system

Abstract

The system according to this embodiment aims to automatically perform optimal rescheduling even when new tasks are added. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a data collection unit, a prediction unit, and a rescheduling unit. The reception unit receives input of the scope and final completion date of a new task. The analysis unit analyzes the information received by the reception unit. The data collection unit collects past daily report data. The analysis unit analyzes the data collected by the data collection unit and grasps the schedule pattern. The prediction unit predicts the monthly and yearly schedule based on the schedule pattern grasped by the analysis unit. The rescheduling unit performs optimal rescheduling based on the information obtained by the analysis unit and the prediction unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, 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 chatbot character, 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 prior art, when a new task is added, the schedule adjustment is manually performed, resulting in a problem that the work efficiency is reduced and it is difficult to shorten the working hours.

[0005] The system according to the embodiment aims to automatically perform optimal rescheduling even when a new task is added.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a data collection unit, a forecasting unit, and a rescheduling unit. The reception unit receives input of the scope and final completion date of a new task. The analysis unit analyzes the information received by the reception unit. The data collection unit collects past daily report data. The analysis unit analyzes the data collected by the data collection unit to understand the schedule pattern. The forecasting unit predicts the monthly and yearly schedule based on the schedule pattern understood by the analysis unit. The rescheduling unit performs optimal rescheduling based on the information obtained by the analysis unit and the forecasting unit. [Effects of the Invention]

[0007] The system according to this embodiment can automatically perform optimal rescheduling even when new tasks are added. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The task management and scheduling system according to an embodiment of the present invention automates the management and scheduling of work and private tasks, enabling instant and optimal rescheduling even when new appointments are added, thereby improving work efficiency and reducing working hours, and providing a function that supports the balance between work and private time. The task management and scheduling system works as follows: The user inputs a new task. The generating AI, based on the task's scope, final completion date, and past daily report data of the user and their department, predicts periods of relative free time and busy periods within a month or year. This allows for coordination with all appointments, minimizing overtime and facilitating the securing of time outside of working hours. For example, the user inputs a new task, including its scope and final completion date. For instance, the user might input a task such as "Start a new project." This information is input to the generating AI. The generating AI analyzes the input information and, based on past daily report data of the user and their department, predicts periods of relative free time and busy periods within a month or year. The generating AI analyzes past data to understand the user's scheduling patterns. For example, it might predict from past data that a particular month will be busy. The generation AI reschedules new tasks to the optimal time based on predicted schedule patterns. For example, it minimizes overtime by placing new tasks during periods with relatively more free time. Furthermore, the generation AI integrates with calendar apps, allowing users to visually manage their schedules in a virtual space. Users can adjust schedules using drag-and-drop. This is expected to improve work efficiency and reduce overtime costs, while also allowing users to secure more personal time. This makes it easier to balance work and personal life, supporting everyone in achieving a happier life. Employers also benefit from easier cost reduction through improved work efficiency and reduced overtime. In short, task management and scheduling systems can achieve improved work efficiency and shorter working hours.

[0029] The task management and scheduling system according to this embodiment comprises a reception unit, an analysis unit, a data collection unit, a forecasting unit, and a rescheduling unit. The reception unit accepts input of the scope and final completion date of a new task. For example, when a user enters a new task, the reception unit can accept input of the task scope and final completion date. The analysis unit analyzes the information received by the reception unit. For example, the analysis unit analyzes the task scope and final completion date to determine the importance and priority of the task. The data collection unit collects past daily report data. For example, the data collection unit collects past daily report data of users and departments and stores it in a database. The analysis unit analyzes the data collected by the data collection unit to understand schedule patterns. For example, the analysis unit analyzes past daily report data to understand the user's schedule patterns. The forecasting unit forecasts monthly and yearly schedules based on the schedule patterns understood by the analysis unit. For example, the forecasting unit predicts periods when users have relatively more free time and periods when they are busy, based on past data. The rescheduling unit performs optimal rescheduling based on the information obtained by the analysis unit and the prediction unit. For example, the rescheduling unit minimizes overtime by scheduling new tasks during periods with relatively more available time. As a result, the task management and schedule adjustment system according to this embodiment automates everything from inputting new tasks to rescheduling, achieving improved work efficiency and reduced working hours.

[0030] The reception department accepts input of the scope and final completion date for new tasks. For example, when a user enters a new task, the reception department allows them to enter the task scope and final completion date. Specifically, a form for entering detailed task information is provided through the user interface. This form includes items such as task name, task description, start date, end date, assignee, and priority. By entering these items, users can gain a clear overview of the task. The reception department also has a function to verify the entered information in real time and check for missing or inappropriate information. For example, if the end date is earlier than the start date, or if required fields are not entered, an error message is displayed to prompt the user to correct it. Furthermore, the reception department saves the entered task information to a database, making it accessible to subsequent analysis and prediction departments. This allows the reception department to enable users to efficiently enter new tasks and provide accurate information.

[0031] The analysis unit analyzes the information received by the reception unit. For example, the analysis unit analyzes the scope and final completion date of a task to determine its importance and priority. Specifically, it uses an algorithm that comprehensively evaluates the scope, deadline, workload of the person in charge, and task dependencies to automatically calculate task priority. For example, tasks of high importance or with approaching deadlines are given a high priority, while tasks of low importance or with ample time before the deadline are given a low priority. The analysis unit also considers task dependencies, and if a task cannot proceed to the next task until a specific task is completed, it creates a schedule that reflects those dependencies. Furthermore, the analysis unit uses AI to learn from past task data and improve the accuracy of predicting the time and resources required to complete a task. As a result, the analysis unit can accurately determine the importance and priority of tasks and achieve efficient schedule management.

[0032] The data collection unit collects past daily report data. For example, the data collection unit collects past daily report data from users and departments and stores it in a database. Specifically, it builds a system that automatically collects the daily report data entered by users each day and stores it in a central database. The daily report data includes the progress of each task, working time, problems encountered, and next schedule. The data collection unit collects this data regularly and makes it accessible to the analysis and forecasting units. In addition, the data collection unit unifies the data format and input rules to maintain data integrity and consistency, making it easy for users to enter daily reports. Furthermore, the data collection unit has a function that allows for flexible setting of the data collection frequency and method, and can adjust the data collection method according to specific periods and conditions. This allows the data collection unit to efficiently collect past daily report data and strengthen data management for the entire system.

[0033] The forecasting unit predicts monthly and yearly schedules based on schedule patterns identified by the analysis unit. For example, the forecasting unit predicts periods when users have relatively more free time or are busier, based on past data. Specifically, it analyzes past task completion data and daily report data to identify users' work patterns, busy periods, and slow periods. Based on this data, the forecasting unit predicts future schedules and helps users manage tasks efficiently. The forecasting unit also utilizes AI and models learned from past data to predict future task completion and optimize resource allocation. For example, if many tasks are concentrated during a particular period, the forecasting unit adjusts the schedule to concentrate resources during that period. Furthermore, the forecasting unit continuously improves its prediction model based on user feedback to enhance prediction accuracy. As a result, the forecasting unit enables users to manage their schedules efficiently and improve work efficiency.

[0034] The rescheduling unit performs optimal rescheduling based on information obtained from the analysis and prediction units. For example, the rescheduling unit minimizes overtime by scheduling new tasks during periods with relatively more available time. Specifically, it uses an algorithm to optimize task placement based on task priorities determined by the analysis unit and schedule patterns predicted by the prediction unit. The rescheduling unit adjusts task start and end dates to evenly distribute the user's workload and create a manageable schedule. The rescheduling unit also optimizes the order and placement of tasks, taking into account task dependencies and resource constraints. Furthermore, the rescheduling unit flexibly adjusts the schedule based on user feedback, enabling it to respond to unexpected changes and the addition of new tasks. As a result, the rescheduling unit enables users to efficiently manage tasks, improve work efficiency, and reduce working hours.

[0035] The rescheduling unit can work in conjunction with a calendar application to allow users to visually manage their schedules in a virtual space. The rescheduling unit can also work in conjunction with an AR / VR calendar application to allow users to visually manage their schedules in a virtual space. This allows users to visually manage their schedules in a virtual space. Some or all of the above-described processes in the rescheduling unit may be performed using AI, for example, or without AI. For example, the rescheduling unit can input data from a calendar application into a generating AI and have the generating AI manage the schedules.

[0036] The rescheduling unit can be equipped with a function to adjust schedules using drag and drop. For example, the rescheduling unit can adjust schedules by allowing the user to move tasks using drag and drop. The rescheduling unit can also change the priority of tasks using drag and drop. This allows the user to adjust schedules using drag and drop. Some or all of the above-described processes in the rescheduling unit may be performed using AI, for example, or without AI. For example, the rescheduling unit can input drag and drop operation data into a generating AI and have the generating AI perform the schedule adjustment.

[0037] The prediction unit can understand the user's schedule patterns based on past daily report data. For example, the prediction unit analyzes past daily report data to understand the user's schedule patterns. The prediction unit can also predict periods when the user has relatively more free time or when they are busy, based on past daily report data. By understanding the user's schedule patterns, optimal rescheduling becomes possible. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input past daily report data into a generation AI and have the generation AI perform the task of understanding the schedule patterns.

[0038] The analysis unit can analyze the scope and final completion date of a task. For example, the analysis unit analyzes the scope and final completion date of a task to determine its importance and priority. The analysis unit can also predict the progress of a task based on its scope and final completion date. This allows for appropriate schedule adjustments by analyzing the scope and final completion date of a task. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the task scope and final completion date data into a generating AI and have the generating AI perform the analysis.

[0039] The data collection unit can collect past daily report data. For example, the data collection unit can collect past daily report data from users and departments and store it in a database. The data collection unit can also build a system that automatically collects past daily report data. This makes it possible to understand schedule patterns by collecting past daily report data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past daily report data into a generation AI and have the generation AI perform the data collection.

[0040] The reception desk can analyze the user's past task input history and provide the optimal input interface. For example, the reception desk can automatically display as suggestions the scope and final completion date of tasks that the user has frequently entered in the past. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest the scope and final completion date of tasks to be used during a specific time period based on the user's past input history. In this way, the reception desk can provide the optimal input interface by analyzing the user's past task input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past task input history data into a generating AI and have the generating AI perform the task of providing the optimal input interface.

[0041] The input unit can suggest input options based on the user's current projects and areas of interest when a task is entered. For example, the input unit can automatically display the scope and due date of tasks related to the user's current project as suggestions. The input unit can also suggest the scope and due date of related tasks based on the user's areas of interest. Furthermore, the input unit can suggest the scope and due date of related tasks based on projects the user has previously shown interest in. This improves the efficiency of task entry by suggesting input options based on the user's current projects and areas of interest. Some or all of the above processing in the input unit may be performed using AI, for example, or not using AI. For example, the input unit can input data on the user's current projects and areas of interest into a generating AI and have the generating AI perform the task of suggesting input options.

[0042] The reception unit can prioritize the input of highly relevant tasks by considering the user's geographical location when tasks are entered. For example, the reception unit can prioritize tasks that the user can perform near their current location. Furthermore, if the user is in a specific region, the reception unit can prioritize tasks related to that region. Additionally, if the user is on the move, the reception unit can prioritize tasks related to their destination. This allows for the priority input of highly relevant tasks by considering the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, or without AI. For example, the reception unit can input the user's geographical location data into a generating AI and have the generating AI input highly relevant tasks.

[0043] The reception desk can analyze a user's social media activity when a task is entered and input relevant tasks. For example, the reception desk can automatically input tasks that the user has mentioned on social media. It can also input tasks related to projects that the user follows on social media. Furthermore, it can input tasks related to events that the user has shared on social media. This allows for the efficient input of relevant tasks by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media activity data into a generating AI and have the generating AI input the relevant tasks.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the task during the analysis. For example, the analysis unit can perform a detailed analysis for tasks of high importance. It can also perform a simplified analysis for tasks of low importance. Furthermore, it can perform an analysis with an appropriate level of detail for tasks of medium importance. By adjusting the level of detail of the analysis based on the importance of the task, it is possible to provide appropriate analysis results. 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 task importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the task category during analysis. For example, the analysis unit can apply a project management-specific analysis algorithm to project management tasks. It can also apply a personal task-specific analysis algorithm to personal tasks. Furthermore, it can apply a team task-specific analysis algorithm to team tasks. By applying different analysis algorithms depending on the task category, more accurate analysis results can be provided. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input task category data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0046] The analysis unit can determine the priority of analysis based on the task submission timing during the analysis process. For example, the analysis unit will prioritize tasks with approaching deadlines. It can also postpone tasks with distant deadlines. Furthermore, it can appropriately adjust the priority of tasks with medium-term deadlines. By determining the priority of analysis based on task submission timing, the analysis unit can provide analysis results at the appropriate time. 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 task submission timing data into a generating AI and have the generating AI determine the analysis priority.

[0047] The analysis unit can adjust the order of analysis based on the relevance of tasks during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant tasks. It can also postpone the analysis of less relevant tasks. Furthermore, the analysis unit can appropriately adjust the priority of tasks with moderate relevance. In this way, by adjusting the order of analysis based on the relevance of tasks, highly relevant tasks can be prioritized. 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 task relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0048] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the data collection unit can automatically display data that the user has frequently collected in the past as candidates. The data collection unit can also prioritize suggesting collection methods (such as voice or text) that the user has used in the past. Furthermore, the data collection unit can predict and suggest collection methods to be used during specific time periods based on the user's past collection history. In this way, the optimal collection method can be provided by analyzing the user's past data collection history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past data collection history data into a generating AI and have the generating AI select the optimal collection method.

[0049] The data collection unit can filter data based on the user's current projects and areas of interest during data collection. For example, the data collection unit can prioritize collecting data related to the user's current projects. It can also prioritize collecting relevant data based on the user's areas of interest. Furthermore, it can prioritize collecting relevant data based on projects the user has shown interest in in the past. This allows for the priority collection of highly relevant data by filtering data based on the user's current projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's current projects and areas of interest into a generating AI and have the generating AI perform data filtering.

[0050] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, the data collection unit can prioritize the collection of data that is close to the user's current location. Furthermore, if the user is in a specific region, the data collection unit can prioritize the collection of data related to that region. Additionally, if the user is on the move, the data collection unit can prioritize the collection of data related to their destination. This allows for the priority collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information data into a generating AI and have the generating AI perform the collection of highly relevant data.

[0051] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can automatically collect data that a user has mentioned on social media. It can also collect data related to projects that a user follows on social media. Furthermore, the data collection unit can collect data related to events that a user has shared on social media. This allows for the efficient collection of relevant data by analyzing a user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0052] The prediction unit can optimize the current prediction by referring to past data during the prediction process. For example, the prediction unit can predict busy periods based on past data and reflect this in the current prediction. It can also predict the completion time of a specific task based on past data and reflect this in the current prediction. Furthermore, the prediction unit can predict the progress of a specific project by referring to past data and reflect this in the current prediction. In this way, the current prediction can be optimized by referring to past data. Some or all of the above processes in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input past data into a generating AI and have the generating AI perform the optimization of the prediction.

[0053] The prediction unit can apply different prediction algorithms to each task category during prediction. For example, the prediction unit can apply a prediction algorithm specifically for project management to project management tasks. It can also apply a prediction algorithm specifically for individual tasks to individual tasks. Furthermore, it can apply a prediction algorithm specifically for team tasks to team tasks. By applying different prediction algorithms to each task category, more accurate prediction results can be provided. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input task category data into a generating AI and have the generating AI execute the application of the prediction algorithm.

[0054] The prediction unit can analyze changes in predictions based on task submission times. For example, the prediction unit prioritizes analyzing predictions for tasks with approaching deadlines. It can also postpone predictions for tasks with distant deadlines. Furthermore, it can appropriately adjust the priority of predictions for tasks with medium-term deadlines. By analyzing changes in predictions based on task submission times, it can provide prediction results at the appropriate time. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input task submission time data into a generating AI and have the generating AI perform the analysis of changes in predictions.

[0055] The forecasting unit can analyze its predictions by referring to relevant market data for the task during the forecasting process. For example, the forecasting unit can predict the completion time of a specific task based on relevant market data. It can also predict the progress of a specific project by referring to relevant market data. Furthermore, the forecasting unit can predict busy periods at specific times based on relevant market data. This allows for more accurate forecast results by referring to relevant market data for the task. Some or all of the above processing in the forecasting unit may be performed using AI, for example, or without AI. For example, the forecasting unit can input relevant market data into a generating AI and have the generating AI perform the analysis of the prediction.

[0056] The rescheduling unit can analyze the user's past schedule history to select the optimal rescheduling method during rescheduling. For example, the rescheduling unit can automatically display tasks that the user has frequently rescheduled in the past as candidates. The rescheduling unit can also prioritize suggesting rescheduling methods (voice, text, etc.) that the user has used in the past. Furthermore, the rescheduling unit can predict and suggest rescheduling methods to be used during specific time periods based on the user's past schedule history. In this way, the optimal rescheduling method can be provided by analyzing the user's past schedule history. Some or all of the above processing in the rescheduling unit may be performed using AI, for example, or without AI. For example, the rescheduling unit can input the user's past schedule history data into a generating AI and have the generating AI select the optimal rescheduling method.

[0057] The rescheduling unit can customize the rescheduling method based on the user's current living situation during rescheduling. For example, the rescheduling unit can prioritize providing rescheduling methods related to projects the user is currently working on. The rescheduling unit can also prioritize providing relevant rescheduling methods based on the user's living situation. Furthermore, the rescheduling unit can prioritize providing relevant rescheduling methods based on projects the user has shown interest in in the past. This allows for more appropriate rescheduling by customizing the rescheduling method based on the user's current living situation. Some or all of the above processing in the rescheduling unit may be performed using AI, for example, or without AI. For example, the rescheduling unit can input the user's current living situation data into a generating AI and have the generating AI perform the customization of the rescheduling method.

[0058] The rescheduling unit can select the optimal rescheduling method by considering the user's geographical location information during rescheduling. For example, the rescheduling unit can prioritize providing rescheduling methods that are close to the user's current location. Furthermore, if the user is in a specific region, the rescheduling unit can prioritize providing rescheduling methods related to that region. Additionally, if the user is on the move, the rescheduling unit can prioritize providing rescheduling methods related to their destination. This allows the rescheduling unit to provide the optimal rescheduling method by considering the user's geographical location information. Some or all of the above processing in the rescheduling unit may be performed using AI, for example, or without AI. For example, the rescheduling unit can input the user's geographical location data into a generating AI and have the generating AI select the optimal rescheduling method.

[0059] The rescheduling unit can analyze the user's social media activity and propose rescheduling methods during the rescheduling process. For example, the rescheduling unit can automatically propose rescheduling methods mentioned by the user on social media. It can also propose rescheduling methods related to projects the user follows on social media. Furthermore, it can propose rescheduling methods related to events shared by the user on social media. This allows for the efficient proposal of relevant rescheduling methods by analyzing the user's social media activity. Some or all of the above processing in the rescheduling unit may be performed using AI, for example, or without AI. For example, the rescheduling unit can input the user's social media activity data into a generating AI and have the generating AI execute the rescheduling method proposal.

[0060] The rescheduling unit can make schedule-based suggestions by referring to the user's calendar information during rescheduling. For example, the rescheduling unit can refer to the schedules registered in the user's calendar and automatically set up rescheduling. The rescheduling unit can also suggest rescheduling methods related to specific events based on the user's calendar information. Furthermore, the rescheduling unit can suggest the optimal rescheduling method tailored to the schedule based on the user's calendar information. This makes it possible to make optimal schedule-based rescheduling suggestions by referring to the user's calendar information. Some or all of the above processing in the rescheduling unit may be performed using AI, for example, or without AI. For example, the rescheduling unit can input the user's calendar information data into a generating AI and have the generating AI execute schedule-based suggestions.

[0061] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0062] The reception desk can analyze a user's past task input history and provide the optimal input interface. For example, it can automatically display the scope and final completion date of tasks that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest the scope and final completion date of tasks to be used during a specific time period based on the user's past input history. In this way, by analyzing the user's past task input history, the system can provide the optimal input interface.

[0063] The analysis unit can analyze the scope and final completion date of a task. For example, it can analyze the scope and final completion date of a task to determine its importance and priority. It can also predict the progress of a task based on its scope and final completion date. This allows for appropriate schedule adjustments by analyzing the scope and final completion date of a task.

[0064] The data collection unit can collect past daily report data. For example, it can collect past daily report data from users and departments and store it in a database. It can also build a system that automatically collects past daily report data. This makes it possible to understand schedule patterns by collecting past daily report data.

[0065] The forecasting unit can optimize the current forecast by referring to past data during the forecasting process. For example, it can predict busy periods based on past data and reflect this in the current forecast. It can also predict the completion time of a specific task based on past data and reflect this in the current forecast. Furthermore, it can predict the progress of a specific project by referring to past data and reflect this in the current forecast. In this way, the current forecast can be optimized by referring to past data.

[0066] The rescheduling unit can analyze the user's past schedule history to select the optimal rescheduling method during the rescheduling process. For example, it can automatically display tasks that the user has frequently rescheduled in the past as candidates. It can also prioritize suggesting rescheduling methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest rescheduling methods to be used during specific time periods based on the user's past schedule history. In this way, by analyzing the user's past schedule history, it can provide the optimal rescheduling method.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The reception desk accepts input of the scope and expected completion date for new tasks. For example, when a user enters a new task, they can enter the task scope and expected completion date. Step 2: The analysis unit analyzes the information received by the reception unit. For example, it analyzes the task scope and final completion date to determine the task's importance and priority. Step 3: The collection unit collects past daily report data. For example, it collects past daily report data for users and departments and stores it in a database. Step 4: The analysis unit analyzes the data collected by the collection unit to understand the schedule patterns. For example, it analyzes past daily report data to understand the user's schedule patterns. Step 5: The forecasting unit predicts monthly and yearly schedules based on the schedule patterns identified by the analysis unit. For example, it predicts periods when users have relatively more free time or are busier, based on past data. Step 6: The rescheduling unit performs optimal rescheduling based on the information obtained by the analysis unit and the prediction unit. For example, it minimizes overtime by scheduling new tasks during periods when there is relatively more time available.

[0069] (Example of form 2) The task management and scheduling system according to an embodiment of the present invention automates the management and scheduling of work and private tasks, enabling instant and optimal rescheduling even when new appointments are added, thereby improving work efficiency and reducing working hours, and providing a function that supports the balance between work and private time. The task management and scheduling system works as follows: The user inputs a new task. The generating AI, based on the task's scope, final completion date, and past daily report data of the user and their department, predicts periods of relative free time and busy periods within a month or year. This allows for coordination with all appointments, minimizing overtime and facilitating the securing of time outside of working hours. For example, the user inputs a new task, including its scope and final completion date. For instance, the user might input a task such as "Start a new project." This information is input to the generating AI. The generating AI analyzes the input information and, based on past daily report data of the user and their department, predicts periods of relative free time and busy periods within a month or year. The generating AI analyzes past data to understand the user's scheduling patterns. For example, it might predict from past data that a particular month will be busy. The generation AI reschedules new tasks to the optimal time based on predicted schedule patterns. For example, it minimizes overtime by placing new tasks during periods with relatively more free time. Furthermore, the generation AI integrates with calendar apps, allowing users to visually manage their schedules in a virtual space. Users can adjust schedules using drag-and-drop. This is expected to improve work efficiency and reduce overtime costs, while also allowing users to secure more personal time. This makes it easier to balance work and personal life, supporting everyone in achieving a happier life. Employers also benefit from easier cost reduction through improved work efficiency and reduced overtime. In short, task management and scheduling systems can achieve improved work efficiency and shorter working hours.

[0070] The task management and scheduling system according to this embodiment comprises a reception unit, an analysis unit, a data collection unit, a forecasting unit, and a rescheduling unit. The reception unit accepts input of the scope and final completion date of a new task. For example, when a user enters a new task, the reception unit can accept input of the task scope and final completion date. The analysis unit analyzes the information received by the reception unit. For example, the analysis unit analyzes the task scope and final completion date to determine the importance and priority of the task. The data collection unit collects past daily report data. For example, the data collection unit collects past daily report data of users and departments and stores it in a database. The analysis unit analyzes the data collected by the data collection unit to understand schedule patterns. For example, the analysis unit analyzes past daily report data to understand the user's schedule patterns. The forecasting unit forecasts monthly and yearly schedules based on the schedule patterns understood by the analysis unit. For example, the forecasting unit predicts periods when users have relatively more free time and periods when they are busy, based on past data. The rescheduling unit performs optimal rescheduling based on the information obtained by the analysis unit and the prediction unit. For example, the rescheduling unit minimizes overtime by scheduling new tasks during periods with relatively more available time. As a result, the task management and schedule adjustment system according to this embodiment automates everything from inputting new tasks to rescheduling, achieving improved work efficiency and reduced working hours.

[0071] The reception department accepts input of the scope and final completion date for new tasks. For example, when a user enters a new task, the reception department allows them to enter the task scope and final completion date. Specifically, a form for entering detailed task information is provided through the user interface. This form includes items such as task name, task description, start date, end date, assignee, and priority. By entering these items, users can gain a clear overview of the task. The reception department also has a function to verify the entered information in real time and check for missing or inappropriate information. For example, if the end date is earlier than the start date, or if required fields are not entered, an error message is displayed to prompt the user to correct it. Furthermore, the reception department saves the entered task information to a database, making it accessible to subsequent analysis and prediction departments. This allows the reception department to enable users to efficiently enter new tasks and provide accurate information.

[0072] The analysis unit analyzes the information received by the reception unit. For example, the analysis unit analyzes the scope and final completion date of a task to determine its importance and priority. Specifically, it uses an algorithm that comprehensively evaluates the scope, deadline, workload of the person in charge, and task dependencies to automatically calculate task priority. For example, tasks of high importance or with approaching deadlines are given a high priority, while tasks of low importance or with ample time before the deadline are given a low priority. The analysis unit also considers task dependencies, and if a task cannot proceed to the next task until a specific task is completed, it creates a schedule that reflects those dependencies. Furthermore, the analysis unit uses AI to learn from past task data and improve the accuracy of predicting the time and resources required to complete a task. As a result, the analysis unit can accurately determine the importance and priority of tasks and achieve efficient schedule management.

[0073] The data collection unit collects past daily report data. For example, the data collection unit collects past daily report data from users and departments and stores it in a database. Specifically, it builds a system that automatically collects the daily report data entered by users each day and stores it in a central database. The daily report data includes the progress of each task, working time, problems encountered, and next schedule. The data collection unit collects this data regularly and makes it accessible to the analysis and forecasting units. In addition, the data collection unit unifies the data format and input rules to maintain data integrity and consistency, making it easy for users to enter daily reports. Furthermore, the data collection unit has a function that allows for flexible setting of the data collection frequency and method, and can adjust the data collection method according to specific periods and conditions. This allows the data collection unit to efficiently collect past daily report data and strengthen data management for the entire system.

[0074] The forecasting unit predicts monthly and yearly schedules based on schedule patterns identified by the analysis unit. For example, the forecasting unit predicts periods when users have relatively more free time or are busier, based on past data. Specifically, it analyzes past task completion data and daily report data to identify users' work patterns, busy periods, and slow periods. Based on this data, the forecasting unit predicts future schedules and helps users manage tasks efficiently. The forecasting unit also utilizes AI and models learned from past data to predict future task completion and optimize resource allocation. For example, if many tasks are concentrated during a particular period, the forecasting unit adjusts the schedule to concentrate resources during that period. Furthermore, the forecasting unit continuously improves its prediction model based on user feedback to enhance prediction accuracy. As a result, the forecasting unit enables users to manage their schedules efficiently and improve work efficiency.

[0075] The rescheduling unit performs optimal rescheduling based on information obtained from the analysis and prediction units. For example, the rescheduling unit minimizes overtime by scheduling new tasks during periods with relatively more available time. Specifically, it uses an algorithm to optimize task placement based on task priorities determined by the analysis unit and schedule patterns predicted by the prediction unit. The rescheduling unit adjusts task start and end dates to evenly distribute the user's workload and create a manageable schedule. The rescheduling unit also optimizes the order and placement of tasks, taking into account task dependencies and resource constraints. Furthermore, the rescheduling unit flexibly adjusts the schedule based on user feedback, enabling it to respond to unexpected changes and the addition of new tasks. As a result, the rescheduling unit enables users to efficiently manage tasks, improve work efficiency, and reduce working hours.

[0076] The rescheduling unit can work in conjunction with a calendar application to allow users to visually manage their schedules in a virtual space. The rescheduling unit can also work in conjunction with an AR / VR calendar application to allow users to visually manage their schedules in a virtual space. This allows users to visually manage their schedules in a virtual space. Some or all of the above-described processes in the rescheduling unit may be performed using AI, for example, or without AI. For example, the rescheduling unit can input data from a calendar application into a generating AI and have the generating AI manage the schedules.

[0077] The rescheduling unit can be equipped with a function to adjust schedules using drag and drop. For example, the rescheduling unit can adjust schedules by allowing the user to move tasks using drag and drop. The rescheduling unit can also change the priority of tasks using drag and drop. This allows the user to adjust schedules using drag and drop. Some or all of the above-described processes in the rescheduling unit may be performed using AI, for example, or without AI. For example, the rescheduling unit can input drag and drop operation data into a generating AI and have the generating AI perform the schedule adjustment.

[0078] The prediction unit can understand the user's schedule patterns based on past daily report data. For example, the prediction unit analyzes past daily report data to understand the user's schedule patterns. The prediction unit can also predict periods when the user has relatively more free time or when they are busy, based on past daily report data. By understanding the user's schedule patterns, optimal rescheduling becomes possible. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input past daily report data into a generation AI and have the generation AI perform the task of understanding the schedule patterns.

[0079] The analysis unit can analyze the scope and final completion date of a task. For example, the analysis unit analyzes the scope and final completion date of a task to determine its importance and priority. The analysis unit can also predict the progress of a task based on its scope and final completion date. This allows for appropriate schedule adjustments by analyzing the scope and final completion date of a task. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the task scope and final completion date data into a generating AI and have the generating AI perform the analysis.

[0080] The data collection unit can collect past daily report data. For example, the data collection unit can collect past daily report data from users and departments and store it in a database. The data collection unit can also build a system that automatically collects past daily report data. This makes it possible to understand schedule patterns by collecting past daily report data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past daily report data into a generation AI and have the generation AI perform the data collection.

[0081] The reception desk can estimate the user's emotions and adjust the task input method based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input, allowing for quick input of the task scope and final completion date. This allows for more appropriate task input by adjusting the task input method 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 reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0082] The reception desk can analyze the user's past task input history and provide the optimal input interface. For example, the reception desk can automatically display as suggestions the scope and final completion date of tasks that the user has frequently entered in the past. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest the scope and final completion date of tasks to be used during a specific time period based on the user's past input history. In this way, the reception desk can provide the optimal input interface by analyzing the user's past task input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past task input history data into a generating AI and have the generating AI perform the task of providing the optimal input interface.

[0083] The input unit can suggest input options based on the user's current projects and areas of interest when a task is entered. For example, the input unit can automatically display the scope and due date of tasks related to the user's current project as suggestions. The input unit can also suggest the scope and due date of related tasks based on the user's areas of interest. Furthermore, the input unit can suggest the scope and due date of related tasks based on projects the user has previously shown interest in. This improves the efficiency of task entry by suggesting input options based on the user's current projects and areas of interest. Some or all of the above processing in the input unit may be performed using AI, for example, or not using AI. For example, the input unit can input data on the user's current projects and areas of interest into a generating AI and have the generating AI perform the task of suggesting input options.

[0084] The reception desk can estimate the user's emotions and determine the priority of input tasks based on the estimated emotions. For example, if the user is stressed, the reception desk will prioritize high-priority tasks. If the user is relaxed, the reception desk can also process tasks in a balanced manner, including less important tasks. Furthermore, if the user is in a hurry, the reception desk can prioritize high-urgency tasks. In this way, by prioritizing tasks according to the user's emotions, important tasks can be processed preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, or not using AI. For example, the reception desk can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0085] The reception unit can prioritize the input of highly relevant tasks by considering the user's geographical location when tasks are entered. For example, the reception unit can prioritize tasks that the user can perform near their current location. Furthermore, if the user is in a specific region, the reception unit can prioritize tasks related to that region. Additionally, if the user is on the move, the reception unit can prioritize tasks related to their destination. This allows for the priority input of highly relevant tasks by considering the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, or without AI. For example, the reception unit can input the user's geographical location data into a generating AI and have the generating AI input highly relevant tasks.

[0086] The reception desk can analyze a user's social media activity when a task is entered and input relevant tasks. For example, the reception desk can automatically input tasks that the user has mentioned on social media. It can also input tasks related to projects that the user follows on social media. Furthermore, it can input tasks related to events that the user has shared on social media. This allows for the efficient input of relevant tasks by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media activity data into a generating AI and have the generating AI input the relevant tasks.

[0087] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit can provide simple and easy-to-understand analysis results. If the user is relaxed, the analysis unit can also provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results. By adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0088] The analysis unit can adjust the level of detail of the analysis based on the importance of the task during the analysis. For example, the analysis unit can perform a detailed analysis for tasks of high importance. It can also perform a simplified analysis for tasks of low importance. Furthermore, it can perform an analysis with an appropriate level of detail for tasks of medium importance. By adjusting the level of detail of the analysis based on the importance of the task, it is possible to provide appropriate analysis results. 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 task importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0089] The analysis unit can apply different analysis algorithms depending on the task category during analysis. For example, the analysis unit can apply a project management-specific analysis algorithm to project management tasks. It can also apply a personal task-specific analysis algorithm to personal tasks. Furthermore, it can apply a team task-specific analysis algorithm to team tasks. By applying different analysis algorithms depending on the task category, more accurate analysis results can be provided. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input task category data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0090] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide an analysis result with visually stimulating effects. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0091] The analysis unit can determine the priority of analysis based on the task submission timing during the analysis process. For example, the analysis unit will prioritize tasks with approaching deadlines. It can also postpone tasks with distant deadlines. Furthermore, it can appropriately adjust the priority of tasks with medium-term deadlines. By determining the priority of analysis based on task submission timing, the analysis unit can provide analysis results at the appropriate time. 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 task submission timing data into a generating AI and have the generating AI determine the analysis priority.

[0092] The analysis unit can adjust the order of analysis based on the relevance of tasks during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant tasks. It can also postpone the analysis of less relevant tasks. Furthermore, the analysis unit can appropriately adjust the priority of tasks with moderate relevance. In this way, by adjusting the order of analysis based on the relevance of tasks, highly relevant tasks can be prioritized. 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 task relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0093] The data collection unit can estimate the user's emotions and adjust the data collection method based on the estimated emotions. For example, if the user is stressed, the data collection unit can provide a simple data collection method. If the user is relaxed, the data collection unit can also provide a detailed data collection method. Furthermore, if the user is in a hurry, the data collection unit can provide a rapid data collection method. This allows for more appropriate data collection by adjusting the data collection method 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 perform emotion estimation.

[0094] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the data collection unit can automatically display data that the user has frequently collected in the past as candidates. The data collection unit can also prioritize suggesting collection methods (such as voice or text) that the user has used in the past. Furthermore, the data collection unit can predict and suggest collection methods to be used during specific time periods based on the user's past collection history. In this way, the optimal collection method can be provided by analyzing the user's past data collection history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past data collection history data into a generating AI and have the generating AI select the optimal collection method.

[0095] The data collection unit can filter data based on the user's current projects and areas of interest during data collection. For example, the data collection unit can prioritize collecting data related to the user's current projects. It can also prioritize collecting relevant data based on the user's areas of interest. Furthermore, it can prioritize collecting relevant data based on projects the user has shown interest in in the past. This allows for the priority collection of highly relevant data by filtering data based on the user's current projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's current projects and areas of interest into a generating AI and have the generating AI perform data filtering.

[0096] 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 prioritize collecting high-priority data. If the user is relaxed, the data collection unit can also collect a balanced amount of data, including less important data. Furthermore, if the user is in a hurry, the data collection unit can prioritize collecting urgent data. This allows for the priority collection of important data by determining the priority of data to collect 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 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 perform emotion estimation.

[0097] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, the data collection unit can prioritize the collection of data that is close to the user's current location. Furthermore, if the user is in a specific region, the data collection unit can prioritize the collection of data related to that region. Additionally, if the user is on the move, the data collection unit can prioritize the collection of data related to their destination. This allows for the priority collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information data into a generating AI and have the generating AI perform the collection of highly relevant data.

[0098] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can automatically collect data that a user has mentioned on social media. It can also collect data related to projects that a user follows on social media. Furthermore, the data collection unit can collect data related to events that a user has shared on social media. This allows for the efficient collection of relevant data by analyzing a user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0099] The prediction unit can estimate the user's emotions and adjust the display method of the prediction based on the estimated user emotions. For example, if the user is nervous, the prediction unit can provide a simple and highly visible display method. If the user is relaxed, the prediction unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the prediction unit can provide a concise display method. By adjusting the display method of the prediction according to the user's emotions, more appropriate prediction results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0100] The prediction unit can optimize the current prediction by referring to past data during the prediction process. For example, the prediction unit can predict busy periods based on past data and reflect this in the current prediction. It can also predict the completion time of a specific task based on past data and reflect this in the current prediction. Furthermore, the prediction unit can predict the progress of a specific project by referring to past data and reflect this in the current prediction. In this way, the current prediction can be optimized by referring to past data. Some or all of the above processes in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input past data into a generating AI and have the generating AI perform the optimization of the prediction.

[0101] The prediction unit can apply different prediction algorithms to each task category during prediction. For example, the prediction unit can apply a prediction algorithm specifically for project management to project management tasks. It can also apply a prediction algorithm specifically for individual tasks to individual tasks. Furthermore, it can apply a prediction algorithm specifically for team tasks to team tasks. By applying different prediction algorithms to each task category, more accurate prediction results can be provided. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input task category data into a generating AI and have the generating AI execute the application of the prediction algorithm.

[0102] The prediction unit can estimate the user's emotions and adjust the importance of predictions based on the estimated emotions. For example, if the user is stressed, the prediction unit will prioritize displaying high-importance predictions. If the user is relaxed, the prediction unit can also display a balanced selection of predictions, including those of lower importance. Furthermore, if the user is in a hurry, the prediction unit can prioritize displaying urgent predictions. In this way, important predictions can be prioritized by adjusting the importance of predictions 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 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 prediction unit may be performed using AI, or not using AI. For example, the prediction unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0103] The prediction unit can analyze changes in predictions based on task submission times. For example, the prediction unit prioritizes analyzing predictions for tasks with approaching deadlines. It can also postpone predictions for tasks with distant deadlines. Furthermore, it can appropriately adjust the priority of predictions for tasks with medium-term deadlines. By analyzing changes in predictions based on task submission times, it can provide prediction results at the appropriate time. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input task submission time data into a generating AI and have the generating AI perform the analysis of changes in predictions.

[0104] The forecasting unit can analyze its predictions by referring to relevant market data for the task during the forecasting process. For example, the forecasting unit can predict the completion time of a specific task based on relevant market data. It can also predict the progress of a specific project by referring to relevant market data. Furthermore, the forecasting unit can predict busy periods at specific times based on relevant market data. This allows for more accurate forecast results by referring to relevant market data for the task. Some or all of the above processing in the forecasting unit may be performed using AI, for example, or without AI. For example, the forecasting unit can input relevant market data into a generating AI and have the generating AI perform the analysis of the prediction.

[0105] The rescheduling unit can estimate the user's emotions and adjust the rescheduling method based on the estimated emotions. For example, if the user is stressed, the rescheduling unit can provide a simple rescheduling method. It can also provide a more detailed rescheduling method if the user is relaxed. Furthermore, if the user is in a hurry, the rescheduling unit can provide a rapid rescheduling method. This allows for more appropriate rescheduling by adjusting the rescheduling method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the rescheduling unit may be performed using AI, or not. For example, the rescheduling unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0106] The rescheduling unit can analyze the user's past schedule history to select the optimal rescheduling method during rescheduling. For example, the rescheduling unit can automatically display tasks that the user has frequently rescheduled in the past as candidates. The rescheduling unit can also prioritize suggesting rescheduling methods (voice, text, etc.) that the user has used in the past. Furthermore, the rescheduling unit can predict and suggest rescheduling methods to be used during specific time periods based on the user's past schedule history. In this way, the optimal rescheduling method can be provided by analyzing the user's past schedule history. Some or all of the above processing in the rescheduling unit may be performed using AI, for example, or without AI. For example, the rescheduling unit can input the user's past schedule history data into a generating AI and have the generating AI select the optimal rescheduling method.

[0107] The rescheduling unit can customize the rescheduling method based on the user's current living situation during rescheduling. For example, the rescheduling unit can prioritize providing rescheduling methods related to projects the user is currently working on. The rescheduling unit can also prioritize providing relevant rescheduling methods based on the user's living situation. Furthermore, the rescheduling unit can prioritize providing relevant rescheduling methods based on projects the user has shown interest in in the past. This allows for more appropriate rescheduling by customizing the rescheduling method based on the user's current living situation. Some or all of the above processing in the rescheduling unit may be performed using AI, for example, or without AI. For example, the rescheduling unit can input the user's current living situation data into a generating AI and have the generating AI perform the customization of the rescheduling method.

[0108] The rescheduling unit can estimate the user's emotions and determine the priority of rescheduling based on the estimated emotions. For example, if the user is stressed, the rescheduling unit will prioritize high-priority rescheduling. If the user is relaxed, the rescheduling unit can also process rescheduling in a balanced manner, including less important rescheduling. Furthermore, if the user is in a hurry, the rescheduling unit can prioritize high-urgency rescheduling. In this way, by determining the priority of rescheduling according to the user's emotions, important rescheduling can be prioritized. 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 rescheduling unit may be performed using AI, or not using AI. For example, the rescheduling unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0109] The rescheduling unit can select the optimal rescheduling method by considering the user's geographical location information during rescheduling. For example, the rescheduling unit can prioritize providing rescheduling methods that are close to the user's current location. Furthermore, if the user is in a specific region, the rescheduling unit can prioritize providing rescheduling methods related to that region. Additionally, if the user is on the move, the rescheduling unit can prioritize providing rescheduling methods related to their destination. This allows the rescheduling unit to provide the optimal rescheduling method by considering the user's geographical location information. Some or all of the above processing in the rescheduling unit may be performed using AI, for example, or without AI. For example, the rescheduling unit can input the user's geographical location data into a generating AI and have the generating AI select the optimal rescheduling method.

[0110] The rescheduling unit can analyze the user's social media activity and propose rescheduling methods during the rescheduling process. For example, the rescheduling unit can automatically propose rescheduling methods mentioned by the user on social media. It can also propose rescheduling methods related to projects the user follows on social media. Furthermore, it can propose rescheduling methods related to events shared by the user on social media. This allows for the efficient proposal of relevant rescheduling methods by analyzing the user's social media activity. Some or all of the above processing in the rescheduling unit may be performed using AI, for example, or without AI. For example, the rescheduling unit can input the user's social media activity data into a generating AI and have the generating AI execute the rescheduling method proposal.

[0111] The rescheduling unit can make schedule-based suggestions by referring to the user's calendar information during rescheduling. For example, the rescheduling unit can refer to the schedules registered in the user's calendar and automatically set up rescheduling. The rescheduling unit can also suggest rescheduling methods related to specific events based on the user's calendar information. Furthermore, the rescheduling unit can suggest the optimal rescheduling method tailored to the schedule based on the user's calendar information. This makes it possible to make optimal schedule-based rescheduling suggestions by referring to the user's calendar information. Some or all of the above processing in the rescheduling unit may be performed using AI, for example, or without AI. For example, the rescheduling unit can input the user's calendar information data into a generating AI and have the generating AI execute schedule-based suggestions.

[0112] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0113] The reception desk can estimate the user's emotions and adjust the task input method based on that estimation. For example, if the user is stressed, it can provide a simple interface and minimize the input steps. If the user is relaxed, it can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, it can prioritize voice input, allowing them to quickly enter the task scope and final completion date. This allows for more appropriate task input by adjusting the task input method according to the user's emotions.

[0114] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on those emotions. For example, if the user is nervous, it can provide simple and easy-to-understand analysis results. If the user is relaxed, it can provide detailed analysis results. Furthermore, if the user is in a hurry, it can provide concise analysis results. By adjusting the presentation of the analysis according to the user's emotions, it can provide more appropriate analysis results.

[0115] The data collection unit can estimate the user's emotions and adjust the data collection method based on the estimated emotions. For example, if the user is stressed, a simple data collection method can be provided. If the user is relaxed, a detailed data collection method can be provided. Furthermore, if the user is in a hurry, a rapid data collection method can be provided. By adjusting the data collection method according to the user's emotions, more appropriate data collection becomes possible.

[0116] The prediction unit can estimate the user's emotions and adjust the display method of the prediction based on the estimated emotions. For example, if the user is nervous, it can provide a simple and highly visible display method. If the user is relaxed, it can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, it can provide a display method that gets straight to the point. By adjusting the display method of the prediction according to the user's emotions, it is possible to provide more appropriate prediction results.

[0117] The rescheduling unit can estimate the user's emotions and adjust the rescheduling method based on those emotions. For example, if the user is stressed, it can provide a simple rescheduling method. If the user is relaxed, it can provide a more detailed rescheduling method. Furthermore, if the user is in a hurry, it can provide a quick rescheduling method. By adjusting the rescheduling method according to the user's emotions, more appropriate rescheduling becomes possible.

[0118] The reception desk can analyze a user's past task input history and provide the optimal input interface. For example, it can automatically display the scope and final completion date of tasks that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest the scope and final completion date of tasks to be used during a specific time period based on the user's past input history. In this way, by analyzing the user's past task input history, the system can provide the optimal input interface.

[0119] The analysis unit can analyze the scope and final completion date of a task. For example, it can analyze the scope and final completion date of a task to determine its importance and priority. It can also predict the progress of a task based on its scope and final completion date. This allows for appropriate schedule adjustments by analyzing the scope and final completion date of a task.

[0120] The data collection unit can collect past daily report data. For example, it can collect past daily report data from users and departments and store it in a database. It can also build a system that automatically collects past daily report data. This makes it possible to understand schedule patterns by collecting past daily report data.

[0121] The forecasting unit can optimize the current forecast by referring to past data during the forecasting process. For example, it can predict busy periods based on past data and reflect this in the current forecast. It can also predict the completion time of a specific task based on past data and reflect this in the current forecast. Furthermore, it can predict the progress of a specific project by referring to past data and reflect this in the current forecast. In this way, the current forecast can be optimized by referring to past data.

[0122] The rescheduling unit can analyze the user's past schedule history to select the optimal rescheduling method during the rescheduling process. For example, it can automatically display tasks that the user has frequently rescheduled in the past as candidates. It can also prioritize suggesting rescheduling methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest rescheduling methods to be used during specific time periods based on the user's past schedule history. In this way, by analyzing the user's past schedule history, it can provide the optimal rescheduling method.

[0123] The following briefly describes the processing flow for example form 2.

[0124] Step 1: The reception desk accepts input of the scope and expected completion date for new tasks. For example, when a user enters a new task, they can enter the task scope and expected completion date. Step 2: The analysis unit analyzes the information received by the reception unit. For example, it analyzes the task scope and final completion date to determine the task's importance and priority. Step 3: The collection unit collects past daily report data. For example, it collects past daily report data for users and departments and stores it in a database. Step 4: The analysis unit analyzes the data collected by the collection unit to understand the schedule patterns. For example, it analyzes past daily report data to understand the user's schedule patterns. Step 5: The forecasting unit predicts monthly and yearly schedules based on the schedule patterns identified by the analysis unit. For example, it predicts periods when users have relatively more free time or are busier, based on past data. Step 6: The rescheduling unit performs optimal rescheduling based on the information obtained by the analysis unit and the prediction unit. For example, it minimizes overtime by scheduling new tasks during periods when there is relatively more time available.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the reception unit, analysis unit, collection unit, prediction unit, and rescheduling unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and is used when the user inputs the scope and final completion date of a new task. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the information received by the reception unit. The collection unit is implemented by the specific processing unit 290 of the data processing unit 12 and collects past daily report data. The prediction unit is implemented by the specific processing unit 290 of the data processing unit 12 and predicts the schedule based on the data collected by the collection unit. The rescheduling unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs optimal rescheduling based on the information obtained by the analysis unit and the prediction unit. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0129] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0130] 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.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] The 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.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] Figure 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.

[0137] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the 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.

[0140] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0142] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] The data processing system 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.

[0144] Each of the multiple elements described above, including the reception unit, analysis unit, collection unit, prediction unit, and rescheduling unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and is used when the user inputs the scope of a new task and the final completion date. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the information received by the reception unit. The collection unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and collects past daily report data. The prediction unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and predicts the schedule based on the data collected by the collection unit. The rescheduling unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and performs optimal rescheduling based on the information obtained by the analysis unit and the prediction unit. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0145] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0146] 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.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0148] The 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.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (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).

[0151] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] Each of the multiple elements described above, including the reception unit, analysis unit, collection unit, prediction unit, and rescheduling unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and is used when the user inputs the scope of a new task and the final completion date. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the information received by the reception unit. The collection unit is implemented by the specific processing unit 290 of the data processing unit 12 and collects past daily report data. The prediction unit is implemented by the specific processing unit 290 of the data processing unit 12 and predicts the schedule based on the data collected by the collection unit. The rescheduling unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs optimal rescheduling based on the information obtained by the analysis unit and the prediction unit. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0161] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0166] 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).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.).

[0174] 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.

[0175] 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.

[0176] 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.

[0177] Each of the multiple elements described above, including the reception unit, analysis unit, collection unit, prediction unit, and rescheduling unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and is used when the user inputs the scope of a new task and the final completion date. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the information received by the reception unit. The collection unit is implemented by the specific processing unit 290 of the data processing unit 12 and collects past daily report data. The prediction unit is implemented by the specific processing unit 290 of the data processing unit 12 and predicts the schedule based on the data collected by the collection unit. The rescheduling unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs optimal rescheduling based on the information obtained by the analysis unit and the prediction unit. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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."

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] (Note 1) A reception desk that accepts input of the scope and final completion date of new tasks, An analysis unit that analyzes the information received by the reception unit, The collection department collects past daily report data, An analysis unit analyzes the data collected by the aforementioned collection unit to understand the schedule pattern, Based on the schedule patterns identified by the analysis unit, a prediction unit predicts monthly and yearly schedules. The system includes a rescheduling unit that performs optimal rescheduling based on the information obtained by the analysis unit and the prediction unit. A system characterized by the following features. (Note 2) The rescheduling unit, It integrates with calendar apps, allowing users to visually manage their schedules in a virtual space. The system described in Appendix 1, characterized by the features described herein. (Note 3) The rescheduling unit, It features a drag-and-drop function for scheduling. The system described in Appendix 1, characterized by the features described herein. (Note 4) The prediction unit, Understand user schedule patterns based on past daily report data. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, Analyze the task scope and final completion date. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is Collect past daily report data. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and adjusts the task input method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It analyzes the user's past task input history and provides the optimal input interface. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When entering a task, the system suggests input options based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and determines the priority of the input tasks based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When entering tasks, the system prioritizes tasks that are highly relevant to the user, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When entering tasks, the system analyzes the user's social media activity and enters relevant tasks. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the task. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the task category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis, the priority of the analysis will be determined based on the timing of task submission. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the tasks. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned collection unit is We estimate the user's emotions and adjust the data collection method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned collection unit is Analyze the user's past data collection history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned collection unit is When collecting data, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 22) 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 23) The aforementioned collection unit is When collecting data, the system prioritizes collecting highly relevant data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned collection unit is When collecting data, we analyze users' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 25) The prediction unit, It estimates the user's emotions and adjusts how predictions are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The prediction unit, When making predictions, we optimize the current prediction by referring to past data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The prediction unit, When making predictions, different prediction algorithms are applied for each task category. The system described in Appendix 1, characterized by the features described herein. (Note 28) The prediction unit, It estimates the user's emotions and adjusts the importance of the prediction based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The prediction unit, When making predictions, analyze how the predictions change based on the task submission timing. The system described in Appendix 1, characterized by the features described herein. (Note 30) The prediction unit, When making predictions, we analyze the forecast by referring to relevant market data for the task. The system described in Appendix 1, characterized by the features described herein. (Note 31) The rescheduling unit, It estimates the user's emotions and adjusts the rescheduling method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The rescheduling unit, During rescheduling, the system analyzes the user's past schedule history to select the optimal rescheduling method. The system described in Appendix 1, characterized by the features described herein. (Note 33) The rescheduling unit, During rescheduling, the rescheduling method is customized based on the user's current lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 34) The rescheduling unit, It estimates the user's emotions and determines the rescheduling priority based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The rescheduling unit, During rescheduling, the system selects the optimal rescheduling method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The rescheduling unit, When rescheduling, we analyze the user's social media activity and suggest rescheduling methods. The system described in Appendix 1, characterized by the features described herein. (Note 37) The rescheduling unit, When rescheduling, the system references the user's calendar information to provide suggestions based on their schedule. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0197] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that accepts input of the scope and final completion date of new tasks, An analysis unit that analyzes the information received by the reception unit, The collection department collects past daily report data, An analysis unit analyzes the data collected by the aforementioned collection unit to understand the schedule pattern, Based on the schedule patterns identified by the analysis unit, a prediction unit predicts monthly and yearly schedules. The system includes a rescheduling unit that performs optimal rescheduling based on the information obtained by the analysis unit and the prediction unit. A system characterized by the following features.

2. The rescheduling unit, It integrates with calendar apps, allowing users to visually manage their schedules in a virtual space. The system according to feature 1.

3. The rescheduling unit, It features a drag-and-drop function for scheduling. The system according to feature 1.

4. The prediction unit, Understand user schedule patterns based on past daily report data. The system according to feature 1.

5. The aforementioned analysis unit, Analyze the task scope and final completion date. The system according to feature 1.

6. The aforementioned collection unit is Collect past daily report data. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and adjusts the task input method based on the estimated user emotions. The system according to feature 1.

8. The aforementioned reception unit is It analyzes the user's past task input history and provides the optimal input interface. The system according to feature 1.

9. The aforementioned reception unit is When entering a task, the system suggests input options based on the user's current projects and areas of interest. The system according to feature 1.

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