies between tasks. Furthermore, the automation unit can grasp the progress of tasks in real time and perform automation while considering their interrelationships. In addition, the automation unit can analyze the importance of tasks and perform automation while considering their interrelationships. This improves the accuracy of automation by considering the interrelationships of tasks. Some or all of the above-described processes in the automation unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the automation unit can input task interrelationship data into a generative AI, which can then analyze the data to optimize the automation order.

[0047] The automation unit can perform automation while considering the attribute information of the person who submitted the task to be automated. For example, if the task submitter is a new employee, the automation unit can prioritize the automation of low-priority tasks. Conversely, if the task submitter is an experienced employee, the automation unit can prioritize the automation of high-priority tasks. Furthermore, the automation unit can determine the priority of automation based on the job title and responsibilities of the task submitter. This makes it possible to automate appropriate tasks by considering the attribute information of the task submitter. Some or all of the above processing in the automation unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the automation unit can input the attribute information of the task submitter into a generating AI, and the generating AI can analyze the data to determine the priority of automation.

[0048] The automation unit can perform automation while considering the geographical distribution of the tasks to be automated. For example, if a task spans multiple locations, the automation unit can optimize the automation order. Furthermore, if tasks are concentrated at a specific location, the automation unit can determine the automation priority. In addition, the automation unit can improve the accuracy of automation based on the geographical distribution of tasks. This makes it possible to automate appropriate tasks by considering the geographical distribution of tasks. Some or all of the above-described processes in the automation unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the automation unit can input geographical distribution data of tasks into a generative AI, which can then analyze the data to optimize the automation order.

[0049] The automation unit can improve the accuracy of automation by referring to relevant literature for the task to be automated. For example, the automation unit can improve the accuracy of automation by referring to literature related to the task. Furthermore, the automation unit can improve the accuracy of automation by referring to literature related to the progress of the task. In addition, the automation unit can improve the accuracy of automation by referring to literature related to the importance of the task. Thus, the accuracy of automation is improved by referring to relevant literature for the task. Some or all of the above processing in the automation unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the automation unit can input relevant literature data for the task into a generating AI, and the generating AI can analyze the data to improve the accuracy of automation.

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

[0051] The decision-making unit can analyze a new employee's past meeting attendance history and use this information to determine the necessity of future meetings. For example, it can analyze the content and results of past meetings to determine whether a similar meeting is necessary. It can also evaluate the employee's contributions and participation in past meetings and use this as a criterion for determining meeting necessity. Furthermore, it can refer to feedback from past meetings to make an appropriate judgment about meeting necessity. In this way, by utilizing past meeting attendance history, the necessity of meetings can be determined more accurately.

[0052] The automation department can adjust task automation according to the skill level of new employees. For example, if a new employee is a beginner, simple tasks can be prioritized for automation to support skill development. As their skill level increases, more complex tasks can be automated to improve work efficiency. Furthermore, the scope and content of task automation can be adjusted according to the skill level. This enables task automation tailored to the skill level of new employees, leading to more efficient work execution.

[0053] The information gathering department can adjust the method of providing information according to the learning style of new employees. For example, new employees who prefer visual learning can be provided with information that makes extensive use of diagrams and graphs. New employees who prefer auditory learning can be provided with audio guides or podcast-style information. Furthermore, new employees who prefer practical learning can be provided with simulations and exercises related to actual work. This makes it possible to provide information tailored to the learning style of new employees and improve learning efficiency.

[0054] The data collection department can adjust the difficulty level of learning content according to the learning progress of new employees. For example, if learning progress is slow, basic content can be provided intensively to deepen understanding. Conversely, if learning progress is on track, more advanced content can be provided to improve skills. Furthermore, the scope and depth of learning content can also be adjusted according to learning progress. This makes it possible to provide learning content tailored to the learning progress of new employees, thereby achieving efficient learning.

[0055] The following briefly describes the processing flow for example form 1.

[0056] Step 1: The data collection unit collects information on the learning progress and work progress of new employees. For example, the data collection unit can collect data such as test results, study time, and achievement level, and for work progress, it can collect data such as task completion status and adherence to deadlines. The processing in the data collection unit may be performed using AI or not. Step 2: The decision-making unit determines the necessity of the meeting based on the information collected by the collection unit. For example, it analyzes the progress and importance of tasks and determines the necessity of the meeting based on criteria such as delays in progress or the presence of important decisions. The processing in the decision-making unit may be performed using AI or not. Step 3: The automation unit automates daily tasks based on the results determined by the decision unit. For example, the automation unit can automatically process routine tasks and work, such as processing emails, entering data, and creating reports. The processing in the automation unit may or may not be performed using generative AI.

[0057] (Example of form 2) The system according to an embodiment of the present invention is a system in which an AI agent and a new employee learn together, realizing immediate information sharing and improved work efficiency. This system collects the learning progress and work progress of new employees, determines the necessity of meetings, and automates daily tasks, thereby providing an efficient work environment. For example, this system creates an environment in which new employees and an AI agent learn together. New employees receive guidance from the AI ​​agent to deepen their basic understanding of the work. The AI ​​agent grasps the learning progress of new employees in real time and provides necessary information. Next, this system determines the necessity of meetings based on the progress and importance of the work. The AI ​​agent analyzes the progress and importance of each task and determines whether a meeting is necessary. Furthermore, this system automates daily tasks using generative AI. Generative AI automatically processes routine tasks and work. This allows new employees to concentrate on more important tasks. This system reduces meeting time to near zero and realizes an efficient work environment. Thus, this system can realize an efficient work environment by determining the necessity of meetings based on the learning progress and work progress of new employees and automating daily tasks.

[0058] The system according to this embodiment comprises a data collection unit, a decision unit, and an automation unit. The data collection unit collects the learning progress and work progress of new employees. For example, the data collection unit can collect data such as test results, study time, and achievement level to understand the learning progress of new employees. The data collection unit can also collect data such as task completion status and adherence to deadlines to understand the work progress. The decision unit determines the necessity of a meeting based on the information collected by the data collection unit. For example, the decision unit can analyze the progress and importance of work to determine whether a meeting is necessary. The decision unit determines the necessity of a meeting based on criteria such as delays in progress and the presence or absence of important decisions. The automation unit automates daily tasks based on the results determined by the decision unit. For example, the automation unit can automatically process routine tasks and work. The automation unit can automate tasks such as email processing, data entry, and report creation. As a result, the system according to this embodiment can realize an efficient work environment by determining the necessity of meetings based on the learning progress and work progress of new employees and automating daily tasks. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input learning progress data of new employees into the AI, which can analyze the data to understand the learning progress. Some or all of the above-described processes in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input work progress data collected by the data collection unit into the AI, which can analyze the data to determine the necessity of a meeting. Some or all of the above-described processes in the automation unit may be performed using a generation AI, for example, or without a generation AI. For example, the automation unit can input tasks determined by the decision-making unit into a generation AI, which can automatically process the tasks.

[0059] The data collection department collects information on the learning progress and work progress of new employees. Specifically, it can collect data such as the results of tests taken by new employees, the time spent studying, and the degree of achievement for each learning item. This data is automatically acquired from online learning platforms and learning management systems (LMS). For example, online test results are collected as detailed data such as the correct answer rate and answer time for each question, and learning time is recorded as the time logged into the learning platform and the time spent on each content. The degree of achievement is evaluated as the completion status of each learning module and the level of skills acquired. In addition, to understand the progress of work, data such as the completion status and adherence to deadlines can be collected from project management tools and task management systems. For example, the progress of each task is collected as data such as the start date, end date, and progress rate, and adherence to deadlines is evaluated by comparing the planned completion date with the actual completion date. This allows the data collection department to understand the learning progress and work progress of new employees in detail and in real time. Furthermore, the data collection unit can centrally manage this data and collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the decision-making and automation units. Adjusting the frequency and accuracy of data collection allows for flexible responses to specific situations and conditions. This enables the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0060] The decision-making unit determines the necessity of a meeting based on the information collected by the data collection unit. Specifically, it analyzes collected data on the learning progress of new employees and the progress of their work to determine whether a meeting is necessary. For example, based on learning progress data, if a new employee is struggling with a particular learning item or is behind schedule, a follow-up meeting is deemed necessary. Similarly, based on work progress data, if a project is behind schedule or important tasks are incomplete, a progress check meeting is deemed necessary. The decision-making unit inputs this data into the AI, which analyzes the data to determine the necessity of a meeting. The AI ​​evaluates the necessity of a meeting based on specific conditions and patterns, using past data and statistical information. For example, it analyzes past meeting data to evaluate whether meetings were effective for specific progress or learning progress, and suggests a meeting if a similar situation occurs. The AI ​​can also use anomaly detection algorithms to detect unusual patterns or abnormal data, and warn of the need for a meeting early. This allows the decision-making unit to quickly and accurately analyze the collected data and determine the necessity of a meeting in real time. Furthermore, the decision-making unit can also perform long-term risk assessments and trend analyses. For example, based on past data, it can predict fluctuations in risk at specific times or situations and assess the need for future meetings. This allows the decision-making unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the reliability and security of the entire system.

[0061] The automation unit automates daily tasks based on the decisions made by the decision-making unit. Specifically, it can automatically process routine tasks and standardized work. For example, if the decision-making unit determines that a meeting is necessary, the automation unit automatically schedules the meeting and sends notifications to relevant parties. It can also automate tasks such as email processing, data entry, and report creation. The automation unit inputs these tasks into a generation AI, which then automatically processes them. The generation AI uses natural language processing technology to analyze email content and generate appropriate replies. For data entry tasks, the generation AI analyzes the input data and automatically extracts and inputs the necessary information. For report creation, the generation AI automatically creates reports based on collected data and according to standardized report templates. This allows the automation unit to efficiently process daily tasks and improve operational efficiency. Furthermore, the automation unit can monitor the progress of tasks and adjust task priorities as needed. For example, if a high-priority task arises, the automation unit temporarily suspends processing of other tasks and prioritizes the important task. Furthermore, the automation unit can continuously improve the accuracy and efficiency of task processing by collecting task processing results as feedback and utilizing them as training data for the generated AI. This allows the automation unit to simultaneously achieve operational efficiency and quality improvement, thereby enhancing the overall system performance.

[0062] The data collection unit can collect information to answer questions from new employees. For example, it can collect information such as FAQs, manuals, and expert opinions. By collecting information to answer questions, the data collection unit can improve the learning efficiency of new employees. Some or all of the above-described processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can input the content of a new employee's question into an AI, which then searches for and provides relevant information.

[0063] The decision-making unit can analyze the progress and importance of tasks and determine whether a meeting is necessary. For example, to understand the progress of tasks, the decision-making unit can analyze data such as task completion status and adherence to deadlines. The decision-making unit can also use criteria such as the impact and priority of tasks to evaluate their importance. By analyzing the progress and importance of tasks, the decision-making unit can appropriately determine the necessity of a meeting. Some or all of the above processes in the decision-making unit may be performed using AI, for example, or not. For example, the decision-making unit can input task progress data into AI, which can then analyze the data to determine the necessity of a meeting.

[0064] The automation unit can automatically process routine tasks and work. For example, it can automate tasks such as processing emails, data entry, and report creation. By automating routine tasks and work, the automation unit allows new employees to focus on more important tasks. Some or all of the above-mentioned processes in the automation unit may be performed using or without a generative AI. For example, the automation unit can input routine tasks into a generative AI, which can then process the tasks automatically.

[0065] The automation unit can generate periodic reports using a generation AI. For example, the automation unit can automatically generate periodic reports such as weekly, monthly, and quarterly reports using the generation AI. By automatically generating periodic reports using the generation AI, the automation unit can improve the efficiency of reporting operations. Some or all of the above processes in the automation unit may be performed using the generation AI, or they may not be performed using the generation AI. For example, the automation unit can input a template for a periodic report into the generation AI, and the generation AI can automatically generate the report.

[0066] The automation unit can perform data entry automatically using a generation AI. For example, the automation unit can automatically perform data entry tasks such as entering customer information or sales data using the generation AI. By automating data entry using the generation AI, the automation unit can improve the efficiency of data entry work. Some or all of the above-mentioned processes in the automation unit may be performed using the generation AI, or they may be performed without the generation AI. For example, the automation unit can input a data entry template into the generation AI, and the generation AI can automatically enter the data.

[0067] The data collection unit can estimate the emotions of new employees and adjust the timing of information collection based on the estimated emotions. For example, if a new employee is stressed, the data collection unit can reduce the frequency of information collection and provide information when the employee is relaxed. If a new employee is excited, the data collection unit can provide information immediately to increase their motivation to learn. Furthermore, if a new employee is tired, the data collection unit can provide information during breaks to promote efficient learning. This allows for efficient information provision by adjusting the timing of information collection according to the emotions of new employees. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input new employee emotion data into an AI, which can estimate the emotions and adjust the timing of information collection.

[0068] The data collection unit can analyze the past learning history of new employees and select the optimal information collection method. For example, the data collection unit can prioritize providing learning methods that have been effective for new employees in the past. Furthermore, the data collection unit can provide supplementary information for areas where new employees struggle. In addition, the data collection unit can provide information in stages according to the new employee's learning progress. This allows the optimal information collection method to be selected by analyzing the new employee's past learning history. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input the new employee's past learning history data into an AI, which can then analyze the data and select the optimal information collection method.

[0069] The data collection unit can filter information based on the new employee's current projects and areas of interest during the information gathering process. For example, the data collection unit can prioritize providing information related to the projects the new employee is currently working on. It can also filter and provide relevant information based on the new employee's areas of interest. Furthermore, the data collection unit can monitor project progress in real time to quickly provide the new employee with the information they need. This allows for the rapid provision of necessary information by filtering it based on the new employee's current projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the new employee's project data and areas of interest data into an AI, which can then analyze the data and filter the relevant information.

[0070] The data collection unit can estimate the emotions of new employees and determine the priority of information to collect based on the estimated emotions. For example, if a new employee is feeling anxious, the data collection unit can prioritize providing information that provides a sense of security. If a new employee is excited, the data collection unit can prioritize providing information that increases their motivation to learn. Furthermore, if a new employee is tired, the data collection unit can prioritize providing information that helps them relax. This enables efficient information provision by prioritizing information according to the emotions of new employees. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input new employee emotion data into an AI, which can estimate emotions and determine the priority of information.

[0071] The data collection unit can prioritize the collection of highly relevant information by considering the geographical location of new employees during information gathering. For example, if a new employee is in the office, the data collection unit can prioritize providing office-related information. If a new employee is out of the office, the data collection unit can prioritize providing information related to that location. Furthermore, if a new employee is working remotely, the data collection unit can prioritize providing information related to remote work. In this way, by considering the geographical location of new employees, highly relevant information can be prioritized for collection. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the geographical location data of new employees into AI, and the AI ​​can analyze the data to collect highly relevant information.

[0072] The data collection unit can analyze the social media activities of new employees and collect relevant information during the information gathering process. For example, the data collection unit can provide information related to topics that new employees have shown interest in on social media. It can also provide information about experts that new employees follow on social media. Furthermore, the data collection unit can provide relevant information based on the information that new employees have shared on social media. In this way, relevant information can be collected by analyzing the social media activities of new employees. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the social media data of new employees into an AI, which can then analyze the data and collect relevant information.

[0073] The decision-making unit can estimate the emotions of new employees and adjust the criteria for determining the necessity of meetings based on the estimated emotions. For example, if a new employee is stressed, the decision-making unit can reduce the frequency of meetings. Conversely, if a new employee is relaxed, the decision-making unit can increase the frequency of meetings. Furthermore, if a new employee is excited, the decision-making unit can prioritize important meetings. This allows for efficient meeting management by adjusting the criteria for determining the necessity of meetings according to the emotions of new employees. 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 decision-making unit may be performed using AI or not. For example, the decision-making unit can input new employee emotion data into an AI, which can estimate emotions and adjust the criteria for determining the necessity of meetings.

[0074] The decision-making unit can improve the accuracy of its judgment when determining the necessity of a meeting by considering the interrelationships between tasks. For example, the decision-making unit can grasp the progress of tasks in real time and determine the necessity of a meeting by considering the interrelationships. It can also analyze the importance of tasks and determine the necessity of a meeting by considering the interrelationships. Furthermore, the decision-making unit can determine the necessity of a meeting by considering the dependencies between tasks. In this way, the necessity of a meeting can be appropriately determined by considering the interrelationships between tasks. Some or all of the above processing in the decision-making unit may be performed using AI or not. For example, the decision-making unit can input interrelationship data of tasks into AI, and the AI ​​can analyze the data to determine the necessity of a meeting.

[0075] The decision-making unit can consider the attribute information of the person submitting the task when determining the necessity of a meeting. For example, if the person submitting the task is a new employee, the decision-making unit can carefully determine the necessity of a meeting. Also, if the person submitting the task is an experienced employee, the decision-making unit can quickly determine the necessity of a meeting. Furthermore, the decision-making unit can determine the necessity of a meeting based on the person submitting the task's position and responsibilities. In this way, by considering the attribute information of the person submitting the task, the necessity of a meeting can be appropriately determined. Some or all of the above processing in the decision-making unit may be performed using AI or not. For example, the decision-making unit can input the attribute information of the person submitting the task into AI, and the AI ​​can analyze the data to determine the necessity of a meeting.

[0076] The decision unit can estimate the emotions of new employees and adjust the order in which meeting results are displayed based on the estimated emotions. For example, if a new employee is feeling anxious, the decision unit can prioritize displaying results that provide reassurance. Similarly, if a new employee is excited, the decision unit can prioritize displaying important results. Furthermore, if a new employee is tired, the decision unit can prioritize displaying concise results. This allows for efficient information provision by adjusting the order in which meeting results are displayed according to the emotions of the new employees. 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 decision unit may be performed using AI or not. For example, the decision unit can input new employee emotion data into an AI, which can then estimate the emotions and adjust the order in which meeting results are displayed.

[0077] The decision-making unit can make a decision on the necessity of a meeting by considering the geographical distribution of the work. For example, if the work spans multiple locations, the decision-making unit can carefully determine the necessity of a meeting. Also, if the work is concentrated in a specific location, the decision-making unit can quickly determine the necessity of a meeting. Furthermore, the decision-making unit can determine the necessity of a meeting based on the geographical distribution of the work. In this way, by considering the geographical distribution of the work, the necessity of a meeting can be appropriately determined. Some or all of the above processing in the decision-making unit may be performed using AI or not. For example, the decision-making unit can input geographical distribution data of the work into AI, and the AI ​​can analyze the data to determine the necessity of a meeting.

[0078] The decision-making unit can improve the accuracy of its judgment when determining the necessity of a meeting by referring to relevant business literature. For example, the decision-making unit can refer to business-related literature to determine the necessity of a meeting. It can also refer to literature related to the progress of the work to determine the necessity of a meeting. Furthermore, it can refer to literature related to the importance of the work to determine the necessity of a meeting. In this way, by referring to business-related literature, the necessity of a meeting can be appropriately determined. Some or all of the above processing in the decision-making unit may be performed using AI or not. For example, the decision-making unit can input business-related literature data into AI, and the AI ​​can analyze the data to determine the necessity of a meeting.

[0079] The automation unit can estimate the emotions of new employees and determine the priority of tasks to be automated based on the estimated emotions. For example, if a new employee is stressed, the automation unit can prioritize automating low-priority tasks. If a new employee is relaxed, the automation unit can prioritize automating high-priority tasks. Furthermore, if a new employee is excited, the automation unit can prioritize automating urgent tasks. This enables efficient task management by prioritizing tasks according to the emotions of new employees. 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 automation unit may be performed using or without a generative AI. For example, the automation unit can input new employee emotion data into a generative AI, which can estimate the emotions and determine the task priority.

[0080] The automation unit can improve the accuracy of automation by considering the interrelationships of the tasks to be automated. For example, the automation unit can optimize the automation order by considering the dependencies between tasks. Furthermore, the automation unit can grasp the progress of tasks in real time and perform automation while considering their interrelationships. In addition, the automation unit can analyze the importance of tasks and perform automation while considering their interrelationships. This improves the accuracy of automation by considering the interrelationships of tasks. Some or all of the above-described processes in the automation unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the automation unit can input task interrelationship data into a generative AI, which can then analyze the data to optimize the automation order.

[0081] The automation unit can perform automation while considering the attribute information of the person who submitted the task to be automated. For example, if the task submitter is a new employee, the automation unit can prioritize the automation of low-priority tasks. Conversely, if the task submitter is an experienced employee, the automation unit can prioritize the automation of high-priority tasks. Furthermore, the automation unit can determine the priority of automation based on the job title and responsibilities of the task submitter. This makes it possible to automate appropriate tasks by considering the attribute information of the task submitter. Some or all of the above processing in the automation unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the automation unit can input the attribute information of the task submitter into a generating AI, and the generating AI can analyze the data to determine the priority of automation.

[0082] The automation unit can estimate the emotions of new employees and adjust the display method of automated tasks based on the estimated emotions of the new employees. For example, if a new employee is nervous, the automation unit can provide a simple and highly visible display method. If a new employee is relaxed, the automation unit can provide a display method that includes detailed information. Furthermore, if a new employee is in a hurry, the automation unit can provide a display method that gets straight to the point. This allows for efficient information provision by adjusting the task display method according to the emotions of the new employees. 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 automation unit may be performed using or without a generative AI. For example, the automation unit can input new employee emotion data into a generative AI, which can estimate the emotion and adjust the task display method.

[0083] The automation unit can perform automation while considering the geographical distribution of the tasks to be automated. For example, if a task spans multiple locations, the automation unit can optimize the automation order. Furthermore, if tasks are concentrated at a specific location, the automation unit can determine the automation priority. In addition, the automation unit can improve the accuracy of automation based on the geographical distribution of tasks. This makes it possible to automate appropriate tasks by considering the geographical distribution of tasks. Some or all of the above-described processes in the automation unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the automation unit can input geographical distribution data of tasks into a generative AI, which can then analyze the data to optimize the automation order.

[0084] The automation unit can improve the accuracy of automation by referring to relevant literature for the task to be automated. For example, the automation unit can improve the accuracy of automation by referring to literature related to the task. Furthermore, the automation unit can improve the accuracy of automation by referring to literature related to the progress of the task. In addition, the automation unit can improve the accuracy of automation by referring to literature related to the importance of the task. Thus, the accuracy of automation is improved by referring to relevant literature for the task. Some or all of the above processing in the automation unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the automation unit can input relevant literature data for the task into a generating AI, and the generating AI can analyze the data to improve the accuracy of automation.

[0085] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0086] The information collection unit can monitor the health status of new employees and adjust the frequency and content of information provided according to their health condition. For example, if a new employee is unwell, the frequency of information provision can be reduced to lessen their burden. Conversely, if their health is good, the frequency of information provision can be increased to improve learning efficiency. Furthermore, information that promotes relaxation and health-related advice can be provided according to their health condition. This enables the provision of information tailored to the health status of new employees, thereby creating an efficient learning environment.

[0087] The decision-making unit can analyze a new employee's past meeting attendance history and use this information to determine the necessity of future meetings. For example, it can analyze the content and results of past meetings to determine whether a similar meeting is necessary. It can also evaluate the employee's contributions and participation in past meetings and use this as a criterion for determining meeting necessity. Furthermore, it can refer to feedback from past meetings to make an appropriate judgment about meeting necessity. In this way, by utilizing past meeting attendance history, the necessity of meetings can be determined more accurately.

[0088] The automation department can adjust task automation according to the skill level of new employees. For example, if a new employee is a beginner, simple tasks can be prioritized for automation to support skill development. As their skill level increases, more complex tasks can be automated to improve work efficiency. Furthermore, the scope and content of task automation can be adjusted according to the skill level. This enables task automation tailored to the skill level of new employees, leading to more efficient work execution.

[0089] The information gathering department can adjust the method of providing information according to the learning style of new employees. For example, new employees who prefer visual learning can be provided with information that makes extensive use of diagrams and graphs. New employees who prefer auditory learning can be provided with audio guides or podcast-style information. Furthermore, new employees who prefer practical learning can be provided with simulations and exercises related to actual work. This makes it possible to provide information tailored to the learning style of new employees and improve learning efficiency.

[0090] The decision-making unit can estimate the emotions of new employees and adjust the meeting's progress based on those estimates. For example, if new employees are nervous, the system can create a relaxed atmosphere to ensure a smooth meeting. If new employees are excited, the system can create an environment that encourages them to actively share their opinions. Furthermore, if new employees are tired, the system can simplify the meeting's progress to ensure efficiency. By adjusting the meeting's progress according to the emotions of new employees, effective meeting management becomes possible.

[0091] The automation unit can estimate the emotions of new employees and adjust the notification method for automated tasks based on those estimated emotions. For example, if a new employee is stressed, notifications can be kept to a minimum to reduce their burden. If a new employee is relaxed, detailed notifications can be provided to support task progress. Furthermore, if a new employee is in a hurry, only important tasks can be notified, allowing them to work efficiently. In this way, by adjusting the task notification method according to the emotions of new employees, efficient work performance can be achieved.

[0092] The data collection department can adjust the difficulty level of learning content according to the learning progress of new employees. For example, if learning progress is slow, basic content can be provided intensively to deepen understanding. Conversely, if learning progress is on track, more advanced content can be provided to improve skills. Furthermore, the scope and depth of learning content can also be adjusted according to learning progress. This makes it possible to provide learning content tailored to the learning progress of new employees, thereby achieving efficient learning.

[0093] The decision-making unit can estimate the emotions of new employees and adjust the meeting agenda based on those estimates. For example, if a new employee is feeling anxious, it can prioritize topics that provide reassurance. If a new employee is excited, it can set topics that encourage active participation. Furthermore, if a new employee is tired, it can summarize important topics concisely for discussion. By adjusting the meeting agenda according to the emotions of new employees, effective meeting management becomes possible.

[0094] The automation unit can estimate the emotions of new employees and adjust the feedback method for automated tasks based on those estimated emotions. For example, if a new employee is stressed, positive feedback can be prioritized to boost their motivation. If a new employee is relaxed, detailed feedback can be provided to support skill development. Furthermore, if a new employee is in a hurry, concise feedback can be provided to help them work efficiently. In this way, by adjusting the task feedback method according to the emotions of new employees, efficient work performance becomes possible.

[0095] The information gathering unit can estimate the emotions of new employees and adjust the timing of information provision based on those estimates. For example, if a new employee is feeling stressed, the frequency of information provision can be reduced to alleviate their burden. Conversely, if a new employee is relaxed, the frequency of information provision can be increased to improve learning efficiency. Furthermore, if a new employee is excited, information can be provided immediately to increase their motivation to learn. In this way, by adjusting the timing of information provision according to the emotions of new employees, efficient information provision becomes possible.

[0096] The following briefly describes the processing flow for example form 2.

[0097] Step 1: The data collection unit collects information on the learning progress and work progress of new employees. For example, the data collection unit can collect data such as test results, study time, and achievement level, and for work progress, it can collect data such as task completion status and adherence to deadlines. The processing in the data collection unit may be performed using AI or not. Step 2: The decision-making unit determines the necessity of the meeting based on the information collected by the collection unit. For example, it analyzes the progress and importance of tasks and determines the necessity of the meeting based on criteria such as delays in progress or the presence of important decisions. The processing in the decision-making unit may be performed using AI or not. Step 3: The automation unit automates daily tasks based on the results determined by the decision unit. For example, the automation unit can automatically process routine tasks and work, such as processing emails, entering data, and creating reports. The processing in the automation unit may or may not be performed using generative AI.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] Each of the multiple elements described above, including the data collection unit, decision unit, and automation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit uses the camera 42 and microphone 38B of the smart device 14 to collect the learning progress and work progress of new employees, and the control unit 46A processes the data. The decision unit is implemented in the specific processing unit 290 of the data processing unit 12, and determines the necessity of a meeting based on the collected data. The automation unit is implemented in the specific processing unit 290 of the data processing unit 12, and automates daily tasks using generating AI. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0102] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] Each of the multiple elements described above, including the data collection unit, decision-making unit, and automation unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit uses the camera 42 and microphone 238 of the smart glasses 214 to collect the learning progress and work progress of new employees, and the control unit 46A processes the data. The decision-making unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and determines the necessity of a meeting based on the collected data. The automation unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and automates daily tasks using generating AI. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0118] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] Each of the multiple elements described above, including the data collection unit, decision unit, and automation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit uses the camera 42 and microphone 238 of the headset terminal 314 to collect information on the learning progress and work progress of new employees, and the control unit 46A processes the data. The decision unit is implemented in the specific processing unit 290 of the data processing unit 12, and determines the necessity of a meeting based on the collected data. The automation unit is implemented in the specific processing unit 290 of the data processing unit 12, and automates daily tasks using generating AI. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0134] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] Each of the multiple elements described above, including the data collection unit, decision unit, and automation unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the data collection unit uses the camera 42 and microphone 238 of the robot 414 to collect the learning progress and work progress of new employees, and the control unit 46A processes the data. The decision unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and determines the necessity of a meeting based on the collected data. The automation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and automates daily tasks using generating AI. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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."

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] (Note 1) The collection department collects information on the learning progress and work progress of new employees, A judgment unit that determines the necessity of a meeting based on the information collected by the aforementioned collection unit, The system includes an automation unit that automates daily tasks based on the results determined by the aforementioned determination unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Gather information to answer questions from new employees. The system described in Appendix 1, characterized by the features described herein. (Note 3) The unit that makes the determination said, Analyze the progress and importance of tasks to determine whether a meeting is necessary. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned automation unit, Automate the processing of routine tasks and standardized work. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned automation unit, Generate regular reports using AI. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned automation unit, Data entry is automated using generation AI. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the emotions of new employees and adjusts the timing of information gathering based on these estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the past learning history of new employees to select the most suitable information gathering method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When gathering information, filter it based on the new employee's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is The system estimates the emotions of new employees and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When gathering information, prioritize collecting highly relevant information by considering the geographical location of new employees. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When gathering information, analyze the social media activity of new employees and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The unit that makes the determination said, We estimate the emotions of new employees and adjust the criteria for determining the necessity of meetings based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The unit that makes the determination said, When determining the necessity of a meeting, consider the interrelationships between tasks to improve the accuracy of the decision. The system described in Appendix 1, characterized by the features described herein. (Note 15) The unit that makes the determination said, When determining the necessity of a meeting, the attribute information of the person submitting the work should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 16) The unit that makes the determination said, The system estimates the emotions of new employees and adjusts the order in which meeting results are displayed based on the estimated emotions of the new employees. The system described in Appendix 1, characterized by the features described herein. (Note 17) The unit that makes the determination said, When determining the necessity of a meeting, the geographical distribution of work should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 18) The unit that makes the determination said, When determining the necessity of a meeting, refer to relevant business literature to improve the accuracy of the decision. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned automation unit, The system estimates the emotions of new employees and prioritizes tasks to be automated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned automation unit, Improve the accuracy of automation by considering the interrelationships between tasks to be automated. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned automation unit, The automation process takes into account the attribute information of the person who submitted the task to be automated. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned automation unit, This tool estimates the emotions of new employees and adjusts how automated tasks are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned automation unit, Automate tasks while considering their geographical distribution. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned automation unit, Refer to relevant literature for the task to be automated to improve the accuracy of automation. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0170] 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. The collection department collects information on the learning progress and work progress of new employees, A judgment unit that determines the necessity of a meeting based on the information collected by the aforementioned collection unit, The system includes an automation unit that automates daily tasks based on the results determined by the aforementioned determination unit. A system characterized by the following features.

2. The aforementioned collection unit is Gather information to answer questions from new employees. The system according to feature 1.

3. The unit that makes the determination said, Analyze the progress and importance of tasks to determine whether a meeting is necessary. The system according to feature 1.

4. The aforementioned automation unit, Automate the processing of routine tasks and standardized work. The system according to feature 1.

5. The aforementioned automation unit, The AI ​​generates periodic reports. The system according to feature 1.

6. The aforementioned automation unit, Data entry is automated using generation AI. The system according to feature 1.

7. The aforementioned collection unit is The system estimates the emotions of new employees and adjusts the timing of information gathering based on these estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze the past learning history of new employees to select the most suitable information gathering method. The system according to feature 1.

9. The aforementioned collection unit is When gathering information, filter it based on the new employee's current projects and areas of interest. The system according to feature 1.

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