system

Abstract

The system according to this embodiment aims to efficiently manage the progress and schedules of team members in a remote work environment and to propose optimal task assignments and meeting timings. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, and a proposal unit. The collection unit collects the progress status and schedule of each member. The analysis unit analyzes the data collected by the collection unit. The proposal unit proposes the optimal task assignment and meeting timing based on the evaluation results obtained by the analysis unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to efficiently manage the progress status and schedule of team members in a remote work environment and propose an optimal task allocation and meeting timing.

[0005] The system according to the embodiment aims to efficiently manage the progress status and schedule of team members in a remote work environment and propose an optimal task allocation and meeting timing.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, and a proposal unit. The data collection unit collects the progress and schedule of each member. The analysis unit analyzes the data collected by the data collection unit. The proposal unit proposes the optimal task assignment and meeting timing based on the evaluation results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently manage the progress and schedules of team members in a remote work environment and propose optimal task assignments and meeting timings. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The AI ​​agent system according to an embodiment of the present invention is a system that analyzes the progress and efficiency of team members' work in a remote work environment and automatically proposes optimal task assignments and meeting timings. The AI ​​agent system collects each member's progress and schedule, analyzes the collected data, and proposes optimal task assignments and meeting timings. This mechanism supports improved productivity in a remote work environment. For example, the AI ​​agent system collects each member's progress and schedule. In this process, it obtains data from task management tools and calendar apps used by each member. For example, it collects information such as the completion status of tasks and scheduled meetings. This allows the system to understand the current status of each member. Next, the AI ​​agent system analyzes the collected data. The AI ​​analyzes the collected data to evaluate the progress and efficiency of each member's tasks. For example, it evaluates the speed of task completion and meeting attendance rates. This allows the system to quantitatively evaluate the efficiency and progress of each member. Furthermore, based on the evaluation results, the AI ​​agent system proposes optimal task assignments and meeting timings. The AI ​​optimizes task assignments and meeting timings, taking into account the efficiency and progress of each member. For example, tasks can be reduced for members who are behind schedule and increased for highly efficient members. Meeting times can also be suggested to optimize the schedule, taking each member's availability into consideration. This improves the overall productivity of the team. The AI ​​agent system streamlines communication and task management in remote work environments, improving work efficiency. For instance, the visualization of task progress facilitates smoother communication among team members. Furthermore, the suggestion of optimal task assignments and meeting times reduces wasted time and allows for more efficient work. The objective of this invention is to maximize the overall productivity and efficiency of the team, even in a remote work environment, and to provide employees with a comfortable working environment. This overcomes the challenges of communication and task management inherent in remote work, enabling a more efficient remote work environment.This allows the AI ​​agent system to support increased productivity in remote work environments.

[0029] The AI ​​agent system according to this embodiment comprises a data collection unit, an analysis unit, and a proposal unit. The data collection unit collects the progress and schedule of each member. The data collection unit obtains data from, for example, task management tools or calendar applications used by each member. The data collection unit collects, for example, information on task completion status and scheduled meetings. This allows the data collection unit to understand the current status of each member. The analysis unit analyzes the data collected by the data collection unit. The analysis unit analyzes the collected data to evaluate, for example, the progress and efficiency of each member's tasks. The analysis unit evaluates, for example, the speed of task completion and meeting attendance rates. This allows the analysis unit to quantitatively evaluate the efficiency and progress of each member. The proposal unit proposes optimal task assignments and meeting timings based on the evaluation results obtained by the analysis unit. The proposal unit optimizes task assignments and meeting timings, for example, by considering the efficiency and progress of each member. The proposal unit reduces the tasks of members who are behind schedule and increases the tasks of highly efficient members. The proposal team also suggests the optimal meeting time, taking into account each member's schedule. This allows the AI ​​agent system, according to the embodiment, to support improved productivity in a remote work environment.

[0030] The data collection department collects the progress and schedules of each member. Specifically, it obtains data from the task management tools and calendar apps used by each member. For example, from task management tools, it collects information such as the progress of tasks each member is responsible for, the start and end dates of tasks, and the priority of tasks. From calendar apps, it collects information such as information on meetings and events each member is scheduled to attend, the start and end times of meetings, and the list of meeting participants. By regularly acquiring this data and updating it in real time, the data collection department can accurately grasp the current status of each member. Furthermore, the data collection department can also collect data from each member's communication tools. For example, by analyzing the frequency and content of each member's communication from chat tools and email, it can understand the communication situation within the team. This allows the data collection department to understand not only the progress and schedule of each member's tasks, but also the communication situation of the entire team. The data collection department centrally manages this data and can link it with other departments and systems as needed. For example, the collected data is stored on a cloud server and made accessible to the analysis department and the proposal department. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The Analysis Department analyzes the data collected by the Data Collection Department. Specifically, it analyzes the collected data to evaluate the progress and efficiency of each member's tasks. For example, it evaluates the speed of task completion, meeting attendance rates, and progress based on task priority. The Analysis Department uses AI to analyze this data and quantitatively evaluate the efficiency and progress of each member. The AI ​​uses machine learning algorithms to learn each member's work patterns and efficiency trends from past data and evaluates each member's performance by comparing them with current data. For example, to evaluate the speed of task completion, it compares the completion time of past tasks with the completion time of current tasks to calculate each member's work efficiency. Also, to evaluate meeting attendance rates, it compares past meeting attendance data with current meeting attendance data to evaluate each member's participation in meetings. Furthermore, the Analysis Department can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. For example, if a particular member's task progress suddenly slows down or the meeting attendance rate suddenly drops, it will be detected as an anomaly and the manager will be notified. This allows the analysis department to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and security of the entire system.

[0032] The proposal department proposes optimal task assignments and meeting timings based on evaluation results obtained by the analysis department. Specifically, it optimizes task assignments and meeting timings considering the efficiency and progress of each member. For example, it reduces tasks for members who are behind schedule and increases tasks for highly efficient members. It also proposes optimal meeting times considering each member's schedule. The proposal department uses AI to make these proposals. The AI ​​analyzes each member's schedule and task progress to calculate optimal task assignments and meeting timings. For example, it analyzes each member's schedule and proposes the optimal meeting time that everyone can attend. It also analyzes each member's task progress and proposes optimal task assignments based on task priority and urgency. Furthermore, the proposal department can collect feedback from each member and continuously improve the accuracy and effectiveness of its proposals. For example, it reviews and improves proposals based on feedback from each member regarding the proposed task assignments and meeting timings. In addition, the proposal department can reliably transmit information using multiple communication methods. For example, it uses not only smartphone notifications but also voice calls and emails to ensure that important information is delivered reliably. This allows the proposal department to provide each member with the most suitable proposal quickly and reliably, supporting improved productivity in a remote work environment.

[0033] The data collection unit can acquire data from task management tools and calendar apps used by each member. For example, the data collection unit can acquire task completion status from the task management tools used by each member. The data collection unit can also acquire information about scheduled meetings from the calendar apps used by each member. This allows the data collection unit to accurately understand the progress and schedule of each member. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input data acquired from task management tools and calendar apps into a generating AI and have the generating AI perform data analysis.

[0034] The analysis unit can analyze the collected data and evaluate the progress and efficiency of each member's tasks. For example, the analysis unit can evaluate the completion rate and adherence to deadlines of each member's tasks based on the collected data. The analysis unit can also evaluate the processing speed and resource utilization efficiency of each member's tasks. For example, the analysis unit can evaluate each member's progress based on the completion rate of tasks. The analysis unit can also evaluate each member's efficiency based on adherence to deadlines. This allows the analysis unit to quantitatively evaluate the efficiency and progress of each member. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform the data analysis.

[0035] Based on the evaluation results, the proposal department can propose reducing the tasks of members who are behind schedule and increasing the tasks of highly efficient members. For example, the proposal department can propose reducing the tasks of members who are behind schedule. The proposal department can also propose increasing the tasks of highly efficient members. For example, the proposal department can propose reducing the number of tasks of members who are behind schedule. The proposal department can also propose increasing the number of tasks of highly efficient members. This allows the proposal department to allocate tasks according to the efficiency of each member. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input the evaluation results into a generating AI and have the generating AI execute task allocation proposals.

[0036] The proposal department can suggest the optimal timing for meetings, taking into account each member's schedule. For example, the proposal department can suggest the optimal timing for meetings based on each member's schedule. The proposal department can also suggest the optimal timing for meetings, taking into account each member's availability and the progress of the project. For example, the proposal department can suggest the optimal timing for meetings based on each member's availability. The proposal department can also suggest the optimal timing for meetings based on the progress of the project. By optimizing meeting timing, the proposal department can reduce wasted time and proceed with work efficiently. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input each member's schedule data into a generating AI and have the generating AI generate suggestions for the optimal timing for meetings.

[0037] The data collection unit can analyze each member's past data collection history and select the optimal collection method. For example, the data collection unit can select the most efficient collection method based on each member's past data collection history. The data collection unit can also analyze each member's past data collection history and optimize the collection frequency. For example, the data collection unit can adjust the collection timing based on each member's past data collection history. This enables efficient data collection by allowing the data collection unit to select the optimal collection method based on past data collection history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past data collection history into a generating AI and have the generating AI select the optimal collection method.

[0038] The data collection unit can filter data based on each member's current projects and areas of interest during data collection. For example, the data collection unit can collect only data related to each member's current projects. The data collection unit can also prioritize the collection of highly relevant data based on each member's areas of interest. For example, the data collection unit can filter and collect data related to each member's current tasks. This allows the data collection unit to prioritize the collection of data related to each member's current projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input data on each member's projects and areas of interest into a generating AI and have the generating AI perform the filtering.

[0039] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of each member during data collection. For example, the data collection unit prioritizes the collection of highly relevant data based on each member's current location. The data collection unit can also select the optimal data collection method by considering the geographical location information of each member. For example, the data collection unit adjusts the collection timing based on each member's geographical location information. In this way, the data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of each member. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input each member's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0040] The data collection unit can analyze each member's social media activity and collect relevant data during data collection. For example, the data collection unit can analyze each member's social media activity and prioritize the collection of relevant data. The data collection unit can also select the optimal data collection method based on each member's social media activity. For example, the data collection unit can adjust the timing of data collection based on each member's social media activity. This allows the data collection unit to prioritize the collection of relevant data by analyzing each member's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input each member's social media activity data into a generating AI and have the generating AI collect the relevant data.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of each member's task during data analysis. For example, the analysis unit performs a detailed analysis based on the importance of each member's task. The analysis unit can also determine the priority of the analysis according to the importance of each member's task. For example, the analysis unit adjusts the level of detail of the analysis considering the importance of each member's task. This enables efficient data analysis by allowing the analysis unit to adjust the level of detail according to the importance of each member's task. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance data of each member's task into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0042] The analysis department can apply different analysis algorithms to data analysis depending on each member's role and job responsibilities. For example, the analysis department can select the optimal analysis algorithm according to each member's role. The analysis department can also apply different analysis algorithms based on each member's job responsibilities. For example, the analysis department can adjust the analysis algorithm considering each member's role and job responsibilities. This enables efficient data analysis by applying the optimal analysis algorithm according to each member's role and job responsibilities. Some or all of the above processes in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can input data on each member's role and job responsibilities into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0043] The analysis unit can determine the priority of analysis based on each member's submission timing during data analysis. For example, the analysis unit determines the priority of analysis based on each member's submission timing. The analysis unit can also adjust the level of detail of the analysis, taking into account each member's submission timing. For example, the analysis unit adjusts the timing of the analysis according to each member's submission timing. This enables efficient data analysis by allowing the analysis unit to determine the priority of analysis based on each member's submission timing. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input each member's submission timing data into a generating AI and have the generating AI determine the priority of analysis.

[0044] The analysis unit can adjust the order of analysis based on the relationships between each member during data analysis. For example, the analysis unit adjusts the order of analysis based on the relationships between each member. The analysis unit can also determine the priority of analysis by considering the relationships between each member. For example, the analysis unit adjusts the level of detail of analysis according to the relationships between each member. This enables efficient data analysis by allowing the analysis unit to adjust the order of analysis based on the relationships between each member. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relationship data for each member into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0045] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0046] The data collection unit can collect health data from each member and adjust task assignments based on their health status. For example, the data collection unit can acquire heart rate and sleep data from each member's wearable device. The data collection unit can also collect stress level and exercise data from each member's health app. This allows the data collection unit to understand each member's health status and adjust the workload accordingly.

[0047] The analytics department can predict future performance based on each member's past performance data. For example, it can predict each member's future performance based on past task completion speed and meeting attendance rates. The analytics department can also analyze the success and failure rates of past projects to assess the risks of future projects. This allows the analytics department to predict future performance and manage risks.

[0048] The proposal team can propose optimal task assignments by considering each member's skill set. For example, the proposal team can analyze each member's skill profile and assign tasks according to their skills. The proposal team can also propose challenging tasks appropriate to each member's skill level to encourage skill improvement. In this way, the proposal team can assign tasks based on skill sets and support the growth of its members.

[0049] The data collection unit can gather data on each member's work environment and adjust task assignments based on that environment. For example, the data collection unit can collect data on the noise level and lighting conditions of each member's workspace. It can also collect data on the arrangement of each member's desk and the condition of their chair. This allows the data collection unit to adjust the workload based on the work environment and provide a comfortable working environment.

[0050] The following briefly describes the processing flow for example form 1.

[0051] Step 1: The data collection team gathers each member's progress and schedule. For example, they obtain data from the task management tools and calendar apps used by each member, collecting information such as task completion status and scheduled meetings. This allows them to understand each member's current status. Step 2: The analysis department analyzes the data collected by the data collection department. For example, to evaluate the progress and efficiency of each member's tasks, they analyze the collected data and evaluate things like the speed of task completion and meeting attendance rates. This allows for a quantitative evaluation of each member's efficiency and progress. Step 3: The proposal team proposes optimal task assignments and meeting timings based on the evaluation results obtained by the analysis team. For example, they optimize task assignments and meeting timings by considering each member's efficiency and progress. They reduce tasks for members who are behind schedule and increase tasks for highly efficient members. They also propose optimal meeting times considering each member's schedule. This helps support increased productivity in a remote work environment.

[0052] (Example of form 2) The AI ​​agent system according to an embodiment of the present invention is a system that analyzes the progress and efficiency of team members' work in a remote work environment and automatically proposes optimal task assignments and meeting timings. The AI ​​agent system collects each member's progress and schedule, analyzes the collected data, and proposes optimal task assignments and meeting timings. This mechanism supports improved productivity in a remote work environment. For example, the AI ​​agent system collects each member's progress and schedule. In this process, it obtains data from task management tools and calendar apps used by each member. For example, it collects information such as the completion status of tasks and scheduled meetings. This allows the system to understand the current status of each member. Next, the AI ​​agent system analyzes the collected data. The AI ​​analyzes the collected data to evaluate the progress and efficiency of each member's tasks. For example, it evaluates the speed of task completion and meeting attendance rates. This allows the system to quantitatively evaluate the efficiency and progress of each member. Furthermore, based on the evaluation results, the AI ​​agent system proposes optimal task assignments and meeting timings. The AI ​​optimizes task assignments and meeting timings, taking into account the efficiency and progress of each member. For example, tasks can be reduced for members who are behind schedule and increased for highly efficient members. Meeting times can also be suggested to optimize the schedule, taking each member's availability into consideration. This improves the overall productivity of the team. The AI ​​agent system streamlines communication and task management in remote work environments, improving work efficiency. For instance, the visualization of task progress facilitates smoother communication among team members. Furthermore, the suggestion of optimal task assignments and meeting times reduces wasted time and allows for more efficient work. The objective of this invention is to maximize the overall productivity and efficiency of the team, even in a remote work environment, and to provide employees with a comfortable working environment. This overcomes the challenges of communication and task management inherent in remote work, enabling a more efficient remote work environment.This allows the AI ​​agent system to support increased productivity in remote work environments.

[0053] The AI ​​agent system according to this embodiment comprises a data collection unit, an analysis unit, and a proposal unit. The data collection unit collects the progress and schedule of each member. The data collection unit obtains data from, for example, task management tools or calendar applications used by each member. The data collection unit collects, for example, information on task completion status and scheduled meetings. This allows the data collection unit to understand the current status of each member. The analysis unit analyzes the data collected by the data collection unit. The analysis unit analyzes the collected data to evaluate, for example, the progress and efficiency of each member's tasks. The analysis unit evaluates, for example, the speed of task completion and meeting attendance rates. This allows the analysis unit to quantitatively evaluate the efficiency and progress of each member. The proposal unit proposes optimal task assignments and meeting timings based on the evaluation results obtained by the analysis unit. The proposal unit optimizes task assignments and meeting timings, for example, by considering the efficiency and progress of each member. The proposal unit reduces the tasks of members who are behind schedule and increases the tasks of highly efficient members. The proposal team also suggests the optimal meeting time, taking into account each member's schedule. This allows the AI ​​agent system, according to the embodiment, to support improved productivity in a remote work environment.

[0054] The data collection department collects the progress and schedules of each member. Specifically, it obtains data from the task management tools and calendar apps used by each member. For example, from task management tools, it collects information such as the progress of tasks each member is responsible for, the start and end dates of tasks, and the priority of tasks. From calendar apps, it collects information such as information on meetings and events each member is scheduled to attend, the start and end times of meetings, and the list of meeting participants. By regularly acquiring this data and updating it in real time, the data collection department can accurately grasp the current status of each member. Furthermore, the data collection department can also collect data from each member's communication tools. For example, by analyzing the frequency and content of each member's communication from chat tools and email, it can understand the communication situation within the team. This allows the data collection department to understand not only the progress and schedule of each member's tasks, but also the communication situation of the entire team. The data collection department centrally manages this data and can link it with other departments and systems as needed. For example, the collected data is stored on a cloud server and made accessible to the analysis department and the proposal department. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0055] The Analysis Department analyzes the data collected by the Data Collection Department. Specifically, it analyzes the collected data to evaluate the progress and efficiency of each member's tasks. For example, it evaluates the speed of task completion, meeting attendance rates, and progress based on task priority. The Analysis Department uses AI to analyze this data and quantitatively evaluate the efficiency and progress of each member. The AI ​​uses machine learning algorithms to learn each member's work patterns and efficiency trends from past data and evaluates each member's performance by comparing them with current data. For example, to evaluate the speed of task completion, it compares the completion time of past tasks with the completion time of current tasks to calculate each member's work efficiency. Also, to evaluate meeting attendance rates, it compares past meeting attendance data with current meeting attendance data to evaluate each member's participation in meetings. Furthermore, the Analysis Department can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. For example, if a particular member's task progress suddenly slows down or the meeting attendance rate suddenly drops, it will be detected as an anomaly and the manager will be notified. This allows the analysis department to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and security of the entire system.

[0056] The proposal department proposes optimal task assignments and meeting timings based on evaluation results obtained by the analysis department. Specifically, it optimizes task assignments and meeting timings considering the efficiency and progress of each member. For example, it reduces tasks for members who are behind schedule and increases tasks for highly efficient members. It also proposes optimal meeting times considering each member's schedule. The proposal department uses AI to make these proposals. The AI ​​analyzes each member's schedule and task progress to calculate optimal task assignments and meeting timings. For example, it analyzes each member's schedule and proposes the optimal meeting time that everyone can attend. It also analyzes each member's task progress and proposes optimal task assignments based on task priority and urgency. Furthermore, the proposal department can collect feedback from each member and continuously improve the accuracy and effectiveness of its proposals. For example, it reviews and improves proposals based on feedback from each member regarding the proposed task assignments and meeting timings. In addition, the proposal department can reliably transmit information using multiple communication methods. For example, it uses not only smartphone notifications but also voice calls and emails to ensure that important information is delivered reliably. This allows the proposal department to provide each member with the most suitable proposal quickly and reliably, supporting improved productivity in a remote work environment.

[0057] The data collection unit can acquire data from task management tools and calendar apps used by each member. For example, the data collection unit can acquire task completion status from the task management tools used by each member. The data collection unit can also acquire information about scheduled meetings from the calendar apps used by each member. This allows the data collection unit to accurately understand the progress and schedule of each member. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input data acquired from task management tools and calendar apps into a generating AI and have the generating AI perform data analysis.

[0058] The analysis unit can analyze the collected data and evaluate the progress and efficiency of each member's tasks. For example, the analysis unit can evaluate the completion rate and adherence to deadlines of each member's tasks based on the collected data. The analysis unit can also evaluate the processing speed and resource utilization efficiency of each member's tasks. For example, the analysis unit can evaluate each member's progress based on the completion rate of tasks. The analysis unit can also evaluate each member's efficiency based on adherence to deadlines. This allows the analysis unit to quantitatively evaluate the efficiency and progress of each member. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform the data analysis.

[0059] Based on the evaluation results, the proposal department can propose reducing the tasks of members who are behind schedule and increasing the tasks of highly efficient members. For example, the proposal department can propose reducing the tasks of members who are behind schedule. The proposal department can also propose increasing the tasks of highly efficient members. For example, the proposal department can propose reducing the number of tasks of members who are behind schedule. The proposal department can also propose increasing the number of tasks of highly efficient members. This allows the proposal department to allocate tasks according to the efficiency of each member. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input the evaluation results into a generating AI and have the generating AI execute task allocation proposals.

[0060] The proposal department can suggest the optimal timing for meetings, taking into account each member's schedule. For example, the proposal department can suggest the optimal timing for meetings based on each member's schedule. The proposal department can also suggest the optimal timing for meetings, taking into account each member's availability and the progress of the project. For example, the proposal department can suggest the optimal timing for meetings based on each member's availability. The proposal department can also suggest the optimal timing for meetings based on the progress of the project. By optimizing meeting timing, the proposal department can reduce wasted time and proceed with work efficiently. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input each member's schedule data into a generating AI and have the generating AI generate suggestions for the optimal timing for meetings.

[0061] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the collection timing to reduce the burden. The data collection unit can also accelerate the collection timing to efficiently acquire data if the user is relaxed. For example, if the user is concentrating, the data collection unit can adjust the collection timing to minimize interruptions to their work. In this way, the data collection unit can reduce the burden on the user by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the timing of data collection.

[0062] The data collection unit can analyze each member's past data collection history and select the optimal collection method. For example, the data collection unit can select the most efficient collection method based on each member's past data collection history. The data collection unit can also analyze each member's past data collection history and optimize the collection frequency. For example, the data collection unit can adjust the collection timing based on each member's past data collection history. This enables efficient data collection by allowing the data collection unit to select the optimal collection method based on past data collection history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past data collection history into a generating AI and have the generating AI select the optimal collection method.

[0063] The data collection unit can filter data based on each member's current projects and areas of interest during data collection. For example, the data collection unit can collect only data related to each member's current projects. The data collection unit can also prioritize the collection of highly relevant data based on each member's areas of interest. For example, the data collection unit can filter and collect data related to each member's current tasks. This allows the data collection unit to prioritize the collection of data related to each member's current projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input data on each member's projects and areas of interest into a generating AI and have the generating AI perform the filtering.

[0064] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will postpone collecting less important data. The data collection unit can also prioritize collecting more important data if the user is relaxed. For example, if the user is focused, the data collection unit will quickly collect more important data. This enables efficient data collection by allowing the data collection unit to prioritize data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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 data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of the data.

[0065] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of each member during data collection. For example, the data collection unit prioritizes the collection of highly relevant data based on each member's current location. The data collection unit can also select the optimal data collection method by considering the geographical location information of each member. For example, the data collection unit adjusts the collection timing based on each member's geographical location information. In this way, the data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of each member. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input each member's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0066] The data collection unit can analyze each member's social media activity and collect relevant data during data collection. For example, the data collection unit can analyze each member's social media activity and prioritize the collection of relevant data. The data collection unit can also select the optimal data collection method based on each member's social media activity. For example, the data collection unit can adjust the timing of data collection based on each member's social media activity. This allows the data collection unit to prioritize the collection of relevant data by analyzing each member's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input each member's social media activity data into a generating AI and have the generating AI collect the relevant data.

[0067] The analysis unit can estimate the user's emotions and adjust the data analysis method based on the estimated emotions. For example, if the user is stressed, the analysis unit may select a simpler analysis method. The analysis unit may also select a more detailed analysis method if the user is relaxed. For example, if the user is focused, the analysis unit may select a more rapid analysis method. This allows the analysis unit to perform efficient data analysis by adjusting the data analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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 analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the data analysis method.

[0068] The analysis unit can adjust the level of detail of the analysis based on the importance of each member's task during data analysis. For example, the analysis unit performs a detailed analysis based on the importance of each member's task. The analysis unit can also determine the priority of the analysis according to the importance of each member's task. For example, the analysis unit adjusts the level of detail of the analysis considering the importance of each member's task. This enables efficient data analysis by allowing the analysis unit to adjust the level of detail according to the importance of each member's task. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance data of each member's task into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0069] The analysis department can apply different analysis algorithms to data analysis depending on each member's role and job responsibilities. For example, the analysis department can select the optimal analysis algorithm according to each member's role. The analysis department can also apply different analysis algorithms based on each member's job responsibilities. For example, the analysis department can adjust the analysis algorithm considering each member's role and job responsibilities. This enables efficient data analysis by applying the optimal analysis algorithm according to each member's role and job responsibilities. Some or all of the above processes in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can input data on each member's role and job responsibilities into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0070] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is stressed, the analysis unit provides a simple and highly visible display method. The analysis unit can also provide a display method that includes detailed information if the user is relaxed. For example, if the user is focused, the analysis unit provides a quick and concise display method. This allows the analysis unit to provide a highly visible display by adjusting the display method of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the display method of the analysis results.

[0071] The analysis unit can determine the priority of analysis based on each member's submission timing during data analysis. For example, the analysis unit determines the priority of analysis based on each member's submission timing. The analysis unit can also adjust the level of detail of the analysis, taking into account each member's submission timing. For example, the analysis unit adjusts the timing of the analysis according to each member's submission timing. This enables efficient data analysis by allowing the analysis unit to determine the priority of analysis based on each member's submission timing. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input each member's submission timing data into a generating AI and have the generating AI determine the priority of analysis.

[0072] The analysis unit can adjust the order of analysis based on the relationships between each member during data analysis. For example, the analysis unit adjusts the order of analysis based on the relationships between each member. The analysis unit can also determine the priority of analysis by considering the relationships between each member. For example, the analysis unit adjusts the level of detail of analysis according to the relationships between each member. This enables efficient data analysis by allowing the analysis unit to adjust the order of analysis based on the relationships between each member. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relationship data for each member into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0073] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is stressed, the suggestion unit will present simple and easily understandable suggestions. If the user is relaxed, the suggestion unit can also present suggestions that include more detailed information. If the user is focused, the suggestion unit will present quick and concise suggestions. This allows the suggestion unit to present highly understandable suggestions by adjusting the way it presents suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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 suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the way it presents suggestions.

[0074] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0075] The data collection unit can collect health data from each member and adjust task assignments based on their health status. For example, the data collection unit can acquire heart rate and sleep data from each member's wearable device. The data collection unit can also collect stress level and exercise data from each member's health app. This allows the data collection unit to understand each member's health status and adjust the workload accordingly.

[0076] The analytics department can predict future performance based on each member's past performance data. For example, it can predict each member's future performance based on past task completion speed and meeting attendance rates. The analytics department can also analyze the success and failure rates of past projects to assess the risks of future projects. This allows the analytics department to predict future performance and manage risks.

[0077] The proposal team can propose optimal task assignments by considering each member's skill set. For example, the proposal team can analyze each member's skill profile and assign tasks according to their skills. The proposal team can also propose challenging tasks appropriate to each member's skill level to encourage skill improvement. In this way, the proposal team can assign tasks based on skill sets and support the growth of its members.

[0078] The data collection unit can gather data on each member's work environment and adjust task assignments based on that environment. For example, the data collection unit can collect data on the noise level and lighting conditions of each member's workspace. It can also collect data on the arrangement of each member's desk and the condition of their chair. This allows the data collection unit to adjust the workload based on the work environment and provide a comfortable working environment.

[0079] The proposal team can propose optimal task assignments, taking into account each member's motivation level. For example, the proposal team can evaluate each member's motivation level and assign challenging tasks to highly motivated members. The proposal team can also suggest tasks that provide a greater sense of accomplishment to less motivated members. In this way, the proposal team can assign tasks based on motivation levels, thereby boosting the members' morale.

[0080] The analysis department can estimate each member's emotions and evaluate task progress based on those estimated emotions. For example, the analysis department can collect emotional data from each member and evaluate the impact of emotions on task progress. The analysis department can also re-evaluate task progress in response to changes in emotions. This allows the analysis department to perform emotion-based progress evaluations and provide more accurate assessments.

[0081] The proposal team can estimate each member's emotions and adjust meeting timing based on those estimates. For example, the proposal team can collect emotional data from each member and delay the meeting if they are stressed. They can also advance the meeting if they are relaxed. This allows the proposal team to suggest meeting timings based on emotions, reducing the burden on members.

[0082] The data collection unit can estimate each member's emotions and adjust the data collection method based on those estimates. For example, if a member is feeling stressed, the unit will collect data using non-invasive methods. It can also collect detailed data if the member is relaxed. This allows the unit to select emotion-based data collection methods, reducing the burden on members.

[0083] The analysis unit can estimate each member's emotions and adjust the data analysis results based on those estimated emotions. For example, if each member is feeling stressed, the analysis unit will provide a simple analysis result. It can also provide a more detailed analysis result if the member is relaxed. This allows the analysis unit to provide emotion-based analysis results, facilitating a better understanding of the members.

[0084] The proposal team can estimate each member's emotions and adjust task priorities based on those estimates. For example, if a member is feeling stressed, the proposal team might postpone less important tasks. Conversely, if a member is relaxed, the proposal team might prioritize more important tasks. This allows the proposal team to propose emotion-based task priorities, enabling efficient task management.

[0085] The following briefly describes the processing flow for example form 2.

[0086] Step 1: The data collection team gathers each member's progress and schedule. For example, they obtain data from the task management tools and calendar apps used by each member, collecting information such as task completion status and scheduled meetings. This allows them to understand each member's current status. Step 2: The analysis department analyzes the data collected by the data collection department. For example, to evaluate the progress and efficiency of each member's tasks, they analyze the collected data and evaluate things like the speed of task completion and meeting attendance rates. This allows for a quantitative evaluation of each member's efficiency and progress. Step 3: The proposal team proposes optimal task assignments and meeting timings based on the evaluation results obtained by the analysis team. For example, they optimize task assignments and meeting timings by considering each member's efficiency and progress. They reduce tasks for members who are behind schedule and increase tasks for highly efficient members. They also propose optimal meeting times considering each member's schedule. This helps support increased productivity in a remote work environment.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] Each of the multiple elements described above, including the data collection unit, analysis unit, and proposal unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the smart device 14 and acquires data from each member's task management tool or calendar application. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data to evaluate each member's efficiency and progress. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the optimal task assignment and meeting timing based on the evaluation results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0091] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0096] 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).

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] Each of the multiple elements described above, including the data collection unit, analysis unit, and proposal unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the smart glasses 214 and acquires data from each member's task management tool or calendar app. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data to evaluate each member's efficiency and progress. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the optimal task assignment and meeting timing based on the evaluation results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0107] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] Each of the multiple elements described above, including the data collection unit, analysis unit, and proposal unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the headset terminal 314 and acquires data from each member's task management tool or calendar application. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data to evaluate each member's efficiency and progress. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the optimal task assignment and meeting timing based on the evaluation results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0123] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] Each of the multiple elements described above, including the data collection unit, analysis unit, and proposal unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the robot 414 and acquires data from each member's task management tool or calendar application. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data to evaluate the efficiency and progress of each member. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the optimal task assignment and meeting timing based on the evaluation results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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."

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] (Note 1) The collection department collects the progress and schedules of each member, An analysis unit analyzes the data collected by the aforementioned collection unit, The system includes a proposal unit that suggests optimal task assignments and meeting timings based on evaluation results obtained by the aforementioned analysis unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Data is collected from the task management tools and calendar apps used by each member. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is The collected data is analyzed to evaluate the progress and efficiency of each member's tasks. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, Based on the evaluation results, we will propose reducing the tasks of members who are behind schedule and increasing the tasks of high-performing members. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, We will propose the optimal meeting time, taking into account each member's schedule. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Analyze each member's past data collection history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting data, filter it based on each member's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting data, the system prioritizes collecting highly relevant data, taking into account the geographical location of each member. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, analyze each member's social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is We estimate the user's emotions and adjust the data analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is During data analysis, adjust the level of detail based on the importance of each member's task. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is When analyzing data, different analysis algorithms are applied depending on each member's role and responsibilities. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is When analyzing data, prioritize the analysis based on when each member submitted their work. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During data analysis, adjust the order of analysis based on the relationships between each member. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0159] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The collection department collects the progress and schedules of each member, An analysis unit analyzes the data collected by the aforementioned collection unit, The system includes a proposal unit that suggests optimal task assignments and meeting timings based on evaluation results obtained by the aforementioned analysis unit. A system characterized by the following features.

2. The aforementioned collection unit is Data is collected from the task management tools and calendar apps used by each member. The system according to feature 1.

3. The aforementioned analysis unit is The collected data is analyzed to evaluate the progress and efficiency of each member's tasks. The system according to feature 1.

4. The aforementioned proposal section is, Based on the evaluation results, we will propose reducing the tasks of members who are behind schedule and increasing the tasks of high-performing members. The system according to feature 1.

5. The aforementioned proposal section is, We will propose the optimal meeting time, taking into account each member's schedule. The system according to feature 1.

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

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

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

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