system

Abstract

The system according to this embodiment aims to monitor employees' calendars and tasks in real time and to provide efficient information and emergency response. [Solution] The system according to the embodiment comprises a monitoring unit, a provision unit, a response unit, an optimization unit, and an extraction unit. The monitoring unit monitors employees' calendars and tasks in real time. The provision unit provides preparatory materials and trend information based on the information collected by the monitoring unit. The response unit responds to schedule changes and emergencies based on the information provided by the provision unit. The optimization unit learns and optimizes employee reactions and feedback based on the information handled by the response unit. The extraction unit extracts information using audio and video based on the information optimized by the optimization unit.

Description

Technical Field

[0006] , ,

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance 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 the management of employees' calendars and tasks is often performed manually, making it difficult to provide efficient information and respond to emergencies.

[0005] The system according to the embodiment aims to monitor employees' calendars and tasks in real time and provide efficient information and emergency response.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a monitoring unit, a provision unit, a response unit, an optimization unit, and an extraction unit. The monitoring unit monitors employees' calendars and tasks in real time. The provision unit provides preparatory materials and trend information based on the information collected by the monitoring unit. The response unit responds to schedule changes and emergencies based on the information provided by the provision unit. The optimization unit learns and optimizes employee responses and feedback based on the information handled by the response unit. The extraction unit extracts information using audio and video based on the information optimized by the optimization unit. [Effects of the Invention]

[0007] The system according to this embodiment can monitor employees' calendars and tasks in real time, enabling efficient information provision and emergency response. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The proactive information delivery agent system according to an embodiment of the present invention is a system that monitors employees' calendars and tasks in real time and automatically provides preparatory materials and trend information related to meetings and projects. The proactive information delivery agent system monitors employees' calendars and tasks in real time and collects information related to meetings and projects. For example, it collects information such as meeting agendas and project progress. Next, based on the collected information, the proactive information delivery agent system automatically provides preparatory materials and trend information related to meetings and projects. For example, it provides the latest research results and industry trend information related to the meeting agenda. It also provides necessary materials and information according to the project progress. Furthermore, the proactive information delivery agent system responds immediately to schedule changes and emergencies. For example, if the meeting time is changed or an urgent task arises, the proactive information delivery agent system provides the latest information in real time. This allows employees to always be aware of the latest information and respond quickly. In addition, the proactive information delivery agent system learns in real time from the responses and feedback to the newsletters that employees provide every morning and automatically optimizes the content and format for the following days. For example, if an employee shows high interest in a particular piece of information, the system prioritizes providing that information. Furthermore, the proactive information delivery agent system uses the power of multimedia, such as audio and video, to automatically extract important information from large amounts of data. This allows it to generate the information each employee needs to perform their tasks from the database and efficiently provide only the necessary information. For example, it enables employees to quickly grasp necessary information through audio instructions and video explanations. This system allows employees to perform their tasks efficiently without being overwhelmed by the sheer volume of information or missing important details. It also saves time searching for useful information, thereby improving work efficiency.This allows the proactive information delivery agent system to monitor employee calendars and tasks in real time, provide preparatory materials and trend information, respond to schedule changes and emergencies, learn and optimize from employee responses and feedback, and extract information using voice and video.

[0029] The proactive information provision agent system according to this embodiment comprises a monitoring unit, a provision unit, a response unit, an optimization unit, and an extraction unit. The monitoring unit monitors employees' calendars and tasks in real time. For example, the monitoring unit gains a detailed understanding of employees' schedules and task contents. The monitoring unit can collect and monitor in real time information on meetings and tasks registered in employees' calendars. For example, the monitoring unit can understand the agenda of meetings and the progress of projects registered in employees' calendars. The monitoring unit can also monitor the priority and progress of employees' tasks and collect necessary information. The provision unit provides preparatory materials and trend information based on the information collected by the monitoring unit. For example, the provision unit provides the latest research results and industry trend information related to meeting agendas. The provision unit enables employees to quickly grasp the information they need for meetings and projects. The provision unit can also provide necessary materials and information according to the progress of projects. For example, the provision unit provides the latest research results related to meeting agendas. The provision unit can also provide industry trend information. The response unit responds to schedule changes and emergencies based on information provided by the delivery unit. For example, the response unit provides up-to-date information if meeting times are changed or urgent tasks arise. The response unit enables employees to respond quickly. The response unit can also provide information to respond to emergencies. For example, the response unit provides up-to-date information if meeting times are changed. The response unit can also provide up-to-date information if urgent tasks arise. The optimization unit learns and optimizes employee responses and feedback based on the information handled by the response unit. For example, the optimization unit learns in real time from employee responses and feedback to the daily newsletter provided and automatically optimizes the content and format for subsequent days. The optimization unit ensures that employees receive more useful information. The optimization unit can also learn from employee responses and feedback and optimize the content and format of the information provided. For example, if an employee shows high interest in specific information, the optimization unit prioritizes providing that information.The extraction unit extracts information using audio and video based on information optimized by the optimization unit. The extraction unit can, for example, automatically extract important information from a large amount of data using audio and video. The extraction unit enables employees to quickly grasp the information they need. The extraction unit can also extract and provide information to employees using audio and video. For example, the extraction unit can enable employees to quickly grasp the information they need through audio instructions and video explanations. As a result, the proactive information provision agent system according to this embodiment can monitor employees' calendars and tasks in real time, provide preparatory materials and trend information, respond to schedule changes and emergencies, learn and optimize employee responses and feedback, and extract information using audio and video.

[0030] The monitoring department monitors employees' calendars and tasks in real time. Specifically, it integrates with calendar applications and task management tools to periodically acquire data in order to gain a detailed understanding of employees' schedules and tasks. The monitoring department can collect and monitor information on meetings and tasks registered in employees' calendars in real time. For example, to understand the agenda of meetings and the progress of projects registered in employees' calendars, it analyzes the content of calendar entries and extracts detailed information on related tasks and meetings. The monitoring department also monitors the priority and progress of employees' tasks and collects necessary information. This allows the monitoring department to understand the status of employees' work in real time and build a foundation for providing necessary information at the appropriate time. Furthermore, the monitoring department can instantly detect changes in employees' calendars and tasks and reflect them throughout the system, enabling responses based on the latest information. For example, if a meeting time is changed or a new task is added, the monitoring department can immediately detect these changes and notify other departments. In this way, the monitoring department can play a crucial role in improving the work efficiency of employees.

[0031] The Information Provision Department provides preparatory materials and trend information based on information collected by the Monitoring Department. Specifically, it collects and organizes necessary information from publicly available online databases and specialized information sources to provide the latest research findings and industry trend information related to the meeting agenda. The Information Provision Department provides relevant materials and information in an appropriate format so that employees can quickly grasp the information needed for meetings and projects. For example, when providing the latest research findings related to the meeting agenda, it clearly presents summaries and key points so that employees can grasp important information in a short time. The Information Provision Department can also provide necessary materials and information according to the progress of the project. For example, it provides the information and materials necessary for the next step according to the progress of the project, supporting employees in smoothly carrying out their work. Furthermore, the Information Provision Department can also provide industry trend information. For example, it provides information to understand the latest industry trends and the movements of competitors, so that employees can respond quickly to market changes. In this way, the Information Provision Department can play an important role in enabling employees to quickly and accurately grasp the information they need, thereby improving work efficiency and results.

[0032] The response department responds to schedule changes and emergencies based on information provided by the service provider. Specifically, it integrates with employee calendars and task management tools to immediately reflect changes in order to provide the latest information when meeting times are changed or urgent tasks arise. The response department notifies employees of changes in real time and prompts them to take necessary actions so that they can respond quickly. For example, if a meeting time is changed, it notifies employees of details such as the new time, place, and agenda to support a smooth response. The response department can also provide information to respond to emergencies. For example, if an urgent task arises, it provides details such as the importance, priority, and response method of the task so that employees can respond quickly and appropriately. Furthermore, the response department can collect employee feedback and continuously improve the accuracy and effectiveness of its responses. For example, based on employee feedback, the response department reviews notification content and response methods to achieve more effective responses. In this way, the response department can support employees in responding quickly and appropriately to schedule changes and emergencies, playing a crucial role in improving work efficiency and results.

[0033] The optimization unit learns from and optimizes employee responses and feedback based on the information handled by the response unit. Specifically, it uses machine learning algorithms to learn in real time from employee responses and feedback to the newsletter provided every morning, and automatically optimizes the content and format for subsequent days. The optimization unit analyzes employee responses and feedback in detail to optimize the content and format of the information provided, so that it can provide employees with more useful information. For example, if an employee shows high interest in a particular piece of information, it will prioritize providing that information. It also analyzes how employees reacted to the information provided and optimizes the method and timing of information delivery. Furthermore, the optimization unit can customize the content and format of information delivery according to employees' work patterns and individual needs. For example, if a particular employee prefers to receive information at a specific time, it will deliver the information according to that time. In this way, the optimization unit can achieve the most effective information delivery for employees and play a crucial role in improving work efficiency and performance.

[0034] The extraction unit extracts information using audio and video based on information optimized by the optimization unit. Specifically, it utilizes natural language processing and image recognition technologies to automatically extract important information from large amounts of data using audio and video. The extraction unit analyzes audio and video, extracts important information, and provides it to employees so that they can quickly grasp the information they need. For example, it can extract important statements and topics from meeting recordings and provide them as summaries. It can also extract important scenes and information from video data and provide them in a visually easy-to-understand format. Furthermore, the extraction unit can extract information using audio and video and provide it to employees. For example, it can enable employees to quickly grasp the information they need through audio instructions and video explanations. In this way, the extraction unit can play an important role in enabling employees to quickly grasp important information from large amounts of data, thereby improving work efficiency and results. In addition, the extraction unit can customize the information extraction method and delivery format according to employee needs. For example, if a particular employee prefers audio instructions, the system will prioritize providing information via audio to that employee. This allows the extraction unit to provide employees with the most effective information, playing a crucial role in improving operational efficiency and enhancing results.

[0035] The monitoring department can gain a detailed understanding of employees' schedules and tasks. For example, the monitoring department can track meeting agendas and project progress registered in employees' calendars. The monitoring department can monitor the priorities and progress of employees' tasks and collect necessary information. For example, the monitoring department can track meeting agendas registered in employees' calendars in detail. The monitoring department can also track project progress in detail. The monitoring department can track the priorities of employees' tasks in detail and collect necessary information. This allows for more accurate monitoring by providing a detailed understanding of employees' schedules and tasks. Some or all of the above processes in the monitoring department may be performed using AI, for example, or not. For example, the monitoring department can input meeting agendas registered in employees' calendars into AI and have the AI ​​perform a detailed analysis of the meeting agendas.

[0036] The information delivery department can provide the latest research findings and industry trend information related to the meeting agenda. For example, the information delivery department can provide the latest research findings related to the meeting agenda. The information delivery department can enable employees to quickly grasp the information needed for meetings and projects. The information delivery department can also provide industry trend information. For example, the information delivery department can provide the latest research findings related to the meeting agenda. The information delivery department can also provide industry trend information. This allows employees to stay up-to-date by providing the latest research findings and industry trend information related to the meeting agenda. Some or all of the above processing in the information delivery department may be performed using AI, for example, or not using AI. For example, the information delivery department can input the latest research findings related to the meeting agenda into AI and have the AI ​​deliver the latest research findings.

[0037] The response unit can provide up-to-date information when meeting times are changed or urgent tasks arise. For example, the response unit provides up-to-date information when meeting times are changed. The response unit enables employees to respond quickly. The response unit can also provide up-to-date information when urgent tasks arise. For example, the response unit provides up-to-date information when meeting times are changed. The response unit can also provide up-to-date information when urgent tasks arise. This allows employees to respond quickly by providing up-to-date information when meeting times are changed or urgent tasks arise. Some or all of the above processing in the response unit may be performed using AI, for example, or not using AI. For example, the response unit can input the latest information into the AI ​​when meeting times are changed and have the AI ​​perform the task of providing the latest information.

[0038] The optimization unit can learn in real time the responses and feedback that employees provide to the newsletters they send out every morning, and automatically optimize the content and format for subsequent days. For example, the optimization unit can learn in real time the responses and feedback that employees provide to the newsletters they send out every morning, and automatically optimize the content and format for subsequent days. The optimization unit ensures that employees receive more useful information. Furthermore, the optimization unit can learn from employee responses and feedback and optimize the content and format of the information it provides. For example, if an employee shows high interest in a particular piece of information, the optimization unit will prioritize providing that information. This allows the optimization unit to learn in real time the responses and feedback that employees provide to the newsletters they send out every morning, and automatically optimize the content and format for subsequent days, thereby providing employees with more useful information. Some or all of the above processing in the optimization unit may be performed using AI, or not. For example, the optimization unit can input the responses and feedback that employees provide to the newsletters they send out every morning into the AI, and have the AI ​​perform the optimization of the content and format for subsequent days.

[0039] The extraction unit can automatically extract important information from a large amount of data using audio and video. For example, the extraction unit automatically extracts important information from a large amount of data using audio and video. The extraction unit enables employees to quickly grasp the information they need. Furthermore, the extraction unit can extract information using audio and video and provide it to employees. For example, the extraction unit enables employees to quickly grasp the information they need through audio instructions and video explanations. This allows employees to quickly grasp the information they need by automatically extracting important information from a large amount of data using audio and video. Some or all of the above-described processes in the extraction unit may be performed using AI, or not. For example, the extraction unit can input audio and video into an AI and have the AI ​​perform the extraction of important information.

[0040] The monitoring department can analyze an employee's past schedule history and select the most appropriate monitoring method. For example, the monitoring department can prioritize monitoring meetings where the employee has frequently been late in the past. If an employee has forgotten an important task in the past, the monitoring department can intensify monitoring of that task. The monitoring department can also prioritize monitoring tasks that are concentrated in specific time periods based on the employee's past schedule history. This allows for more effective monitoring by analyzing the employee's past schedule history and selecting the most appropriate monitoring method. Some or all of the above processes in the monitoring department may be performed using AI, for example, or not. For example, the monitoring department can input the employee's past schedule history into AI and have the AI ​​select the most appropriate monitoring method.

[0041] The monitoring unit can filter calendars and tasks based on an employee's current projects and areas of interest. For example, the monitoring unit can prioritize monitoring tasks related to a project the employee is currently working on. It can also prioritize monitoring meetings and events related to an employee's areas of interest. Furthermore, the monitoring unit can reduce the frequency of monitoring other tasks so that employees can focus on their current projects. This allows for the provision of more relevant information by filtering based on an employee's current projects and areas of interest. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or not. For example, the monitoring unit can input information about an employee's current projects and areas of interest into an AI and have the AI ​​perform the filtering.

[0042] The monitoring unit can prioritize monitoring highly relevant tasks by considering employees' geographical location information when monitoring calendars and tasks. For example, if an employee is in a specific location, the monitoring unit will prioritize monitoring tasks related to that location. If an employee is traveling, the monitoring unit will prioritize monitoring tasks related to their destination. Furthermore, if an employee is working remotely, the monitoring unit can prioritize monitoring tasks performed at home. This allows for more effective monitoring by prioritizing highly relevant tasks while considering employees' geographical location information. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or not. For example, the monitoring unit can input employees' geographical location information into AI and have the AI ​​perform monitoring of highly relevant tasks.

[0043] The monitoring unit can analyze employees' social media activity and monitor related tasks when monitoring calendars and tasks. For example, the monitoring unit can prioritize monitoring tasks mentioned by employees on social media. The monitoring unit can prioritize monitoring tasks related to topics of interest from employees' social media activity. The monitoring unit can also prioritize monitoring tasks related to projects that employees follow on social media. This allows for the provision of more relevant information by analyzing employees' social media activity and monitoring related tasks. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or not. For example, the monitoring unit can input employees' social media activity into AI and have the AI ​​perform the monitoring of related tasks.

[0044] The information delivery unit can adjust the level of detail provided based on the importance of the information at the time of delivery. For example, the delivery unit can provide important information in detail to make it easy for employees to understand. It can also provide less important information concisely to reduce the burden on employees. Furthermore, the delivery unit can adjust the amount and format of the information provided according to its importance. This allows employees to prioritize understanding important information by adjusting the level of detail based on the importance of the information. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input the importance of the information into the AI ​​and have the AI ​​perform the adjustment of the level of detail of the delivery.

[0045] The information delivery unit can apply different delivery algorithms depending on the category of information at the time of delivery. For example, the delivery unit can organize and deliver meeting-related information by agenda item. For project-related information, it can deliver it according to its progress. Furthermore, the delivery unit can prioritize the delivery of the latest trend information. By applying different delivery algorithms depending on the category of information, employees can acquire information more efficiently. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not. For example, the delivery unit can input the information categories into the AI ​​and have the AI ​​execute the application of the delivery algorithm.

[0046] The information delivery department can determine the priority of information delivery based on the timing of information submission. For example, the department can prioritize the delivery of urgent information to enable employees to respond quickly. The department can also prioritize the delivery of information with an approaching deadline. Furthermore, the department can adjust the order in which information is delivered according to the submission timing. This allows employees to respond quickly by determining the priority of information delivery based on the submission timing. Some or all of the above processes in the information delivery department may be performed using AI, for example, or not. For example, the information delivery department can input the submission timing of information into the AI ​​and have the AI ​​determine the priority of information delivery.

[0047] The information delivery unit can adjust the order of delivery based on the relevance of the information. For example, the delivery unit can prioritize the delivery of highly relevant information to enable employees to acquire information efficiently. The delivery unit can also postpone the delivery of less relevant information. Furthermore, the delivery unit can adjust the order of delivery according to the relevance of the information. This allows employees to acquire information efficiently by adjusting the order of delivery based on the relevance of the information. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input the relevance of the information into the AI ​​and have the AI ​​perform the adjustment of the delivery order.

[0048] The response unit can analyze an employee's past response history to select the optimal response method when responding to an inquiry. For example, the response unit may prioritize response methods that the employee has preferred in the past. The response unit may select the most effective response method from the employee's past response history. Furthermore, the response unit can analyze an employee's past response history and propose the optimal response method. This allows for more effective responses by analyzing an employee's past response history and selecting the optimal response method. Some or all of the above processes in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input an employee's past response history into AI and have the AI ​​select the optimal response method.

[0049] The response unit can customize its response based on the employee's current situation. For example, if the employee is in a meeting, the response unit will respond in a way that does not disrupt the meeting. If the employee is traveling, the response unit will provide information relevant to their destination. Furthermore, if the employee is working remotely, the response unit can provide information relevant to tasks to be performed at home. This allows for a more appropriate response by customizing the response based on the employee's current situation. Some or all of the above processing in the response unit may be performed using AI, for example, or not. For example, the response unit can input the employee's current situation into the AI ​​and have the AI ​​customize the response.

[0050] The response unit can select the optimal response method by considering the employee's geographical location information when responding to an inquiry. For example, if the employee is in a specific location, the response unit can provide information related to that location. If the employee is on the move, the response unit can provide information related to the destination. Furthermore, if the employee is working remotely, the response unit can provide information related to tasks performed at home. This allows for more effective responses by selecting the optimal response method by considering the employee's geographical location information. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input the employee's geographical location information into AI and have the AI ​​select the optimal response method.

[0051] The response unit can analyze an employee's social media activity and propose a course of action when responding to an issue. For example, the response unit can provide information related to tasks mentioned by the employee on social media. The response unit can provide information related to topics of interest from the employee's social media activity. The response unit can also provide information related to projects that the employee follows on social media. This allows the response unit to provide more relevant information by analyzing the employee's social media activity and proposing a course of action. Some or all of the above processing in the response unit may be performed using AI, for example, or not using AI. For example, the response unit can input the employee's social media activity into AI and have the AI ​​propose a course of action.

[0052] The optimization unit can analyze employees' past responses and feedback during optimization to select the optimal optimization method. For example, the optimization unit may prioritize selecting optimization methods that employees have preferred in the past. The optimization unit selects the most effective optimization method based on employees' past responses and feedback. The optimization unit can also analyze employees' past responses and feedback and propose the optimal optimization method. This allows for more effective optimization by analyzing employees' past responses and feedback to select the optimal optimization method. Some or all of the above processes in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input employees' past responses and feedback into AI and have the AI ​​select the optimal optimization method.

[0053] The optimization unit can customize the optimization methods based on the employee's current situation during optimization. For example, if an employee is in a meeting, the optimization unit will optimize in a way that does not disrupt the meeting. If an employee is traveling, the optimization unit will provide information related to their destination. Furthermore, if an employee is working remotely, the optimization unit can provide information related to tasks to be performed at home. This allows for more appropriate optimization by customizing the optimization methods based on the employee's current situation. Some or all of the above-described processes in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input the employee's current situation into the AI ​​and have the AI ​​perform the customization of the optimization methods.

[0054] The optimization unit can select the optimal optimization method by considering the geographical location information of employees during the optimization process. For example, if an employee is in a specific location, the optimization unit can provide information related to that location. If an employee is on the move, the optimization unit can provide information related to their destination. Furthermore, if an employee is working remotely, the optimization unit can provide information related to tasks performed at home. By selecting the optimal optimization method while considering the geographical location information of employees, more effective optimization becomes possible. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input the geographical location information of employees into AI and have the AI ​​select the optimal optimization method.

[0055] The optimization unit can analyze employees' social media activity and propose optimization methods during the optimization process. For example, the optimization unit can provide information related to tasks mentioned by employees on social media. The optimization unit can provide information related to topics of interest from employees' social media activity. The optimization unit can also provide information related to projects that employees follow on social media. This allows for the provision of more relevant information by analyzing employees' social media activity and proposing optimization methods. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input employees' social media activity into AI and have the AI ​​execute the proposal of optimization methods.

[0056] The extraction unit can improve the accuracy of extraction by considering the interrelationships of information during the extraction process. For example, the extraction unit can group related information and extract it. The extraction unit analyzes the interrelationships of information and extracts the most relevant information. Furthermore, the extraction unit can improve the accuracy of extraction by considering the interrelationships of information. By improving the accuracy of extraction by considering the interrelationships of information, more reliable information can be provided. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input the interrelationships of information into AI and have AI perform the task of improving the accuracy of extraction.

[0057] The extraction unit can perform extraction while considering the attribute information of the information submitter. For example, the extraction unit can extract highly reliable information by considering the expertise of the information submitter. The extraction unit can extract important information by considering the position and experience of the information submitter. Furthermore, the extraction unit can analyze the attribute information of the information submitter and extract the most suitable information. As a result, by performing extraction while considering the attribute information of the information submitter, more reliable information can be provided. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without using AI. For example, the extraction unit can input the attribute information of the information submitter into AI and have the AI ​​perform the extraction.

[0058] The extraction unit can perform extraction while considering the geographical distribution of information. For example, the extraction unit can prioritize the extraction of information related to a specific region. The extraction unit extracts the most relevant information while considering the geographical distribution. Furthermore, the extraction unit can analyze the geographical distribution and extract the most relevant information. As a result, by performing extraction while considering the geographical distribution of information, more relevant information can be provided. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input the geographical distribution of information into AI and have the AI ​​perform the extraction.

[0059] The extraction unit can improve the accuracy of its extraction by referring to relevant literature during the extraction process. For example, the extraction unit extracts highly reliable information by referring to relevant literature. The extraction unit analyzes the relevant literature and extracts the most relevant information. Furthermore, the extraction unit can improve the accuracy of its extraction by considering the relevant literature. This allows for the provision of more reliable information by improving the accuracy of extraction by referring to relevant literature. Some or all of the above-described processes in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input relevant literature into AI and have AI perform the task of improving the accuracy of its extraction.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The monitoring unit can not only monitor employees' calendars and tasks in real time, but also their health status. For example, it can monitor employees' heart rate and sleep patterns, and provide appropriate alerts if there are any abnormalities in their health. The provision unit can provide health advice and resources based on employees' health status. For example, if an employee is under high stress, it can provide guidance for relaxation and suggest stretches. The response unit can adjust the workload according to the employee's health status. For example, if an employee's health is deteriorating, it can change the priority of tasks and reduce the workload. The optimization unit can optimize the information and task content provided based on the employee's health status. For example, if an employee is in good health, it can provide challenging tasks, and if their health is poor, it can provide easier tasks. The extraction unit can extract and provide necessary information based on the employee's health status. For example, if an employee's health is deteriorating, it can prioritize providing health-related information. This enables the provision of information that takes employee health status into consideration, thereby promoting employee health maintenance and improving work efficiency.

[0062] The monitoring unit can monitor not only employee calendars and tasks, but also employee device usage. For example, it can provide alerts prompting employees to take breaks if they are using their devices for extended periods. The provision unit can provide advice and resources on digital well-being based on employee device usage. For example, it can suggest exercises to reduce eye strain or propose digital detoxes. The response unit can adjust the workload based on employee device usage. For example, if an employee is using their device for extended periods, it can change the priority of tasks to reduce the workload. The optimization unit can optimize the information and tasks provided based on employee device usage. For example, if an employee is using their device frequently, it can provide tasks that can be done offline. The extraction unit can extract and provide necessary information based on employee device usage. For example, if an employee is using their device frequently, it can prioritize providing information on digital well-being. This enables the provision of information that takes employee device usage into consideration, thereby improving digital well-being.

[0063] The monitoring unit can not only monitor employees' calendars and tasks but also analyze their communication patterns. For example, it can monitor who employees frequently communicate with and how often, and provide necessary information. The provision unit can provide advice and resources on improving communication efficiency based on employees' communication patterns. For example, it can suggest guides for effective communication and how to use communication tools. The response unit can adjust the workload according to employees' communication patterns. For example, if there is a lot of communication, it can change the priority of tasks to reduce the workload. The optimization unit can optimize the information and task content provided based on employees' communication patterns. For example, if there is little communication, it can provide tasks for team building. The extraction unit can extract and provide necessary information based on employees' communication patterns. For example, if there is a lot of communication, it can prioritize providing information on improving communication efficiency. This makes it possible to provide information that takes employees' communication patterns into consideration, thereby improving communication efficiency.

[0064] The monitoring unit can not only monitor employees' calendars and tasks, but also analyze their learning history. For example, it can monitor the training and learning content employees have previously attended and provide necessary information. The provision unit can provide advice and resources on improving learning efficiency based on employees' learning history. For example, it can suggest effective learning methods and ways to utilize learning tools. The response unit can adjust the workload according to employees' learning history. For example, if there is a lot of learning to do, it can change the priority of tasks to reduce the workload. The optimization unit can optimize the information and task content provided based on employees' learning history. For example, if there is little learning to do, it can provide tasks that offer learning opportunities. The extraction unit can extract and provide necessary information based on employees' learning history. For example, if there is a lot of learning to do, it can prioritize providing information on improving learning efficiency. This makes it possible to provide information that takes employees' learning history into consideration, thereby improving learning efficiency.

[0065] The monitoring unit can not only monitor employees' calendars and tasks, but also analyze their hobbies and interests. For example, it can monitor events and tasks related to employees' hobbies and interests and provide necessary information. The provisioning unit can provide relevant advice and resources based on employees' hobbies and interests. For example, it can suggest information on hobby-related events or content that will pique their interest. The response unit can adjust the workload according to employees' hobbies and interests. For example, if there is a hobby-related event, it can change the priority of tasks to make it easier for employees to participate. The optimization unit can optimize the information and task content provided based on employees' hobbies and interests. For example, it can provide hobby-related tasks to improve motivation. The extraction unit can extract and provide necessary information based on employees' hobbies and interests. For example, it can prioritize providing hobby-related information. This makes it possible to provide information that takes employees' hobbies and interests into consideration, thereby improving motivation.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The monitoring department monitors employees' calendars and tasks in real time. For example, it gains a detailed understanding of employees' schedules and tasks, and collects information on meetings and tasks. This allows for monitoring of meeting agendas, project progress, and task priorities and progress. Step 2: The provisioning department provides preparatory materials and trend information based on the information collected by the monitoring department. For example, they provide the latest research findings and industry trend information related to the meeting agenda, enabling employees to quickly grasp the information needed for the meeting or project. Step 3: The response team responds to schedule changes and emergencies based on the information provided by the delivery team. For example, they provide up-to-date information if meeting times are changed or urgent tasks arise, enabling employees to respond quickly. Step 4: The optimization unit learns from and optimizes employee responses and feedback based on the information handled by the response unit. For example, it learns in real time from employee responses and feedback to the information provided and automatically optimizes the content and format for the following days. Step 5: The extraction unit extracts information using audio and video based on the information optimized by the optimization unit. For example, it automatically extracts important information from a large amount of data using audio and video, allowing employees to quickly grasp the information they need.

[0068] (Example of form 2) The proactive information delivery agent system according to an embodiment of the present invention is a system that monitors employees' calendars and tasks in real time and automatically provides preparatory materials and trend information related to meetings and projects. The proactive information delivery agent system monitors employees' calendars and tasks in real time and collects information related to meetings and projects. For example, it collects information such as meeting agendas and project progress. Next, based on the collected information, the proactive information delivery agent system automatically provides preparatory materials and trend information related to meetings and projects. For example, it provides the latest research results and industry trend information related to the meeting agenda. It also provides necessary materials and information according to the project progress. Furthermore, the proactive information delivery agent system responds immediately to schedule changes and emergencies. For example, if the meeting time is changed or an urgent task arises, the proactive information delivery agent system provides the latest information in real time. This allows employees to always be aware of the latest information and respond quickly. In addition, the proactive information delivery agent system learns in real time from the responses and feedback to the newsletters that employees provide every morning and automatically optimizes the content and format for the following days. For example, if an employee shows high interest in a particular piece of information, the system prioritizes providing that information. Furthermore, the proactive information delivery agent system uses the power of multimedia, such as audio and video, to automatically extract important information from large amounts of data. This allows it to generate the information each employee needs to perform their tasks from the database and efficiently provide only the necessary information. For example, it enables employees to quickly grasp necessary information through audio instructions and video explanations. This system allows employees to perform their tasks efficiently without being overwhelmed by the sheer volume of information or missing important details. It also saves time searching for useful information, thereby improving work efficiency.This allows the proactive information delivery agent system to monitor employee calendars and tasks in real time, provide preparatory materials and trend information, respond to schedule changes and emergencies, learn and optimize from employee responses and feedback, and extract information using voice and video.

[0069] The proactive information provision agent system according to this embodiment comprises a monitoring unit, a provision unit, a response unit, an optimization unit, and an extraction unit. The monitoring unit monitors employees' calendars and tasks in real time. For example, the monitoring unit gains a detailed understanding of employees' schedules and task contents. The monitoring unit can collect and monitor in real time information on meetings and tasks registered in employees' calendars. For example, the monitoring unit can understand the agenda of meetings and the progress of projects registered in employees' calendars. The monitoring unit can also monitor the priority and progress of employees' tasks and collect necessary information. The provision unit provides preparatory materials and trend information based on the information collected by the monitoring unit. For example, the provision unit provides the latest research results and industry trend information related to meeting agendas. The provision unit enables employees to quickly grasp the information they need for meetings and projects. The provision unit can also provide necessary materials and information according to the progress of projects. For example, the provision unit provides the latest research results related to meeting agendas. The provision unit can also provide industry trend information. The response unit responds to schedule changes and emergencies based on information provided by the delivery unit. For example, the response unit provides up-to-date information if meeting times are changed or urgent tasks arise. The response unit enables employees to respond quickly. The response unit can also provide information to respond to emergencies. For example, the response unit provides up-to-date information if meeting times are changed. The response unit can also provide up-to-date information if urgent tasks arise. The optimization unit learns and optimizes employee responses and feedback based on the information handled by the response unit. For example, the optimization unit learns in real time from employee responses and feedback to the daily newsletter provided and automatically optimizes the content and format for subsequent days. The optimization unit ensures that employees receive more useful information. The optimization unit can also learn from employee responses and feedback and optimize the content and format of the information provided. For example, if an employee shows high interest in specific information, the optimization unit prioritizes providing that information.The extraction unit extracts information using audio and video based on information optimized by the optimization unit. The extraction unit can, for example, automatically extract important information from a large amount of data using audio and video. The extraction unit enables employees to quickly grasp the information they need. The extraction unit can also extract and provide information to employees using audio and video. For example, the extraction unit can enable employees to quickly grasp the information they need through audio instructions and video explanations. As a result, the proactive information provision agent system according to this embodiment can monitor employees' calendars and tasks in real time, provide preparatory materials and trend information, respond to schedule changes and emergencies, learn and optimize employee responses and feedback, and extract information using audio and video.

[0070] The monitoring department monitors employees' calendars and tasks in real time. Specifically, it integrates with calendar applications and task management tools to periodically acquire data in order to gain a detailed understanding of employees' schedules and tasks. The monitoring department can collect and monitor information on meetings and tasks registered in employees' calendars in real time. For example, to understand the agenda of meetings and the progress of projects registered in employees' calendars, it analyzes the content of calendar entries and extracts detailed information on related tasks and meetings. The monitoring department also monitors the priority and progress of employees' tasks and collects necessary information. This allows the monitoring department to understand the status of employees' work in real time and build a foundation for providing necessary information at the appropriate time. Furthermore, the monitoring department can instantly detect changes in employees' calendars and tasks and reflect them throughout the system, enabling responses based on the latest information. For example, if a meeting time is changed or a new task is added, the monitoring department can immediately detect these changes and notify other departments. In this way, the monitoring department can play a crucial role in improving the work efficiency of employees.

[0071] The Information Provision Department provides preparatory materials and trend information based on information collected by the Monitoring Department. Specifically, it collects and organizes necessary information from publicly available online databases and specialized information sources to provide the latest research findings and industry trend information related to the meeting agenda. The Information Provision Department provides relevant materials and information in an appropriate format so that employees can quickly grasp the information needed for meetings and projects. For example, when providing the latest research findings related to the meeting agenda, it clearly presents summaries and key points so that employees can grasp important information in a short time. The Information Provision Department can also provide necessary materials and information according to the progress of the project. For example, it provides the information and materials necessary for the next step according to the progress of the project, supporting employees in smoothly carrying out their work. Furthermore, the Information Provision Department can also provide industry trend information. For example, it provides information to understand the latest industry trends and the movements of competitors, so that employees can respond quickly to market changes. In this way, the Information Provision Department can play an important role in enabling employees to quickly and accurately grasp the information they need, thereby improving work efficiency and results.

[0072] The response department responds to schedule changes and emergencies based on information provided by the service provider. Specifically, it integrates with employee calendars and task management tools to immediately reflect changes in order to provide the latest information when meeting times are changed or urgent tasks arise. The response department notifies employees of changes in real time and prompts them to take necessary actions so that they can respond quickly. For example, if a meeting time is changed, it notifies employees of details such as the new time, place, and agenda to support a smooth response. The response department can also provide information to respond to emergencies. For example, if an urgent task arises, it provides details such as the importance, priority, and response method of the task so that employees can respond quickly and appropriately. Furthermore, the response department can collect employee feedback and continuously improve the accuracy and effectiveness of its responses. For example, based on employee feedback, the response department reviews notification content and response methods to achieve more effective responses. In this way, the response department can support employees in responding quickly and appropriately to schedule changes and emergencies, playing a crucial role in improving work efficiency and results.

[0073] The optimization unit learns from and optimizes employee responses and feedback based on the information handled by the response unit. Specifically, it uses machine learning algorithms to learn in real time from employee responses and feedback to the newsletter provided every morning, and automatically optimizes the content and format for subsequent days. The optimization unit analyzes employee responses and feedback in detail to optimize the content and format of the information provided, so that it can provide employees with more useful information. For example, if an employee shows high interest in a particular piece of information, it will prioritize providing that information. It also analyzes how employees reacted to the information provided and optimizes the method and timing of information delivery. Furthermore, the optimization unit can customize the content and format of information delivery according to employees' work patterns and individual needs. For example, if a particular employee prefers to receive information at a specific time, it will deliver the information according to that time. In this way, the optimization unit can achieve the most effective information delivery for employees and play a crucial role in improving work efficiency and performance.

[0074] The extraction unit extracts information using audio and video based on information optimized by the optimization unit. Specifically, it utilizes natural language processing and image recognition technologies to automatically extract important information from large amounts of data using audio and video. The extraction unit analyzes audio and video, extracts important information, and provides it to employees so that they can quickly grasp the information they need. For example, it can extract important statements and topics from meeting recordings and provide them as summaries. It can also extract important scenes and information from video data and provide them in a visually easy-to-understand format. Furthermore, the extraction unit can extract information using audio and video and provide it to employees. For example, it can enable employees to quickly grasp the information they need through audio instructions and video explanations. In this way, the extraction unit can play an important role in enabling employees to quickly grasp important information from large amounts of data, thereby improving work efficiency and results. In addition, the extraction unit can customize the information extraction method and delivery format according to employee needs. For example, if a particular employee prefers audio instructions, the system will prioritize providing information via audio to that employee. This allows the extraction unit to provide employees with the most effective information, playing a crucial role in improving operational efficiency and enhancing results.

[0075] The monitoring department can gain a detailed understanding of employees' schedules and tasks. For example, the monitoring department can track meeting agendas and project progress registered in employees' calendars. The monitoring department can monitor the priorities and progress of employees' tasks and collect necessary information. For example, the monitoring department can track meeting agendas registered in employees' calendars in detail. The monitoring department can also track project progress in detail. The monitoring department can track the priorities of employees' tasks in detail and collect necessary information. This allows for more accurate monitoring by providing a detailed understanding of employees' schedules and tasks. Some or all of the above processes in the monitoring department may be performed using AI, for example, or not. For example, the monitoring department can input meeting agendas registered in employees' calendars into AI and have the AI ​​perform a detailed analysis of the meeting agendas.

[0076] The information delivery department can provide the latest research findings and industry trend information related to the meeting agenda. For example, the information delivery department can provide the latest research findings related to the meeting agenda. The information delivery department can enable employees to quickly grasp the information needed for meetings and projects. The information delivery department can also provide industry trend information. For example, the information delivery department can provide the latest research findings related to the meeting agenda. The information delivery department can also provide industry trend information. This allows employees to stay up-to-date by providing the latest research findings and industry trend information related to the meeting agenda. Some or all of the above processing in the information delivery department may be performed using AI, for example, or not using AI. For example, the information delivery department can input the latest research findings related to the meeting agenda into AI and have the AI ​​deliver the latest research findings.

[0077] The response unit can provide up-to-date information when meeting times are changed or urgent tasks arise. For example, the response unit provides up-to-date information when meeting times are changed. The response unit enables employees to respond quickly. The response unit can also provide up-to-date information when urgent tasks arise. For example, the response unit provides up-to-date information when meeting times are changed. The response unit can also provide up-to-date information when urgent tasks arise. This allows employees to respond quickly by providing up-to-date information when meeting times are changed or urgent tasks arise. Some or all of the above processing in the response unit may be performed using AI, for example, or not using AI. For example, the response unit can input the latest information into the AI ​​when meeting times are changed and have the AI ​​perform the task of providing the latest information.

[0078] The optimization unit can learn in real time the responses and feedback that employees provide to the newsletters they send out every morning, and automatically optimize the content and format for subsequent days. For example, the optimization unit can learn in real time the responses and feedback that employees provide to the newsletters they send out every morning, and automatically optimize the content and format for subsequent days. The optimization unit ensures that employees receive more useful information. Furthermore, the optimization unit can learn from employee responses and feedback and optimize the content and format of the information it provides. For example, if an employee shows high interest in a particular piece of information, the optimization unit will prioritize providing that information. This allows the optimization unit to learn in real time the responses and feedback that employees provide to the newsletters they send out every morning, and automatically optimize the content and format for subsequent days, thereby providing employees with more useful information. Some or all of the above processing in the optimization unit may be performed using AI, or not. For example, the optimization unit can input the responses and feedback that employees provide to the newsletters they send out every morning into the AI, and have the AI ​​perform the optimization of the content and format for subsequent days.

[0079] The extraction unit can automatically extract important information from a large amount of data using audio and video. For example, the extraction unit automatically extracts important information from a large amount of data using audio and video. The extraction unit enables employees to quickly grasp the information they need. Furthermore, the extraction unit can extract information using audio and video and provide it to employees. For example, the extraction unit enables employees to quickly grasp the information they need through audio instructions and video explanations. This allows employees to quickly grasp the information they need by automatically extracting important information from a large amount of data using audio and video. Some or all of the above-described processes in the extraction unit may be performed using AI, or not. For example, the extraction unit can input audio and video into an AI and have the AI ​​perform the extraction of important information.

[0080] The monitoring unit can estimate an employee's emotions and adjust the frequency of monitoring calendars and tasks based on the estimated emotions. For example, if an employee is stressed, the monitoring unit can increase monitoring of important tasks and meetings and reduce unnecessary notifications. If an employee is relaxed, the monitoring unit can maintain the normal monitoring frequency and provide only necessary information. Also, if an employee is busy, the monitoring unit can increase monitoring of high-priority tasks and meetings and decrease the monitoring frequency of other tasks. This reduces the burden on employees by adjusting the frequency of monitoring calendars and tasks based on their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, the monitoring unit can input employee emotion data into an AI and have the AI ​​adjust the frequency of monitoring calendars and tasks.

[0081] The monitoring department can analyze an employee's past schedule history and select the most appropriate monitoring method. For example, the monitoring department can prioritize monitoring meetings where the employee has frequently been late in the past. If an employee has forgotten an important task in the past, the monitoring department can intensify monitoring of that task. The monitoring department can also prioritize monitoring tasks that are concentrated in specific time periods based on the employee's past schedule history. This allows for more effective monitoring by analyzing the employee's past schedule history and selecting the most appropriate monitoring method. Some or all of the above processes in the monitoring department may be performed using AI, for example, or not. For example, the monitoring department can input the employee's past schedule history into AI and have the AI ​​select the most appropriate monitoring method.

[0082] The monitoring unit can filter calendars and tasks based on an employee's current projects and areas of interest. For example, the monitoring unit can prioritize monitoring tasks related to a project the employee is currently working on. It can also prioritize monitoring meetings and events related to an employee's areas of interest. Furthermore, the monitoring unit can reduce the frequency of monitoring other tasks so that employees can focus on their current projects. This allows for the provision of more relevant information by filtering based on an employee's current projects and areas of interest. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or not. For example, the monitoring unit can input information about an employee's current projects and areas of interest into an AI and have the AI ​​perform the filtering.

[0083] The monitoring unit can estimate an employee's emotions and determine the priority of tasks to monitor based on the estimated emotions. For example, if an employee is stressed, the monitoring unit will prioritize important tasks and lower the priority of other tasks. If an employee is relaxed, the monitoring unit will monitor tasks with normal priority. Also, if an employee is busy, the monitoring unit can prioritize the most important tasks and adjust the priority of other tasks. This reduces the burden on employees by determining the priority of tasks to monitor based on their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, the monitoring unit can input employee emotion data into an AI and have the AI ​​determine task priorities.

[0084] The monitoring unit can prioritize monitoring highly relevant tasks by considering employees' geographical location information when monitoring calendars and tasks. For example, if an employee is in a specific location, the monitoring unit will prioritize monitoring tasks related to that location. If an employee is traveling, the monitoring unit will prioritize monitoring tasks related to their destination. Furthermore, if an employee is working remotely, the monitoring unit can prioritize monitoring tasks performed at home. This allows for more effective monitoring by prioritizing highly relevant tasks while considering employees' geographical location information. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or not. For example, the monitoring unit can input employees' geographical location information into AI and have the AI ​​perform monitoring of highly relevant tasks.

[0085] The monitoring unit can analyze employees' social media activity and monitor related tasks when monitoring calendars and tasks. For example, the monitoring unit can prioritize monitoring tasks mentioned by employees on social media. The monitoring unit can prioritize monitoring tasks related to topics of interest from employees' social media activity. The monitoring unit can also prioritize monitoring tasks related to projects that employees follow on social media. This allows for the provision of more relevant information by analyzing employees' social media activity and monitoring related tasks. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or not. For example, the monitoring unit can input employees' social media activity into AI and have the AI ​​perform the monitoring of related tasks.

[0086] The information delivery unit can estimate an employee's emotions and adjust the way the information is presented based on the estimated emotions. For example, if an employee is stressed, the unit can provide simple, visually easy-to-understand information. If an employee is relaxed, the unit can provide detailed information. If an employee is busy, the unit can provide concise, to-the-point information. By adjusting the way the information is presented based on the employee's emotions, the information becomes easier for employees to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information delivery unit may be performed using AI or not. For example, the information delivery unit can input employee emotion data into an AI and have the AI ​​adjust the way the information is presented.

[0087] The information delivery unit can adjust the level of detail provided based on the importance of the information at the time of delivery. For example, the delivery unit can provide important information in detail to make it easy for employees to understand. It can also provide less important information concisely to reduce the burden on employees. Furthermore, the delivery unit can adjust the amount and format of the information provided according to its importance. This allows employees to prioritize understanding important information by adjusting the level of detail based on the importance of the information. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input the importance of the information into the AI ​​and have the AI ​​perform the adjustment of the level of detail of the delivery.

[0088] The information delivery unit can apply different delivery algorithms depending on the category of information at the time of delivery. For example, the delivery unit can organize and deliver meeting-related information by agenda item. For project-related information, it can deliver it according to its progress. Furthermore, the delivery unit can prioritize the delivery of the latest trend information. By applying different delivery algorithms depending on the category of information, employees can acquire information more efficiently. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not. For example, the delivery unit can input the information categories into the AI ​​and have the AI ​​execute the application of the delivery algorithm.

[0089] The information delivery unit can estimate an employee's emotions and adjust the length of the information it provides based on the estimated emotions. For example, if an employee is stressed, the unit can provide short, concise information. If an employee is relaxed, the unit can provide detailed information. If an employee is busy, the unit can provide only concise and important information. By adjusting the length of the information provided based on the employee's emotions, the information becomes easier for employees to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information delivery unit may be performed using AI or not. For example, the information delivery unit can input employee emotion data into an AI and have the AI ​​adjust the length of the information.

[0090] The information delivery department can determine the priority of information delivery based on the timing of information submission. For example, the department can prioritize the delivery of urgent information to enable employees to respond quickly. The department can also prioritize the delivery of information with an approaching deadline. Furthermore, the department can adjust the order in which information is delivered according to the submission timing. This allows employees to respond quickly by determining the priority of information delivery based on the submission timing. Some or all of the above processes in the information delivery department may be performed using AI, for example, or not. For example, the information delivery department can input the submission timing of information into the AI ​​and have the AI ​​determine the priority of information delivery.

[0091] The information delivery unit can adjust the order of delivery based on the relevance of the information. For example, the delivery unit can prioritize the delivery of highly relevant information to enable employees to acquire information efficiently. The delivery unit can also postpone the delivery of less relevant information. Furthermore, the delivery unit can adjust the order of delivery according to the relevance of the information. This allows employees to acquire information efficiently by adjusting the order of delivery based on the relevance of the information. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input the relevance of the information into the AI ​​and have the AI ​​perform the adjustment of the delivery order.

[0092] The response unit can estimate an employee's emotions and adjust its response based on the estimated emotions. For example, if an employee is stressed, the response unit will provide a quick and concise response. If an employee is relaxed, the response unit will provide a response that includes detailed explanations. If an employee is busy, the response unit will provide a concise response. By adjusting the response based on the employee's emotions, employees can perform their work more comfortably. 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 response unit may be performed using AI or not using AI. For example, the response unit can input employee emotion data into AI and have the AI ​​adjust the response method.

[0093] The response unit can analyze an employee's past response history to select the optimal response method when responding to an inquiry. For example, the response unit may prioritize response methods that the employee has preferred in the past. The response unit may select the most effective response method from the employee's past response history. Furthermore, the response unit can analyze an employee's past response history and propose the optimal response method. This allows for more effective responses by analyzing an employee's past response history and selecting the optimal response method. Some or all of the above processes in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input an employee's past response history into AI and have the AI ​​select the optimal response method.

[0094] The response unit can customize its response based on the employee's current situation. For example, if the employee is in a meeting, the response unit will respond in a way that does not disrupt the meeting. If the employee is traveling, the response unit will provide information relevant to their destination. Furthermore, if the employee is working remotely, the response unit can provide information relevant to tasks to be performed at home. This allows for a more appropriate response by customizing the response based on the employee's current situation. Some or all of the above processing in the response unit may be performed using AI, for example, or not. For example, the response unit can input the employee's current situation into the AI ​​and have the AI ​​customize the response.

[0095] The response unit can estimate an employee's emotions and determine the priority of responses based on the estimated emotions. For example, if an employee is stressed, the response unit will prioritize important responses. If an employee is relaxed, the response unit will respond with normal priorities. Also, if an employee is busy, the response unit can prioritize the most important responses. This reduces the burden on employees by determining the priority of responses based on their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the response unit may be performed using AI, for example, or not using AI. For example, the response unit can input employee emotion data into AI and have the AI ​​determine the priority of responses.

[0096] The response unit can select the optimal response method by considering the employee's geographical location information when responding to an inquiry. For example, if the employee is in a specific location, the response unit can provide information related to that location. If the employee is on the move, the response unit can provide information related to the destination. Furthermore, if the employee is working remotely, the response unit can provide information related to tasks performed at home. This allows for more effective responses by selecting the optimal response method by considering the employee's geographical location information. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input the employee's geographical location information into AI and have the AI ​​select the optimal response method.

[0097] The response unit can analyze an employee's social media activity and propose a course of action when responding to an issue. For example, the response unit can provide information related to tasks mentioned by the employee on social media. The response unit can provide information related to topics of interest from the employee's social media activity. The response unit can also provide information related to projects that the employee follows on social media. This allows the response unit to provide more relevant information by analyzing the employee's social media activity and proposing a course of action. Some or all of the above processing in the response unit may be performed using AI, for example, or not using AI. For example, the response unit can input the employee's social media activity into AI and have the AI ​​propose a course of action.

[0098] The optimization unit can estimate employees' emotions and adjust the optimization method based on the estimated emotions. For example, if an employee is stressed, the optimization unit provides a simple and visually easy-to-understand optimization method. If an employee is relaxed, the optimization unit provides a detailed optimization method. Furthermore, if an employee is busy, the optimization unit can provide a concise optimization method that gets straight to the point. By adjusting the optimization method based on employees' emotions, employees can perform their work more comfortably. 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 optimization unit may be performed using AI, for example, or not using AI. For example, the optimization unit can input employee emotion data into AI and have the AI ​​perform the adjustment of the optimization method.

[0099] The optimization unit can analyze employees' past responses and feedback during optimization to select the optimal optimization method. For example, the optimization unit may prioritize selecting optimization methods that employees have preferred in the past. The optimization unit selects the most effective optimization method based on employees' past responses and feedback. The optimization unit can also analyze employees' past responses and feedback and propose the optimal optimization method. This allows for more effective optimization by analyzing employees' past responses and feedback to select the optimal optimization method. Some or all of the above processes in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input employees' past responses and feedback into AI and have the AI ​​select the optimal optimization method.

[0100] The optimization unit can customize the optimization methods based on the employee's current situation during optimization. For example, if an employee is in a meeting, the optimization unit will optimize in a way that does not disrupt the meeting. If an employee is traveling, the optimization unit will provide information related to their destination. Furthermore, if an employee is working remotely, the optimization unit can provide information related to tasks to be performed at home. This allows for more appropriate optimization by customizing the optimization methods based on the employee's current situation. Some or all of the above-described processes in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input the employee's current situation into the AI ​​and have the AI ​​perform the customization of the optimization methods.

[0101] The optimization unit can estimate employees' emotions and determine optimization priorities based on those estimated emotions. For example, if an employee is stressed, the optimization unit will prioritize important optimizations. If an employee is relaxed, the optimization unit will perform optimizations with normal priorities. Furthermore, if an employee is busy, the optimization unit can prioritize the most important optimizations. This reduces the burden on employees by determining optimization priorities based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the optimization unit may be performed using AI or not. For example, the optimization unit can input employee emotion data into an AI and have the AI ​​determine the optimization priorities.

[0102] The optimization unit can select the optimal optimization method by considering the geographical location information of employees during the optimization process. For example, if an employee is in a specific location, the optimization unit can provide information related to that location. If an employee is on the move, the optimization unit can provide information related to their destination. Furthermore, if an employee is working remotely, the optimization unit can provide information related to tasks performed at home. By selecting the optimal optimization method while considering the geographical location information of employees, more effective optimization becomes possible. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input the geographical location information of employees into AI and have the AI ​​select the optimal optimization method.

[0103] The optimization unit can analyze employees' social media activity and propose optimization methods during the optimization process. For example, the optimization unit can provide information related to tasks mentioned by employees on social media. The optimization unit can provide information related to topics of interest from employees' social media activity. The optimization unit can also provide information related to projects that employees follow on social media. This allows for the provision of more relevant information by analyzing employees' social media activity and proposing optimization methods. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input employees' social media activity into AI and have the AI ​​execute the proposal of optimization methods.

[0104] The extraction unit can estimate an employee's emotions and determine the priority of information to extract based on the estimated emotions. For example, if an employee is stressed, the extraction unit will prioritize extracting important information. If an employee is relaxed, the extraction unit will extract information with normal priority. Also, if an employee is busy, the extraction unit can prioritize extracting the most important information. This allows employees to quickly grasp the information they need by determining the priority of information to extract based on their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the extraction unit may be performed using AI, for example, or not using AI. For example, the extraction unit can input employee emotion data into an AI and have the AI ​​perform the determination of information priority.

[0105] The extraction unit can improve the accuracy of extraction by considering the interrelationships of information during the extraction process. For example, the extraction unit can group related information and extract it. The extraction unit analyzes the interrelationships of information and extracts the most relevant information. Furthermore, the extraction unit can improve the accuracy of extraction by considering the interrelationships of information. By improving the accuracy of extraction by considering the interrelationships of information, more reliable information can be provided. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input the interrelationships of information into AI and have AI perform the task of improving the accuracy of extraction.

[0106] The extraction unit can perform extraction while considering the attribute information of the information submitter. For example, the extraction unit can extract highly reliable information by considering the expertise of the information submitter. The extraction unit can extract important information by considering the position and experience of the information submitter. Furthermore, the extraction unit can analyze the attribute information of the information submitter and extract the most suitable information. As a result, by performing extraction while considering the attribute information of the information submitter, more reliable information can be provided. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without using AI. For example, the extraction unit can input the attribute information of the information submitter into AI and have the AI ​​perform the extraction.

[0107] The extraction unit can estimate an employee's emotions and adjust how the extracted information is displayed based on the estimated emotions. For example, if an employee is stressed, the extraction unit provides a simple and visually easy-to-understand display. If an employee is relaxed, the extraction unit provides detailed information. If an employee is busy, the extraction unit can provide concise information that gets straight to the point. By adjusting how the extracted information is displayed based on the employee's emotions, it becomes easier for employees to understand the information. 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 extraction unit may be performed using AI or not. For example, the extraction unit can input employee emotion data into an AI and have the AI ​​adjust how the information is displayed.

[0108] The extraction unit can perform extraction while considering the geographical distribution of information. For example, the extraction unit can prioritize the extraction of information related to a specific region. The extraction unit extracts the most relevant information while considering the geographical distribution. Furthermore, the extraction unit can analyze the geographical distribution and extract the most relevant information. As a result, by performing extraction while considering the geographical distribution of information, more relevant information can be provided. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input the geographical distribution of information into AI and have the AI ​​perform the extraction.

[0109] The extraction unit can improve the accuracy of its extraction by referring to relevant literature during the extraction process. For example, the extraction unit extracts highly reliable information by referring to relevant literature. The extraction unit analyzes the relevant literature and extracts the most relevant information. Furthermore, the extraction unit can improve the accuracy of its extraction by considering the relevant literature. This allows for the provision of more reliable information by improving the accuracy of extraction by referring to relevant literature. Some or all of the above-described processes in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input relevant literature into AI and have AI perform the task of improving the accuracy of its extraction.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The monitoring unit can not only monitor employees' calendars and tasks in real time, but also their health status. For example, it can monitor employees' heart rate and sleep patterns, and provide appropriate alerts if there are any abnormalities in their health. The provision unit can provide health advice and resources based on employees' health status. For example, if an employee is under high stress, it can provide guidance for relaxation and suggest stretches. The response unit can adjust the workload according to the employee's health status. For example, if an employee's health is deteriorating, it can change the priority of tasks and reduce the workload. The optimization unit can optimize the information and task content provided based on the employee's health status. For example, if an employee is in good health, it can provide challenging tasks, and if their health is poor, it can provide easier tasks. The extraction unit can extract and provide necessary information based on the employee's health status. For example, if an employee's health is deteriorating, it can prioritize providing health-related information. This enables the provision of information that takes employee health status into consideration, thereby promoting employee health maintenance and improving work efficiency.

[0112] The monitoring unit can monitor not only employee calendars and tasks, but also employee device usage. For example, it can provide alerts prompting employees to take breaks if they are using their devices for extended periods. The provision unit can provide advice and resources on digital well-being based on employee device usage. For example, it can suggest exercises to reduce eye strain or propose digital detoxes. The response unit can adjust the workload based on employee device usage. For example, if an employee is using their device for extended periods, it can change the priority of tasks to reduce the workload. The optimization unit can optimize the information and tasks provided based on employee device usage. For example, if an employee is using their device frequently, it can provide tasks that can be done offline. The extraction unit can extract and provide necessary information based on employee device usage. For example, if an employee is using their device frequently, it can prioritize providing information on digital well-being. This enables the provision of information that takes employee device usage into consideration, thereby improving digital well-being.

[0113] The monitoring unit can not only monitor employees' calendars and tasks but also analyze their communication patterns. For example, it can monitor who employees frequently communicate with and how often, and provide necessary information. The provision unit can provide advice and resources on improving communication efficiency based on employees' communication patterns. For example, it can suggest guides for effective communication and how to use communication tools. The response unit can adjust the workload according to employees' communication patterns. For example, if there is a lot of communication, it can change the priority of tasks to reduce the workload. The optimization unit can optimize the information and task content provided based on employees' communication patterns. For example, if there is little communication, it can provide tasks for team building. The extraction unit can extract and provide necessary information based on employees' communication patterns. For example, if there is a lot of communication, it can prioritize providing information on improving communication efficiency. This makes it possible to provide information that takes employees' communication patterns into consideration, thereby improving communication efficiency.

[0114] The monitoring unit can not only monitor employees' calendars and tasks, but also analyze their learning history. For example, it can monitor the training and learning content employees have previously attended and provide necessary information. The provision unit can provide advice and resources on improving learning efficiency based on employees' learning history. For example, it can suggest effective learning methods and ways to utilize learning tools. The response unit can adjust the workload according to employees' learning history. For example, if there is a lot of learning to do, it can change the priority of tasks to reduce the workload. The optimization unit can optimize the information and task content provided based on employees' learning history. For example, if there is little learning to do, it can provide tasks that offer learning opportunities. The extraction unit can extract and provide necessary information based on employees' learning history. For example, if there is a lot of learning to do, it can prioritize providing information on improving learning efficiency. This makes it possible to provide information that takes employees' learning history into consideration, thereby improving learning efficiency.

[0115] The monitoring unit can not only monitor employees' calendars and tasks, but also analyze their hobbies and interests. For example, it can monitor events and tasks related to employees' hobbies and interests and provide necessary information. The provisioning unit can provide relevant advice and resources based on employees' hobbies and interests. For example, it can suggest information on hobby-related events or content that will pique their interest. The response unit can adjust the workload according to employees' hobbies and interests. For example, if there is a hobby-related event, it can change the priority of tasks to make it easier for employees to participate. The optimization unit can optimize the information and task content provided based on employees' hobbies and interests. For example, it can provide hobby-related tasks to improve motivation. The extraction unit can extract and provide necessary information based on employees' hobbies and interests. For example, it can prioritize providing hobby-related information. This makes it possible to provide information that takes employees' hobbies and interests into consideration, thereby improving motivation.

[0116] The monitoring unit can estimate employees' emotions and adjust the frequency of monitoring calendars and tasks based on the estimated emotions. For example, if an employee is stressed, monitoring of important tasks and meetings can be increased, and unnecessary notifications can be reduced. If an employee is relaxed, the normal monitoring frequency can be maintained, and only necessary information can be provided. Also, if an employee is busy, monitoring of high-priority tasks and meetings can be increased, and the monitoring frequency of other tasks can be decreased. In this way, the burden on employees can be reduced by adjusting the frequency of monitoring calendars and tasks based on their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI or not using AI. For example, the monitoring unit can input employee emotion data into AI and have the AI ​​adjust the frequency of monitoring calendars and tasks.

[0117] The information delivery unit can estimate an employee's emotions and adjust the way the information is presented based on the estimated emotions. For example, if an employee is stressed, it can provide simple, visually easy-to-understand information. If an employee is relaxed, it can provide detailed information. If an employee is busy, it can provide concise information that gets straight to the point. By adjusting the way the information is presented based on the employee's emotions, it makes the information easier for employees to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information delivery unit may be performed using AI or not. For example, the information delivery unit can input employee emotion data into an AI and have the AI ​​adjust the way the information is presented.

[0118] The response unit can estimate an employee's emotions and adjust its response based on the estimated emotions. For example, if an employee is stressed, it will respond quickly and concisely. If an employee is relaxed, it will respond with detailed explanations. If an employee is busy, it will respond to the point. By adjusting the response based on the employee's emotions, employees can perform their work more comfortably. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the response unit may be performed using AI or not. For example, the response unit can input employee emotion data into an AI and have the AI ​​adjust the response method.

[0119] The optimization unit can estimate employees' emotions and adjust the optimization method based on the estimated emotions. For example, if an employee is stressed, it can provide a simple and visually easy-to-understand optimization method. If an employee is relaxed, it can provide a detailed optimization method. If an employee is busy, it can provide a concise optimization method that gets straight to the point. By adjusting the optimization method based on employees' emotions, employees can perform their work more comfortably. 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 optimization unit may be performed using AI, or not using AI. For example, the optimization unit can input employee emotion data into AI and have the AI ​​perform the adjustment of the optimization method.

[0120] The extraction unit can estimate an employee's emotions and determine the priority of information to extract based on the estimated emotions. For example, if an employee is stressed, important information can be extracted first. If an employee is relaxed, information can be extracted with normal priority. Also, if an employee is busy, the most important information can be extracted first. This allows employees to quickly grasp the information they need by determining the priority of information to extract based on their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the extraction unit may be performed using AI or not using AI. For example, the extraction unit can input employee emotion data into an AI and have the AI ​​perform the determination of information priority.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The monitoring department monitors employees' calendars and tasks in real time. For example, it gains a detailed understanding of employees' schedules and tasks, and collects information on meetings and tasks. This allows for monitoring of meeting agendas, project progress, and task priorities and progress. Step 2: The provisioning department provides preparatory materials and trend information based on the information collected by the monitoring department. For example, they provide the latest research findings and industry trend information related to the meeting agenda, enabling employees to quickly grasp the information needed for the meeting or project. Step 3: The response team responds to schedule changes and emergencies based on the information provided by the delivery team. For example, they provide up-to-date information if meeting times are changed or urgent tasks arise, enabling employees to respond quickly. Step 4: The optimization unit learns from and optimizes employee responses and feedback based on the information handled by the response unit. For example, it learns in real time from employee responses and feedback to the information provided and automatically optimizes the content and format for the following days. Step 5: The extraction unit extracts information using audio and video based on the information optimized by the optimization unit. For example, it automatically extracts important information from a large amount of data using audio and video, allowing employees to quickly grasp the information they need.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the monitoring unit, provision unit, response unit, optimization unit, and extraction unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the monitoring unit is implemented by the control unit 46A of the smart device 14 and monitors employees' calendars and tasks in real time. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides preparatory materials and trend information based on the collected information. The response unit is implemented by the control unit 46A of the smart device 14 and responds to schedule changes and emergencies. The optimization unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns and optimizes based on employee reactions and feedback. The extraction unit is implemented by the control unit 46A of the smart device 14 and extracts information using voice and video. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the monitoring unit, provision unit, response unit, optimization unit, and extraction unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the monitoring unit is implemented by the control unit 46A of the smart glasses 214 and monitors employees' calendars and tasks in real time. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides preparatory materials and trend information based on the collected information. The response unit is implemented by the control unit 46A of the smart glasses 214 and responds to schedule changes and emergencies. The optimization unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns and optimizes based on employee reactions and feedback. The extraction unit is implemented by the control unit 46A of the smart glasses 214 and extracts information using voice and video. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] Each of the multiple elements described above, including the monitoring unit, provision unit, response unit, optimization unit, and extraction unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the monitoring unit is implemented by the control unit 46A of the headset terminal 314 and monitors employees' calendars and tasks in real time. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides preparatory materials and trend information based on the collected information. The response unit is implemented by the control unit 46A of the headset terminal 314 and responds to schedule changes and emergencies. The optimization unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns and optimizes based on employee reactions and feedback. The extraction unit is implemented by the control unit 46A of the headset terminal 314 and extracts information using voice and video. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] Each of the multiple elements described above, including the monitoring unit, provision unit, response unit, optimization unit, and extraction unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the monitoring unit is implemented by the control unit 46A of the robot 414 and monitors employees' calendars and tasks in real time. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides preparatory materials and trend information based on the collected information. The response unit is implemented by the control unit 46A of the robot 414 and responds to schedule changes and emergencies. The optimization unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns and optimizes employee reactions and feedback. The extraction unit is implemented by the control unit 46A of the robot 414 and extracts information using voice and video. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] (Note 1) The monitoring department monitors employees' calendars and tasks in real time, A provision unit provides preparatory materials and trend information based on the information collected by the aforementioned monitoring unit, Based on the information provided by the aforementioned provisioning unit, a response unit is provided to respond to changes in the schedule or emergencies, An optimization unit learns and optimizes employee responses and feedback based on the information handled by the aforementioned response unit, The system includes an extraction unit that extracts information using audio and video based on the information optimized by the optimization unit. A system characterized by the following features. (Note 2) The aforementioned monitoring unit, Understand employees' schedules and tasks in detail. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, This provides the latest research findings and industry trend information related to the conference agenda. The system described in Appendix 1, characterized by the features described herein. (Note 4) The corresponding part is, Provide updates in case of changes to meeting times or urgent tasks. The system described in Appendix 1, characterized by the features described herein. (Note 5) The optimization unit, The system learns in real time from employee responses and feedback on the newsletters they provide every morning, and automatically optimizes the content and format for subsequent days. The system described in Appendix 1, characterized by the features described herein. (Note 6) The extraction unit is Automatically extract important information from large amounts of data using audio and video. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned monitoring unit, The system estimates employee sentiment and adjusts the frequency of monitoring calendars and tasks based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned monitoring unit, Analyze employees' past schedule history and select the optimal monitoring method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned monitoring unit, When monitoring calendars and tasks, filter based on employees' current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned monitoring unit, Estimate employee sentiment and prioritize monitoring tasks based on the estimated employee sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned monitoring unit, When monitoring calendars and tasks, prioritize monitoring highly relevant tasks by considering employees' geographical locations. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned monitoring unit, When monitoring calendars and tasks, analyze employee social media activity and monitor related tasks. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned supply unit is, We estimate employees' emotions and adjust the way we present information based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned supply unit is, When providing information, adjust the level of detail based on its importance. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned supply unit is, When providing information, different delivery algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned supply unit is, The system estimates employee sentiment and adjusts the length of information provided based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned supply unit is, When providing information, we will determine the priority of provision based on when the information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, When providing information, the order of provision will be adjusted based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The corresponding part is, Estimate employees' emotions and adjust responses based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The corresponding part is, When responding to an issue, the company analyzes the employee's past response history to select the most appropriate response method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The corresponding part is, When responding, customize the response method based on the employee's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The corresponding part is, The system estimates employee emotions and determines the priority of responses based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The corresponding part is, When responding to an issue, the most appropriate response method will be selected, taking into account the geographical location of the employee. The system described in Appendix 1, characterized by the features described herein. (Note 24) The corresponding part is, When responding to an issue, we analyze the employee's social media activity and propose appropriate response methods. The system described in Appendix 1, characterized by the features described herein. (Note 25) The optimization unit, We estimate employee sentiment and adjust the optimization method based on the estimated employee sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 26) The optimization unit, During optimization, we analyze past employee responses and feedback to select the most suitable optimization method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The optimization unit, During optimization, customize the optimization methods based on the current status of employees. The system described in Appendix 1, characterized by the features described herein. (Note 28) The optimization unit, The system estimates employee sentiment and determines optimization priorities based on the estimated employee sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 29) The optimization unit, During optimization, the optimal optimization method is selected by considering the geographical location information of employees. The system described in Appendix 1, characterized by the features described herein. (Note 30) The optimization unit, During the optimization process, we analyze employees' social media activity and propose optimization strategies. The system described in Appendix 1, characterized by the features described herein. (Note 31) The extraction unit is The system estimates employee sentiment and prioritizes the information to extract based on the estimated employee sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 32) The extraction unit is During extraction, the interrelationships between pieces of information are taken into consideration to improve the accuracy of the extraction. The system described in Appendix 1, characterized by the features described herein. (Note 33) The extraction unit is During extraction, the attribute information of the information submitter is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 34) The extraction unit is We estimate employee sentiment and adjust how information extracted based on that estimated sentiment is displayed. The system described in Appendix 1, characterized by the features described herein. (Note 35) The extraction unit is During extraction, the geographical distribution of the information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 36) The extraction unit is During extraction, we improve the accuracy of the extraction by referring to relevant literature. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0195] 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 monitoring department monitors employees' calendars and tasks in real time, A provision unit provides preparatory materials and trend information based on the information collected by the aforementioned monitoring unit, Based on the information provided by the aforementioned provisioning unit, a response unit is established to respond to changes in the schedule or emergencies. An optimization unit learns and optimizes employee responses and feedback based on the information handled by the aforementioned response unit, The system includes an extraction unit that extracts information using audio and video based on the information optimized by the optimization unit. A system characterized by the following features.

2. The aforementioned monitoring unit, Understand employees' schedules and tasks in detail. The system according to feature 1.

3. The aforementioned supply unit is, This provides the latest research findings and industry trend information related to the conference agenda. The system according to feature 1.

4. The corresponding part is, Provide updates in case of changes to meeting times or urgent tasks. The system according to feature 1.

5. The optimization unit, The system learns in real time from employee responses and feedback on the newsletters they provide every morning, and automatically optimizes the content and format for subsequent days. The system according to feature 1.

6. The extraction unit is Automatically extract important information from large amounts of data using audio and video. The system according to feature 1.

7. The aforementioned monitoring unit, The system estimates employee sentiment and adjusts the frequency of monitoring calendars and tasks based on the estimated sentiment. The system according to feature 1.

8. The aforementioned monitoring unit, Analyze employees' past schedule history and select the optimal monitoring method. The system according to feature 1.

9. The aforementioned monitoring unit, When monitoring calendars and tasks, filter based on employees' current projects and areas of interest. The system according to feature 1.

10. The aforementioned monitoring unit, Estimate employee sentiment and prioritize monitoring tasks based on the estimated employee sentiment. The system according to feature 1.

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